Next Article in Journal
Back to Locality? Demand Potential Analysis for Short Food Supply Chains
Previous Article in Journal
Sex Differences in Multimorbidity, Inappropriate Medication and Adverse Outcomes of Inpatient Care: MoPIM Cohort Study
Previous Article in Special Issue
Early Gastric Cancer: Update on Prevention, Diagnosis and Treatment
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress?

by
Silvia Rodrigues Jardim
1,
Lucila Marieta Perrotta de Souza
2 and
Heitor Siffert Pereira de Souza
2,3,*
1
Division of Worker’s Health, Universidade Federal do Rio de Janeiro, Rio de Janeiro 22290-140, RJ, Brazil
2
Departamento de Clínica Médica, Hospital Universitário, Universidade Federal do Rio de Janeiro, Rua Prof. Rodolpho Paulo Rocco 255, Ilha do Fundão, Rio de Janeiro 21941-913, RJ, Brazil
3
D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo, Rio de Janeiro 22281-100, RJ, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(4), 3640; https://doi.org/10.3390/ijerph20043640
Submission received: 21 December 2022 / Revised: 9 February 2023 / Accepted: 13 February 2023 / Published: 18 February 2023

Abstract

:
The overall burden of cancer is rapidly increasing worldwide, reflecting not only population growth and aging, but also the prevalence and spread of risk factors. Gastrointestinal (GI) cancers, including stomach, liver, esophageal, pancreatic, and colorectal cancers, represent more than a quarter of all cancers. While smoking and alcohol use are the risk factors most commonly associated with cancer development, a growing consensus also includes dietary habits as relevant risk factors for GI cancers. Current evidence suggests that socioeconomic development results in several lifestyle modifications, including shifts in dietary habits from local traditional diets to less-healthy Western diets. Moreover, recent data indicate that increased production and consumption of processed foods underlies the current pandemics of obesity and related metabolic disorders, which are directly or indirectly associated with the emergence of various chronic noncommunicable conditions and GI cancers. However, environmental changes are not restricted to dietary patterns, and unhealthy behavioral features should be analyzed with a holistic view of lifestyle. In this review, we discussed the epidemiological aspects, gut dysbiosis, and cellular and molecular characteristics of GI cancers and explored the impact of unhealthy behaviors, diet, and physical activity on developing GI cancers in the context of progressive societal changes.

1. Introduction

Gastrointestinal (GI) cancers, including stomach, liver, esophageal, pancreatic, and colorectal cancers (CRCs), represent more than a quarter of all cancers; moreover, their prevalence is continuously increasing [1,2]. In absolute numbers, pre-pandemic data estimated that in 2018, there were approximately 5 million new cases of GI cancers, with more than 3 million associated deaths [3]. Among the major malignancies of the GI tract, data obtained from the GLOBOCAN database for 2020 estimated that there were more than 600,000 new cases of esophageal cancer (EC), with more than 500,000 related deaths [4]. Using the same database, the global patterns of gastric cancer (GC) analysis estimated that there were 1.1 million new cases and more than 700,000 related deaths in 2020 [5]. Similarly, the incidence of colorectal cancer (CRC) has been rising at an alarming rate, with an estimated 1.9 million new cases and 900,000 related deaths worldwide in 2020 [6]. Although patients with CRC who are diagnosed at early stages usually have a relatively favorable prognosis [7], the increasing incidence of CRC among young adults over the last few decades [8,9] is a major concern. Even though the prognosis of GI cancers, in general, could be better among younger patients, the overall burden is greater, and the outcome can be worse than those of non-GI cancers [1]. The burden of GI cancers can be very significant in young patients given that they have a long life expectancy and constitute the major contributors to the economy and family support [10].
As recent studies indicate that most GI cancers share several common risk factors, such as smoking, alcohol ingestion, infections, dietary habits, and obesity [2,11], it has been hypothesized that their increasing incidence could be attributed to progressive changes in the presence of these factors [12,13]. Regarding CRC, it is important to highlight the decline or stabilization in the incidence of new cases in a small number of countries with a high human development index (HDI) [14,15], where CRC reduction has been attributed to relatively recent changes in lifestyle and the introduction of screening programs [2]. Therefore, in addition to advances in early detection and treatment options, there is an unmet and urgent need to invest in cancer research attempting to uncover basic mechanisms and the epidemiology behind the so-called modifiable risk factors.
Although breast, prostate, and lung cancers are highly prevalent, GI cancers rank first in terms of incidence and mortality and account for significant socioeconomic burden. Several environmental and lifestyle factors, such as smoking and alcohol consumption, have been associated with cancer development, including GI cancer [16]. Nevertheless, growing consensus also positions dietary habits as relevant risk factors for GI cancers [17,18,19]. Current evidence suggests that socioeconomic development results in several lifestyle modifications, including shifts in dietary habits to potentially less-healthy diets [14,20,21,22]. Moreover, recent data indicate that the increased production and consumption of processed foods underlie the current pandemics of obesity and related metabolic disorders, which are directly or indirectly associated with the emergence of various chronic noncommunicable conditions and GI cancers [23,24,25].
However, progressive environmental changes are not limited to those in dietary patterns. Unhealthy behavioral features are highly complex and should be analyzed with a holistic view of lifestyle. The incidence and mortality rates of GI cancers vary widely among different nations and even among different regions of each country. Apart from exposure to environmental factors, disparities in risk, prognosis, and survival of patients with GI cancers may depend much more on socioeconomic status (for example, access to prevention, vaccination, and early diagnosis) than on genetic or geographic risk factors [26]. Similarly, reducing health inequalities poses a challenge to most nations, where social determinants of health, from diet to access to screening, diagnosis, and proper treatment, differ considerably [27]. Understanding the disease risk and mortality of digestive cancers requires more than looking for genetic inheritance or individual habits, but very importantly, taking into account social, economic, cultural, and psychological circumstances that determine lifestyle and exposure to risk factors during all phases of life [28].
This review aimed to integrate available knowledge on the recent GI cancers generated in different fields through distinct methodologies, while analyzing the disease at the individual and the population levels in a holistic and complex framework. In this review, we discussed epidemiological aspects, genetic background, epigenetic modifications, gut dysbiosis, and cellular and molecular mechanisms shared by most GI cancers. Furthermore, we explored the impact of unhealthy behaviors, diet, nutrition, and physical activity on developing GI cancers in the context of the progressive globalization of socioeconomic and environmental structures.
To address the question of the increase in the incidence of GI cancers as a global phenomenon and the potential role of unhealthy lifestyles, the methodology utilized in this study relied on a literature review using the MEDLINE database within the National Library of Medicine (PubMed). Herein, we propose a holistic approach for analyzing the several aspects potentially underlying the rise of GI cancers, considering the multidirectional interactions among the major individual components. Therefore, we combined a scoping approach to identify and analyze knowledge gaps, characteristics related to crucial concepts, and the types of available evidence in the most important GI cancer-related topics, with a narrative approach.

2. Epidemiology of the Major Gastrointestinal Cancers

2.1. EC

EC is the fourth most prevalent type of GI cancer. There are two primary histological subtypes of EC: adenocarcinoma and squamous cell carcinoma (SCC). Most patients with EC present with advanced disease; therefore, the mean overall 5-year survival rate is only 18% (10–30%). EC is thrice more frequent in male patients than that in female patients. The primary risk factors for SCC are alcohol and tobacco use, while those for adenocarcinoma are gastroesophageal reflux (especially erosive esophagitis and Barrett’s esophagus), tobacco use, and obesity. SCC is the dominant subtype worldwide, especially in Asia, Africa, and Southern Europe [29], whereas adenocarcinoma ranks first (nearly 60%) in the United States (US) and Northern Europe [30].
Nevertheless, considering the population of the US, adenocarcinoma accounts for nearly 68% of ECs in non-Hispanic whites, while SCC accounts for 80% of ECs in African Americans [31]. These differences could be explained by the interaction between multiple factors, as tenuous boundaries exist between ethnicity and lifestyle. Solving this complex equation requires paying attention to socioeconomic status and access to healthcare. For instance, functional variants in alcohol-metabolizing genes have been identified in Asian populations that, when associated with lifestyle factors, significantly increase the risk of SCC [32]. However, social and racial disparities in the incidence and mortality rates of GI cancers, including EC, as in other so-called noncommunicable and infectious diseases (such as the recent COVID-19 pandemic), have long been reported [33,34,35].

2.2. GC

GC is the fifth most common cancer and the third leading cause of cancer-related mortality worldwide. Noncardia GC (NCGC) accounts for almost 75% of GC cases [36]. Similar to other GI cancers, the epidemiology of NCGC varies greatly among populations, and outcomes are poor in most parts of the world; one of the reasons is probably its diagnosis at advanced stages [37]. Although the incidence of NCGC is significantly higher in East Asia (34 per 100,000 in South Korea; 28 per 100,000 in Japan) than that in Europe or the United States (6 per 100,000), Asian countries have reported that more than 60% of NCGC cases are diagnosed at a surgically or endoscopically curable stage. Although the incidence of GC has decreased in the US over the past decades, the rates of NCGC among people aged 50 years or less run in the opposite direction, and late diagnosis and poor outcomes are frequent. In addition to Helicobacter pylori infection, other major risk factors for GC include increasing age, male sex, nonwhite race, type of dietary intake, socioeconomic status, genetics, and smoking [38].
Chronic H. pylori infection leads to atrophic gastritis, followed by intestinal metaplasia of the stomach, and is considered a meaningful precursor lesion of GC [39]. Paralleling GC rates, H. pylori prevalence rates also vary widely among countries, from less than 40% in industrialized nations of Europe and North America to greater than 70% in South America, Africa, Eastern Europe, and East Asia [40,41]. In addition to geographical residence, ethnic disparities have been reported as relevant risk factors for GC. For example, in the US, although the overall incidence of GC is nearly 6 per 100,000 among the general population, some high-risk racial and ethnic groups (Asians, Alaskan Indians, American Indians, African Americans, Hispanics) may have a high GC risk [42].

2.3. CRC

CRC is the third most commonly diagnosed malignancy and the second leading cause of cancer-related death, with approximately 1.8 million new cases worldwide [6]. More than 90 percent of CRC cases occur sporadically, highlighting the importance of risk factors in addition to well-established cancer-related genes. Moreover, the global rise in CRC rates may be associated with environmental risk factors, such as unhealthy dietary patterns, overweight, obesity, type 2 diabetes, sedentarism, smoking, and alcohol consumption [43]. As observed in other GI cancer epidemiologic studies, CRC incidence varies widely between different countries and geographic regions, with Australia and New Zealand having the highest and South-Central Asia having the lowest rates [2].
Several studies have supported a relationship between the HDI and CRC incidence and mortality; the highest incidence rates are usually reported in developed countries. Even though economic development and the resulting industrialization are expected to improve access to healthcare, these greatly influence the adoption of the so-called Western lifestyle and the unhealthy habits mentioned above. Guided health policies and early access to healthcare services promote the improvement of CRC outcomes by implementing screening methods, detection, and removal of colonic polyps, as well as detection of early-stage CRC. In this complex and multifactorial context, recent studies have indicated that 49% of CRCs occur in very high HDI countries, with Europe and North America facing the highest CRC burden [44]. Nonetheless, incidence rates are increasing in numerous less-developed countries, and great differences in CRC rates among regions of large countries, such as Canada and Brazil, might be related not only to health policies, but also to regional deprivation and risk factors [45,46,47].

2.4. Liver Cancer (LC)

Primary LC can be broadly classified into hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which are responsible for 75–85% of cases. Poor prognosis is a hallmark of the disease; therefore, the incidence and mortality patterns of LC are closely aligned, as LC ranks third both in incidence and mortality among GI cancer cases. Further, LC is the sixth–seventh most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide [48]. The global distribution of LC varies widely, with nearly 75% of cases occurring in Asia, with China accounting for over 50% of cases and Mongolia having the highest incidence (93.7 per 100,000). In the last four decades, trends of increasing LC cases have been noted in some countries, such as the US, Canada, New Zealand, and Australia [2].
Hepatitis B virus (HBV) and hepatitis C virus (HCV), along with alcohol consumption, are considered the most important risk factors for HCC worldwide. Cofactors for HCC in HBV and HCV carriers include male sex, long duration of infection, HCV genotype 3, coinfections with HIV, insulin resistance, and tobacco use. As obesity, diabetes, and the associated metabolic syndrome have become highly prevalent, nonalcoholic fatty liver disease is considered one of the most common causes of chronic liver diseases and a relevant cause of HCC [48]. Developing nations face a peculiar challenge, as the industrialization process influences the socioeconomic environment, and lifestyle changes are noticed, favoring a high-caloric Westernized diet and increasing the rates of obesity and diabetes [49]. The high male prevalence of HCC can be attributed to exposure to risk factors, such as viral hepatitis, alcohol abuse, cigarette smoking, and elevated iron storage [50].

2.5. Pancreatic Cancer (PC)

PC is the least common of the five major GI cancers and the twelfth most common of all cancers, with a cumulative 5-year survival rate of only 5–15% [51]. Moreover, predicted mortality trends of PC in the next decade are not favorable [52]. Both the incidence and mortality rates of PC are 3–4-fold higher in high HDI countries, with the highest reported rates in North America, Europe, and Australia/New Zealand, and rates in male patients being slightly higher than those in female patients [2]. Higher rates of PC in men could be related to higher exposure to environmental and social risks, such as tobacco and alcohol [53]. A few additional hypotheses could explain, at least to some extent, these findings: the adoption of a Westernized lifestyle, which includes the consumption of processed food, red meat, high sugar, and fat meals; global increase in metabolic risk factors, such as high body mass index (BMI) or increased bone mineral density, and alcohol consumption, which might also be related to the increase in the urbanization process; and equally significant, proper access to health care [54,55]. PC is usually highly aggressive and hard to diagnose due to nonspecific clinical manifestations. Moreover, diagnostic accuracy varies widely among countries and regions of the same country, depending on access to tertiary healthcare units, chiefly associated with urban, metropolitan, and highly developed centers [56]. BMI, type 2 diabetes, and, of utmost importance, alcohol and cigarette smoking are modifiable risk factors for PC. Regarding the risk of PC, an odds ratio of 1.74 (95% CI 1.61–1.87) was found for current smokers compared to nonsmokers. Heavy alcohol intake seems to be associated with PC and is a relevant cause of pancreatitis, an established risk factor for PC [57].

3. Lifestyle and Risk Factors for Gastrointestinal Cancers

3.1. Alcohol

Alcohol use is a leading risk factor for the global disease burden, and alcohol consumption can increase the risk of cancer. Between 1990 and 2017, global adult per capita alcohol consumption increased, the prevalence of current drinking increased from 45% to 47%, and lifetime abstinence decreased from 46 to 43%, and both trends are forecasted to continue by 2030 [58]. Educational status, diet, tobacco use, personal preferences, and regional and religious habits are some of the multiple lifestyle factors associated with patterns of alcohol use or abstinence and may confound the current research results. Alcoholic beverages cause nearly 4% of cancers, and the highest risk is associated with heavy alcohol consumption. Considering the different patterns of drinking, studies have shown varied associations between cancer risk and drinking frequency, quantity per usual drinking day, and heavy episodic drinking, all of which are associated with increased risk [59].
Several GI cancers have been associated with alcohol consumption. Squamous cell EC, but not esophageal adenocarcinoma, is associated with alcohol consumption. Studies have shown varying results regarding CRC and PC; the risk for PC seems to be related to heavy drinking, and the risk for CRC is associated with moderate or heavy drinking. Regarding GC, the World Cancer Research Fund/American Institute for Cancer Research—2018 report observed an increased risk in patients with an alcohol intake of >45 g/day [60]. HCC is directly linked to alcohol consumption; the association is mainly driven by alcohol-related cirrhosis, alcohol consumption in carriers of hepatitis B or C virus, and probably heavy drinking [60,61]. Alcohol might foster carcinogenesis through several pathways, including the following: both ethanol and its metabolite, acetaldehyde, can impact DNA methylation, leading to the expression of oncogenes; acetaldehyde forms DNA adducts that impair DNA synthesis and repair and cause mutations; inflammation, induction of oxidative stress, disruption of folate absorption, reduced function of the immune system, dysbiosis of the microbiome, liver cirrhosis, and changes in estrogen regulation may also play a role in cancer development [62].

3.2. Tobacco

Smoking is a major risk factor for several diseases, including GI cancers. Although tobacco smoking rates have declined in recent decades, smoking-associated diseases and deaths remain a matter of great concern and a global health problem. The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 identified tobacco as the leading factor among 87 risk factors in terms of disability-adjusted life-years in men and the seventh in women [53]. Tobacco smoke contains different chemical agents, including reactive oxygen species (ROS) and reactive nitrogen species (RNS). Oxidative damage leads to genetic and epigenetic alterations, gene dysregulation, disruption of regulatory elements, and activation of inflammatory response pathways that, in a vicious cycle, result in further generation of ROS and may ultimately evolve into cancer initiation and progression [63].
Several studies over the last few decades have supported the relationship between smoking and GI cancer. Tobacco smoking has also been associated with a 20–30% increase in the risk of esophageal SCC, and importantly, a positive synergistic effect of combined tobacco and alcohol use has been noted [64]. The data also suggest that smoking is a risk factor for both cardia and noncardia GC. As reported for other GI cancers, smokers with a higher rate of cigarette consumption are at a higher risk of GC [65,66,67]. Meta-analyses also support the role of smoking in CRC development [26,68]. Cigarette smoking is a well-established risk factor for LC and PC. The 2014 US Surgeon General’s report presented an increase in the risk of LC derived from tobacco smoking of 70% for current smokers and 40% for former smokers [69]. The risk of PC is the highest among those who smoke the greatest number of cigarettes daily. Meta-analytical studies have found an elevated odds ratio for PC in current smokers compared to that in nonsmokers, but higher for heavy smokers, which decreases proportionally with years after cessation [70,71,72].
Quitting smoking and controlling tobacco consumption demand strategic planning. Lifestyle adoption seems to transcend people’s choices. Successful smoking cessation attempts have been related to socioeconomic status, level of education, access to protobacco advertising, antitobacco campaigns, and living with other smokers. Knowledge of sociodemographic data is relevant in guiding the implementation of public health policies, as the benefits of smoking cessation are well established. Former smokers show a reduced risk of death and cancers [73,74].

3.3. Physical Inactivity, Overweight, and Obesity

A substantial component of public health targets modification of lifestyle and environmental risk factors. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 concluded that from 2010 to 2019, consistent declines (annual rate of change larger than −0.5%) were noted with respect to risks strongly associated with socioeconomic development, measured by the Socio-Demographic Index (SDI), such as household air pollution, unsafe water, sanitation, and handwashing. Tobacco smoking, a risk factor showing a trend of substantial decline, is not necessarily associated with a low SDI. On the contrary, tobacco smoking might increase as countries increase their SDI, at least temporarily. Many exposures that increase by more than 0.5% per year are metabolic risk factors, including high BMI and alcohol use [53].
Although economic development parallels better healthcare, metabolic risk factors could be related to an increase in the urbanization process, which promotes sedentary jobs and inactive lifestyles. Many individuals spend more than half of their time awake while performing sedentary tasks [54]. A systematic review emphasized that, despite variations by region, low and high socioeconomic groups in most low-income and low–middle-income countries present different health risks associated with lifestyle. Tobacco, alcohol, and red meat consumption was more prevalent in low socioeconomic groups, which also displayed less consumption of fruits, vegetables, fish, and fiber, than high socioeconomic groups. High socioeconomic groups tended to have higher levels of physical inactivity and consumed more fats, salt, and processed foods than low socioeconomic groups [75]. An increased BMI should be considered as an adjunct to physical inactivity, excess caloric intake, and diet quality. Obesity is an established risk factor for 13 different cancers, including major GI cancers. Physical inactivity, sedentary behavior, and obesity are probably related to cancer incidence via biological pathways, including insulin sensitivity, sex steroids, metabolic hormones, and chronic inflammation [53].

3.4. Infections

Malignancy develops in a multistep process, and bacteria and viruses have been identified as tumor promoters. Tumor promoters stimulate signaling pathways and cellular proliferation, which can ultimately lead to cancer. For example, infection with H. pylori accounts for more than 60% of GC, and its prevalence varies widely, from 25–50% in developed countries to 70–90% in developing countries [76]. The recurrence of H. pylori infection is very high in developing countries, either due to recrudescence or reinfection. In addition to the prevalence of H. pylori infection, living conditions, economic development, and health conditions have also been associated with infection recurrence [38,77].
Infections from HBV and HCV are currently the most important global risk factors for HCC, which is the main histological type of LC. Accordingly, patients from areas with high HCC prevalence rates tend to be younger than those with low HCC prevalence rates at diagnosis [78]. HBV is a DNA virus, and the evaluation of tissue tumors has commonly shown HBV DNA integration into the genome [48]. The resulting chronic necroinflammatory disease from HBV induces mutations in liver cells, and the lifetime risk of developing HCC is estimated to be 10–25% and is dependent on the presence of active HBV infection and/or cirrhosis. HCV is an RNA virus that does not integrate into its host genome. Tumorigenesis by HCV is probably the result of repetitive damage, regeneration, and fibrosis, and nearly 90% of HCV-associated HCCs are preceded by cirrhosis [48].

4. Common Cellular and Molecular Mechanisms

4.1. Genetic Susceptibility and Epigenetic Modifications

Although cancers present marked variability in tissue origin, histopathological subtypes, and clinical outcomes, they all result from acquiring heritable genomic modifications in the mutant cells comprising the tumors [79]. Abnormalities in critical homeostatic events, such as chromosomal instability, alterations in the methylation frequency of CpG islands in the promoter regions of cancer suppressor genes, and instability of microsatellite DNA regions disrupt the cancer-associated genes. Consequently, such changes induce alterations in the cell cycle, fundamentally disturbing several cellular functions, including proliferation, invasion, migration, and signaling [80]. Nevertheless, the process underlying temporospatial clonal evolution is also crucial for the development of cancer, which is thought to evolve following a stepwise accumulation of a series of genetic and epigenetic abnormalities in normal tissue [81]. In this context, during neoplastic development and malignant transformation, the tissue microenvironment is believed to produce a variable selective pressure that determines the favorable phenotypic attributes that ultimately allow the establishment of the tumor.
Several susceptibility genes have been shown to influence the development of malignant neoplasms of the digestive system. For example, genes categorized as having high-penetrance, such as MHL1, MSH2, MSH6, PMS2, and EPCAM (related to Lynch syndrome), have been primarily associated with CRC, but they are also with other cancers, such as gastric and pancreaticobiliary cancers [82]. The APC gene (related to adenomatous polyposis syndromes) predisposes patients to CRC in up to 100% of cases and is also associated with gastric and small-bowel cancers [83]. TP53 (Li-Fraumeni syndrome) [84], BMPR1A, and SMAD4 (juvenile polyposis syndrome) predispose to CRC and GC. STK1 (Peutz–Jeghers syndrome) predisposes patients to several tumors, including CRC, PC, GC, and small intestinal cancer [83]. However, only a relatively small percentage of tumors can be attributed to well-established cancer-related genes [85,86,87,88,89], which does not explain the worldwide increase in the incidence of GI cancers.
Epigenetics, characterized by heritable modifications in gene expression not followed by permanent changes in the DNA sequence, plays a central role in the pathogenesis of various cancers [90]. Epigenetic changes represent an important example of how the effects of exposure on genes can influence disease development. Disruption of any of the intrinsic processes involved in normal epigenetic regulation may result in abnormal activation or silencing of genes that are frequently associated with cancer [91]. For example, hypermethylation of the promoter of the DNA repair gene MHL1 determines microsatellite instability, which has been typically linked not only to CRC [92], but also to several GI cancers, including GC, PC, esophageal adenocarcinoma, and HCC [93]. The Wnt signaling pathway is a crucial cascade for tissue homeostasis and regeneration, and its dysregulation, mediated by hypermethylation [94] or microRNAs [95], has been associated with cancer development and affects the tumor microenvironment and immune response. Abnormal activation of the Wnt signaling pathway through genetic and epigenetic modifications has been associated with cancer progression and poor prognosis in several tumors, including CRC, GC, PC, EC, and LC [96].

4.2. Carcinogenic Pathways

Several signaling pathways and molecular networks have been implicated in carcinogenesis. The maintenance of tumorigenic properties of cancer cells depends on functional modifications of specific genes and signal transduction mediated by the binding of ligands to specific cell receptors. The preferential use of aerobic glycolysis, which produces lesser ATP than aerobic respiration (known as the Warburg effect), highlights metabolic reprogramming as a hallmark of cancer. Dysregulation of cellular energy metabolism constitutes an early event in carcinogenesis and positions mitochondrial function as fundamental for cancer cells [97]. The shift from oxidative to predominantly glycolytic metabolism often requires the activation of the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of the rapamycin (mTOR) pathway, one of the most ubiquitous abnormalities in cancer [98]. PI3K/AKT/mTOR activation upregulates glycolytic and lipogenic genes and stimulates enzymes to drive glycolysis, converting most of the resulting pyruvate into lactate [99]. Activation of MYC induces glutaminolysis, supplying substrates to the mitochondrial tricarboxylic acid cycle, resulting in citrate production, and ultimately acetyl-CoA for lipid biosynthesis and protein modifications [97]. Additionally, abnormal mitochondrial metabolism can determine the overproduction of ROS, with consequences on transcription factors, including HIF1-alpha and FOS-JUN, resulting in cancer cell proliferation [100,101].
The major cascades recognized for their critical roles in cancer are linked to each other and other intracellular signal transduction pathways, mediating upstream signals from receptor tyrosine kinases (RTK) [102]. For example, the epidermal growth factor receptor, a transmembrane RTK, plays an essential role in epithelial cell proliferation, differentiation, and survival, and its overexpression is associated with poor prognosis in CRC [103] and GC [104]. KRAS, a member of the Ras family of small GTPases, is involved in the activation of signal transduction pathways, such as the RAS/RAF/mitogen-activated protein kinase (MAPK) pathway, c-RAF/MEK/ERK, and PI3K/AKT, which regulate cell proliferation, survival, and differentiation [105]. KRAS is regarded as a major oncogenic promoter in various cancers, and KRAS mutations have been frequently detected in PC and CRC [106].
Chronic inflammatory diseases, including inflammatory bowel disease, Barrett’s esophagus, chronic pancreatitis, chronic gastritis, chronic hepatitis, and nonalcoholic steatohepatitis, have all been associated with an increased risk of cancer development. One of the major links between inflammation and cancer has been attributed to NF-κ B, a transcription factor associated with cell proliferation, apoptosis, and angiogenesis, which is activated by various stimuli, including inflammatory mediators, growth factors, and microorganisms [107]. NF-κB regulates the production of several inflammatory mediators, including the IL-6 family of cytokines [108], involved in the promotion of tumor development through Stat3 signaling, particularly in the early stages of colitis-associated CRC [109]. Another key protumorigenic mechanism involved in the NF-κ B pathway is the activation of antiapoptotic gene expression, which inhibits apoptosis induced by proinflammatory cytokines, such as TNF-α [110].
Other redundant and overlapping pathways also contribute to the inflammatory environment, which favors cancer development. For instance, the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway mediates proinflammatory gene expression and transcription and can also signal PI3K protein (PI3K/Akt pathway) and Ras protein (RAS-MAPK pathway) [111]. Nevertheless, even in disorders associated with low-grade inflammation, such as obesity, the inflammatory milieu maintained by proinflammatory cytokines constitutes an important mechanism underlying carcinogenesis. In a state of metabolic imbalance, particularly in obesity, inflammatory cytokines, immune mediators, tissue damage, adipocytokines derived from adipose tissue, and the resultant activation of NF-κ B and other signaling pathways combine to generate a carcinogenic environment [112].
The epidemiological association between obesity and cancer, including GI cancers, has been proposed to stem from several intricate mechanisms, such as insulin resistance, hyperinsulinemia, oxidative stress, chronic inflammation, and adipocytokine production [113]. Leptin, a hormone predominantly produced by adipose cells, has been associated with obesity [114] and has also been implicated in the development and prognosis of GI cancers, including CRC [115,116], probably because of its ability to promote the enhancement of cell proliferation and migration, inflammation, and antiapoptotic pathways [117]. Moreover, leptin synergizes with several oncogenes, cytokines, and growth factors converging into a downstream cascade, including the JAK-2/STAT, MAPK/ERK, and PI3K/AKT pathways [118,119].
The worldwide association of increasing metabolic disorders, including obesity and diabetes, with noncommunicable diseases, such as cardiovascular diseases and cancer, has recently been attributed to lifestyle changes, including the contemporary abundance and availability of food [120]. It has been hypothesized that nongenetic transmission may underlie obesity and insulin resistance [121]. It has been postulated that the effects of diet could be transmitted epigenetically to offspring, reinforcing the idea that chromatin represents a sensor and a mechanism by which metabolic changes are converted into stable patterns of reprogrammed gene expression [122]. Evidence supporting the link between diet and metabolism and disease is further corroborated by the characterization of specific metabolic sensors and their functions. For instance, mTOR modulates protein synthesis, insulin signaling, and mitochondrial function [123]. Adenosine monophosphate (AMP)-activated protein kinase (AMPK), a sensor of cellular energy status, regulates metabolic pathways that restore energy homeostasis [124]. Thus, it has been proposed that nutrient-based overabundance of metabolites and inflammatory products regulates gene expression via epigenetic modifications [125]. Nonetheless, in addition to typical markers of metabolic health, such as insulin regulation, waist circumference, and BMI, body metabolic rate (BMR), which reflects whole-body energy metabolism, has also been proposed as a relevant risk factor for cancer. Recently, in a large prospective European cohort, long-term follow-up identified a positive association between BMR and several cancers, including CRC, PC, and esophageal adenocarcinoma. The increased incidence observed, even among normal-weight individuals, appears to identify a subgroup of the population at great risk of these types of cancer, independent of adiposity [126].
Chronic psychological stress has also been implicated as a risk factor for the development of several diseases, including cancer [127,128,129]. Chronic stress stimulates the hypothalamic–pituitary–adrenal axis and sympathetic nervous system, leading to the synthesis of stress-related mediators and activation of the renin–angiotensin system [130]. Excessive production of corticosteroids and catecholamines induces the production of proinflammatory cytokines and metabolic changes, including an increase in insulin resistance and the release of free fatty acids from lipolysis [131]. Taken together, these alterations appear to create an inflammatory environment that favors the pathogenesis of metabolic syndrome, diabetes, and insulin resistance and the development of other noncommunicable chronic and immune-mediated diseases, all potentially mediated by chronic psychological stress [132,133]. In cancer, adrenergic receptors are overexpressed in neoplastic cells and the tumor microenvironment [134]. The downstream activation of adrenergic receptors, in turn, inhibits apoptosis and DNA repair with proto-oncogenic effects that enhance cell cycle progression [135]. Activation of adrenergic receptors induces the PI3K/AKT signaling pathway, with consequent stimulation of cell proliferation and angiogenesis [136]. In addition, stress-mediated alterations in the inflammatory response and immune function might compromise immune surveillance mechanisms, further favoring carcinogenesis [137].

4.3. Unhealthy Diet, Gut Dysbiosis, and Other Environmental Triggers

Currently, the gut microbiome is regarded as a physiological organ with a moldable composition that dynamically varies according to age and shifts according to various environmental exposures, including dietary patterns [138]. With the advent of technological advancements, including next-generation sequencing and metabolic profiling, a considerable amount of data has allowed a good understanding of how diet shapes the gut microbiome and may affect health and disease [139]. Interindividual variation in the gut microbiome has been attributed primarily to diet, but it is also attributed to environmental factors, such as lifestyle, exposure to pollutants, use of antibiotics, and, to some extent, host genetics [140,141]. Nevertheless, in a large population-based study, whole-microbiome composition was significantly more influenced by cohabitation than that by genetics. A healthy microbiome pattern was associated with a healthy diet, exposure to rural environments and pets, green spaces, and a high income. In contrast, reduced exposure to diverse microbiota favors an increase in the frequency of immune-mediated inflammatory and allergic diseases [142]. Further, a Western diet, rich in carbohydrates and fat, has been associated with a remarkable reduction in gut microbiome diversity [143].
The global increase in CRC, the most common GI tumor, has been largely associated with risk factors regarded as the Westernization of lifestyle. Such widespread lifestyle changes have been attributed to industrialization and economic development, generally accompanied by dietary modifications, including increased consumption of sugar, refined grains, fat, processed food, and fewer vegetables [8,43]. Therefore, in modern societies, where there is usually an accumulation of risk factors for CRC, such as high levels of sedentarism, smoking, alcohol consumption [144], type 2 diabetes [145], and obesity, an increase in the incidence of CRC is expected [146,147]. The positive association between CRC and regular ingestion of red and processed meat has been consistently reported for over a decade [24]. Heterocyclic amines and polycyclic aromatic hydrocarbons from high-temperature cooking [148,149] and nitrates and nitrites used in meat processing [150] could all favor the development of CRC. In particular, dietary heme iron intake from red and processed meat has been associated with CRC via the generation of potentially carcinogenic N-nitroso compounds [148].
Progressive advancements in food processing and agroindustrial production have led to the expansion of food products that contribute to Westernized menus [151]. These Westernized dietary products, which are usually ultra-processed and with high contents of sugar, saturated fat, and salt, but low in fiber and micronutrients [152], have been associated with cardiovascular diseases, metabolic syndrome, obesity, and overall cancer, among other health disorders [153]. Recent data from large prospective population-based cohorts identified a significant association between high consumption of ultra-processed foods (UPF) and an increased risk of CRC [154] and PC [155]. In addition, in a case-control study, the association between UPF intake and the risk of CRC increased but remained constant after adjustment for BMI, physical activity, educational level, type of job, and income [156]. In another large cohort study, the long-term follow-up of individuals consuming highly proinflammatory diets was associated with an increased risk of developing CRC, which was compensated by fiber ingestion [157]. Recent data from a large developing country showing a temporal association of increasing CRC with overweight, obesity, and diabetes indicate the progressive Westernization of the lifestyle in that country [45] in which dietary habits have been shifting from a traditional fiber-rich diet to a predominant high-calorie diet [158]. Whole grain intake has also been associated with a reduced risk of CRC [159], an effect that has been attributed to the reduction in bowel transit time and increased production of short-chain fatty acids, including butyrate, with anti-inflammatory [160] and anticancer properties [161].
An imbalance in microbial populations and certain gut microbiota components has been associated with the development of diseases, including GI cancers [162,163]. For example, infection with H. pylori has long been associated with the development of atrophic gastritis, metaplasia, and dysplasia, and their progression to cancer [164]. The carcinogenic potential of H. pylori is further corroborated by therapeutic eradication strategies that significantly reduce the risk of cancer development [165]. Recent data also support the role of gut microbial imbalance in the development of CRC. In a recent large meta-analysis, investigators found several consistent taxonomic differences in the gut microbiota with respect to CRC. For example, CRC tumor biopsies revealed a high abundance of the phylum Fusobacteria, whereas other studies found high levels of Fusobacterium and Fusobacterium nucleatum in mucosal samples of patients with CRC [166]. In addition, several studies have provided consistent data regarding the abundance of Parvimonas, Porphyromonas, and Peptostreptococcus in fecal and biopsy samples from patients with CRC [167]. The abundance of other intestinal bacteria, including Bacteroides, Akkermansia, and Ruminococcus, has also been implicated in the development or progression of CRC by inducing a proinflammatory milieu [168]. Similarly, the enrichment of Proteobacteria, including the genus Campylobacter [169] and the species E. coli [170], in the intestinal microbiome has also been associated with the development of CRC by modifying the tumor microenvironment. Moreover, in a large database case-control study, antibiotic use over previous years was associated with subsequent CRC development, reinforcing the potential carcinogenic effects of gut dysbiosis [171].
In PC, Gammaproteobacteria found in tumors have been suggested to be translocated from the gut [172]. The idea that gut bacteria might play a role in pancreatic tumor development was further corroborated by the demonstration of bacterial translocation in an experimental model [173]. Liver carcinogenesis is also associated with the gut microbiome. It has been postulated that gut microbial metabolites, such as secondary biliary acids and microbe-associated molecular patterns, enter the liver through the portal vein and contribute to carcinogenesis. Such effects are mediated by the activation of TLR4 with consequent overexpression of hepatomitogens and epiregulin [174]. Biliary acids, in turn, have also been implicated in liver carcinogenesis, as they induce prostaglandin E2 and cyclooxygenase-2 cascades, which promote tumor development [175] and inhibit NK cell recruitment, further contributing to cancer immune evasion [176].

5. Influence of the Exposome on Gastrointestinal Cancers

5.1. Multifactorial Origin and a Complex Network of Interactions

Functional interactions among diet, intestinal microbiota, and host homeostasis are complex and appear to involve epigenetic programming. In an experimental model, microbial constituents were shown to regulate histone acetylation and methylation in host tissues according to dietary patterns. For example, the consumption of a Western-type diet hampers many chromatin changes that occur in a plant-based diet. Nonetheless, supplementing germ-free mice with short-chain fatty acids could reverse chromatin modification states and transcriptional responses imposed by the gut microbiota on host epigenetic programming [177]. In addition to a long-term diet imbalance, epigenetic regulation has been associated with other factors, such as the gut microbiota and physical activity [178,179]. Modifications in gut microbial composition may affect epigenetic patterns, which orchestrate multiple molecular and cellular homeostatic processes in a potentially reversible fashion. The proposed mechanisms by which the microbiota induces immune and metabolic changes are, for example, signaling via Toll-like receptors, including NF-κ B activation or the signaling of microbiota-derived short-chain fatty acids via G-protein coupled receptors, and histone deacetylases that may be involved in the development of metabolic disorders [180] and CRC [181]. Among the commonly investigated associations between cancer and lifestyle factors, physical activity also plays an important role [182]. Although the exact mechanisms by which physical activity reduces the risk of CRC development are not completely understood, it has been recently suggested that exercise-induced changes in the gut microbiota might play an important role [183].
In the last century, multiple geosocial factors have been linked to the global rise in noncommunicable diseases, including cancer and chronic immune-mediated inflammatory diseases, in parallel with cardiovascular diseases and metabolic disorders. Among these factors, common societal transformations, usually involving socioeconomic development or industrialization, have been followed by progressive urbanization, resulting in densely populated cities. Several other factors have contributed to the complex context of socioeconomic changes, including increased industrial activities, changing working conditions, increased pollution, decreased biodiversity, changes in households and workplaces, family structure, and population composition, with increased migratory movements and the presence of refugees.
Considering the concomitant presence of factors usually shared among most chronic noncommunicable diseases [184], such as the microbiome, genetic, and epigenetic modifications, it is conceivable to think of an increase in GI cancers as part of a dynamically changing exposome; however, they are progressively very globalized and less diverse. Communities still living according to traditional lifestyles harbor a similar microbiota composition compared with those residing in highly developed and industrialized societies [185,186]. Considering that the microbiota has coevolved with humans over millions of years, it is believed that commensal microorganisms greatly contributed to shaping our biology [187]. Nevertheless, the reduced biodiversity found in the gut microbiota of contemporary industrialized societies might reflect not only the progress in medicine, the use of antibiotics, sanitation, and dietary changes, but also the progressive reduction in environmental biodiversity [188]. As the Westernized or industrialized lifestyle spreads globally, changes in the microbiota, constituting an industrial or Westernized gut microbiota, are accompanied by a proinflammatory profile that underlies the rise of most noncommunicable chronic diseases, including cancers. Therefore, an improved understanding of the multidirectional interaction between macro-ecosystems with humans and their gut microbiota and the effects of microbiota dysfunctions induced by different lifestyle aspects may greatly help prevent diseases.

5.2. Anthropocene, Social Connectivity, and Metacommunity

The idea that Western lifestyle factors may trigger gut microbiota, genetic, and epigenetic modifications has recently been regarded as a potential explanation for the rise in GI cancers and their clinical presentation and outcomes, especially in the urban context. Hence, identifying such factors is a critical step in the prevention of GI cancers. Regarding this, it is important to consider the exposome not only as a measure of individual exposures in a lifetime, including insults from environmental and occupational sources to genetics, epigenetics, immune system, and microbiota, but also as an impact to our health. This should position humans as mere victims of a hostile and almost static environment. In fact, human activities have promoted most of the changes in the exposome over time and are acknowledged as the Anthropocene era. At the apex of this epoch, particularly in the last century, dramatic and unprecedented rates of change in human activity were followed by the rapid consumption of resources and reduction in biodiversity [189], coinciding with the global increase in noncommunicable diseases and GI cancers. Many components of Earth’s system have changed radically, and the consequences of recent increases in human population and enterprises have also imposed changes on the landscape, climate, and biosphere [190].
It is also important to recognize that the role of social self-regulation patterns in an individual’s development constitutes a generation-specific process and, therefore, is ultimately society-specific [191]. Analogously, from a biological point of view, data on host-associated microbiomes, for example, indicate that host phenotypes, that is, individual phenotypes, can be transmitted between hosts along with the microbiome [192] and that the social connectivity of the individual is capable of modifying both the composition and traits of the microbiome [193], both of which are consistent with the metacommunity theory. As proposed recently, the application of ecological theory to host-microbiome communities represents an opportunity to incorporate transmission and scale-related matters, such as developmental, behavioral, and evolutionary feedback between the individual and microbiome, into a single conceptual framework, which is critically important for interpreting microbiome variation [194,195]. This could represent a means for integrating macro-ecosystems with an individual, regarded as an ecosystem.

5.3. Lifestyle, Noncommunicable Diseases, and Syndemic

To effectively understand the role of individual behaviors and social determinants in cancer development, fundamental points need to be addressed. From a socioeconomic standpoint, globalization is defined as a process of convergence and unification of the socioeconomic systems of different countries. Based on this thought, globalization could be considered not only as the development of trade activities between countries, but also as a qualitatively new stage in the process of internationalization in which the development and unification of the institutional structure of different states also influence objects of culture and everyday life, defined as lifestyle [196]. Therefore, a Westernized lifestyle, representing a hegemonic societal structure, should be interpreted not only by focusing on individual behaviors, but also by considering an integrative approach to structural determinants of health disadvantage and risk [197]. Nevertheless, structural interventions are obviously more challenging than individual behavioral approaches. However, although dietary patterns and physical activity have usually been regarded as individual lifestyle choices, in 2008, the Commission on Social Determinants of Health of the World Health Organization elaborated a report comprising daily living and working conditions in a broader context of structural determinants to promote health equity [198].
Next, it is also essential to revise the meaning of the so-called noncommunicable diseases as potentially inadequate. Previously, another group of investigators suggested binding these diseases based on their common upstream drivers. They proposed the expression, “socially transmitted conditions,” stressing the anthropogenic and socially contagious nature of diseases. Therefore, socially transmitted conditions are driven by urbanization, industrialization, poverty, and the availability of tobacco, alcohol, processed foods, etc., in addition to physical inactivity. Although the suggested new label should not exempt individuals from responsibility for their own lifestyle choices, it might underscore the available limited choices determined by the social environment [75].
Finally, regarding the spread of progressively more common lifestyles in the context of overlapping chronic noncommunicable disorders or socially transmitted conditions, in which GI cancers thrive, it is tempting to think of the globalization of these disorders in a syndemic framework. Analytical inference estimating disease risks, geographical and temporal distributions, and biological, social, and structural factors can contribute to clarifying the potential syndemic nature of the investigated diseases [199]. The recent observation of the co-occurrence of COVID-19 with pre-existing epidemics, including cancer, diabetes, and HIV, for instance, has shown that patients with compromised immune systems have had a clear increase in morbidity and mortality [200]. Considering the global rise of GI cancers as part of a syndemic context, we may guide future efforts to evaluate and incorporate these multiple levels of impact into clinical practice to improve health care, with a special focus on prevention.

6. Study Limitations and Future Perspectives

6.1. Limitations of the Current Study

Conducting a review involving the major GI cancers is a challenging task, with implicit limitations. Although several common aspects have been raised in this study, important specific differences among GI cancers do exist, and some have been highlighted here. Nevertheless, comparing different studies, with distinct methodologies, may render data difficult to interpret, especially when considering different cancers. Attempts to identify common molecular, genetic, and epigenetic alterations in GI cancers are not straightforward due to cellular and tissue specificities and differences in microenvironmental and macroenvironmental features. For example, the infectious nature underlying certain types of GI cancers, such as H. pylori infection, and viral hepatitis, apparently stem from exclusive epidemiological and pathophysiological backgrounds. In addition, genetic syndromes predisposing to CRC or PC, for instance, also run independent courses compared to the sporadic counterparts of these cancers. Such examples should call attention to the peculiarities of GI cancers, which may not allow for a common rational-specific clinical screening and management of those affected by these disorders and their at-risk relatives, cohabitants, or contacts. Therefore, running a systematic review to respond to a particular question was not appropriate in this study as we were mainly interested in identifying common aspects shared by major GI cancers and mapping knowledge gaps. In fact, this review did not aim to generate a critically appraised response to a specific question, but rather to open a broad discussion on the increasing global incidence of GI cancers and its potential association with factors regarded as noncommunicable, which might guide novel and integrative research projects for directing highly effective public health policies.

6.2. Practical Implications and Research Needs

Although the methodological approach used in this study does not allow objective guidance on GI cancers from a clinical and policy-making standpoint, the issues raised in the current study would direct future systematic reviews and clinical investigations, with innovative approaches. An improved understanding of the multidirectional interactions between macro-ecosystems and the individual, also regarded as an ecosystem, including the potential dysfunctions of the intrinsic gut microbiota induced by different lifestyle aspects, may greatly help prevent diseases. For such complex integrative tasks, it is possible that a multiomics-like approach, already showing a relevant role in cancer diagnosis, survival analysis, and response to treatment [201,202], may also be applied for investigating potentially new and unexplored associations underlying GI cancer pathogenesis. Multiomics approach has recently provided novel insights into the biological mechanisms behind gene–environment interactions [203,204]. In this line, a recent study with multiomics profiling identified several associations revealing potential biological responses and sources of exposure in early life, including signatures for diet, chemical compounds, trace elements, and weather conditions, among others [205]. In addition, recent technological breakthroughs, such as artificial intelligence and machine learning, developed over the past few years [206], may become important tools for supporting the analysis of datasets not restricted to genomics, epigenomics, proteomics, transcriptomics, metatranscriptomics, and other molecular profiling data, but also incorporating social determinants, epidemiological, and lifestyle data, among others.
At this point, we propose a reappraisal of crucial concepts, such as lifestyle, including the Westernized lifestyle, and a revised definition of noncommunicable health conditions. Analogous to applying the ecological theory to host-microbiome communities, contributing to merging distinct fields into a single conceptual framework to understand microbiome variation, we need to discuss the communicability of societal processes and their impact on the individual. Recognizing the syndemic context in which multiple factors synergize to foster GI cancer development might also impact future epidemiological and pathogenesis studies, including an improved understanding of the role of stress and burnout. Using a novel integrative approach might contribute not only to preventing GI cancers by redefining current health policies, but also to building a sustainable societal structure with relatively fewer health disparities.

7. Conclusions

Multiple geosocial factors have been associated with the global rise in chronic noncommunicable diseases, including GI cancers, in the last century. Common societal transformations, usually involving socioeconomic development, have been followed by sociocultural changes in the population progressively agglomerated in urban centers, resulting in changes in the microbiome and genetic and epigenetic modifications. As the Westernized lifestyle spreads globally, changes in the microbiota, constituting an industrial or Westernized gut microbiota, are accompanied by a proinflammatory profile that underlies the rise of most noncommunicable chronic diseases, including cancers. Therefore, it is conceivable to think of an increase in GI cancers as part of a dynamically changing exposome; however, it is progressively very globalized and less diverse.

Author Contributions

Conceptualization, H.S.P.d.S. and S.R.J.; writing—original draft preparation, L.M.P.d.S., S.R.J. and H.S.P.d.S.; writing—review and editing, H.S.P.d.S. and L.M.P.d.S.; visualization, S.R.J. and L.M.P.d.S.; supervision, H.S.P.d.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001, National Council for Scientific and Technological Development (CNPq) (306634/2019-8), and the Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ) (E26/200.802/2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the Brazilian Research Foundation, CAPES, CNPq, and FAPERJ for their financial support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
  2. Arnold, M.; Abnet, C.C.; Neale, R.E.; Vignat, J.; Giovannucci, E.L.; McGlynn, K.A.; Bray, F. Global Burden of 5 Major Types of Gastrointestinal Cancer. Gastroenterology 2020, 159, 335–349.e15. [Google Scholar] [CrossRef]
  3. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [Green Version]
  4. Morgan, E.; Soerjomataram, I.; Rumgay, H.; Coleman, H.G.; Thrift, A.P.; Vignat, J.; Laversanne, M.; Ferlay, J.; Arnold, M. The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020. Gastroenterology 2022, 163, 649–658.e2. [Google Scholar] [CrossRef]
  5. Morgan, E.; Arnold, M.; Camargo, M.C.; Gini, A.; Kunzmann, A.T.; Matsuda, T.; Meheus, F.; Verhoeven, R.H.A.; Vignat, J.; Laversanne, M.; et al. The current and future incidence and mortality of gastric cancer in 185 countries, 2020–2040: A population-based modelling study. EClinicalMedicine 2022, 47, 101404. [Google Scholar] [CrossRef]
  6. Xi, Y.; Xu, P. Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 2021, 14, 101174. [Google Scholar] [CrossRef]
  7. Cardoso, R.; Guo, F.; Heisser, T.; De Schutter, H.; Van Damme, N.; Nilbert, M.C.; Christensen, J.; Bouvier, A.M.; Bouvier, V.; Launoy, G.; et al. Overall and stage-specific survival of patients with screen-detected colorectal cancer in European countries: A population-based study in 9 countries. Lancet Reg. Health Eur. 2022, 21, 100458. [Google Scholar] [CrossRef]
  8. Keum, N.; Giovannucci, E. Global burden of colorectal cancer: Emerging trends, risk factors and prevention strategies. Nat. Reviews. Gastroenterol. Hepatol. 2019, 16, 713–732. [Google Scholar] [CrossRef] [PubMed]
  9. Vuik, F.E.; Nieuwenburg, S.A.; Bardou, M.; Lansdorp-Vogelaar, I.; Dinis-Ribeiro, M.; Bento, M.J.; Zadnik, V.; Pellise, M.; Esteban, L.; Kaminski, M.F.; et al. Increasing incidence of colorectal cancer in young adults in Europe over the last 25 years. Gut 2019, 68, 1820–1826. [Google Scholar] [CrossRef] [PubMed]
  10. Li, J. Digestive cancer incidence and mortality among young adults worldwide in 2020: A population-based study. World J. Gastrointest. Oncol. 2022, 14, 278–294. [Google Scholar] [CrossRef] [PubMed]
  11. Islami, F.; Goding Sauer, A.; Miller, K.D.; Siegel, R.L.; Fedewa, S.A.; Jacobs, E.J.; McCullough, M.L.; Patel, A.V.; Ma, J.; Soerjomataram, I.; et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J. Clin. 2018, 68, 31–54. [Google Scholar] [CrossRef] [PubMed]
  12. Song, M.; Giovannucci, E. Preventable Incidence and Mortality of Carcinoma Associated With Lifestyle Factors Among White Adults in the United States. JAMA Oncol. 2016, 2, 1154–1161. [Google Scholar] [CrossRef] [Green Version]
  13. Arnold, M.; Pandeya, N.; Byrnes, G.; Renehan, P.A.G.; Stevens, G.A.; Ezzati, P.M.; Ferlay, J.; Miranda, J.J.; Romieu, I.; Dikshit, R.; et al. Global burden of cancer attributable to high body-mass index in 2012: A population-based study. Lancet Oncol. 2015, 16, 36–46. [Google Scholar] [CrossRef] [PubMed]
  14. Fidler, M.M.; Bray, F.; Vaccarella, S.; Soerjomataram, I. Assessing global transitions in human development and colorectal cancer incidence. Int. J. Cancer 2017, 140, 2709–2715. [Google Scholar] [CrossRef] [Green Version]
  15. Arnold, M.; Sierra, M.S.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global patterns and trends in colorectal cancer incidence and mortality. Gut 2017, 66, 683–691. [Google Scholar] [CrossRef] [Green Version]
  16. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef] [PubMed]
  17. Clemente-Suarez, V.J.; Mielgo-Ayuso, J.; Martin-Rodriguez, A.; Ramos-Campo, D.J.; Redondo-Florez, L.; Tornero-Aguilera, J.F. The Burden of Carbohydrates in Health and Disease. Nutrients 2022, 14, 3809. [Google Scholar] [CrossRef]
  18. Akimoto, N.; Ugai, T.; Zhong, R.; Hamada, T.; Fujiyoshi, K.; Giannakis, M.; Wu, K.; Cao, Y.; Ng, K.; Ogino, S. Rising incidence of early-onset colorectal cancer—A call to action. Nat. Rev. Clin. Oncol. 2021, 18, 230–243. [Google Scholar] [CrossRef]
  19. Shi, L.W.; Wu, Y.L.; Hu, J.J.; Yang, P.F.; Sun, W.P.; Gao, J.; Wang, K.; Peng, Y.; Wu, J.J.; Zhong, G.C. Dietary Acid Load and the Risk of Pancreatic Cancer: A Prospective Cohort Study. Cancer Epidemiol. Biomarkers Prev. 2021, 30, 1009–1019. [Google Scholar] [CrossRef]
  20. Maomao, C.; He, L.; Dianqin, S.; Siyi, H.; Xinxin, Y.; Fan, Y.; Shaoli, Z.; Changfa, X.; Lin, L.; Ji, P.; et al. Current cancer burden in China: Epidemiology, etiology, and prevention. Cancer Biol. Med. 2022, 19, 1121–1138. [Google Scholar] [CrossRef]
  21. Vernia, F.; Longo, S.; Stefanelli, G.; Viscido, A.; Latella, G. Dietary Factors Modulating Colorectal Carcinogenesis. Nutrients 2021, 13, 143. [Google Scholar] [CrossRef]
  22. van den Brandt, P.A. The impact of a healthy lifestyle on the risk of esophageal and gastric cancer subtypes. Eur. J. Epidemiol. 2022, 37, 931–945. [Google Scholar] [CrossRef]
  23. Chung, A.; Westerman, L.; Martin, J.; Friel, S. The commercial determinants of unhealthy diets. Public Health Res. Pract. 2022, 32, e3232221. [Google Scholar] [CrossRef]
  24. Bouvard, V.; Loomis, D.; Guyton, K.Z.; Grosse, Y.; Ghissassi, F.E.; Benbrahim-Tallaa, L.; Guha, N.; Mattock, H.; Straif, K.; International Agency for Research on Cancer Monograph Working Group. Carcinogenicity of consumption of red and processed meat. Lancet Oncol. 2015, 16, 1599–1600. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Renehan, A.G.; Tyson, M.; Egger, M.; Heller, R.F.; Zwahlen, M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet 2008, 371, 569–578. [Google Scholar] [CrossRef] [PubMed]
  26. Fitzmaurice, C.; Allen, C.; Barber, R.M.; Barregard, L.; Bhutta, Z.A.; Brenner, H.; Dicker, D.J.; Chimed-Orchir, O.; Dandona, R.; Dandona, L.; et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2017, 3, 524–548. [Google Scholar] [CrossRef] [PubMed]
  27. Lima, O.; Kruger, E.; Tennant, M. Sao Paulo urban health index: Measuring and mapping health disparities. Rev. Bras. Epidemiol. 2022, 25, e220005. [Google Scholar] [CrossRef]
  28. Gupta, B.; Lalloo, R.; Johnson, N.W. Life course models for upper aero-digestive tract cancer. Int. Dent. J. 2015, 65, 111–119. [Google Scholar] [CrossRef]
  29. Lin, Y.; Totsuka, Y.; He, Y.; Kikuchi, S.; Qiao, Y.; Ueda, J.; Wei, W.; Inoue, M.; Tanaka, H. Epidemiology of esophageal cancer in Japan and China. J. Epidemiol. 2013, 23, 233–242. [Google Scholar] [CrossRef] [Green Version]
  30. Abnet, C.C.; Arnold, M.; Wei, W.Q. Epidemiology of Esophageal Squamous Cell Carcinoma. Gastroenterology 2018, 154, 360–373. [Google Scholar] [CrossRef]
  31. Ashktorab, H.; Kupfer, S.S.; Brim, H.; Carethers, J.M. Racial Disparity in Gastrointestinal Cancer Risk. Gastroenterology 2017, 153, 910–923. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Cui, R.; Kamatani, Y.; Takahashi, A.; Usami, M.; Hosono, N.; Kawaguchi, T.; Tsunoda, T.; Kamatani, N.; Kubo, M.; Nakamura, Y.; et al. Functional variants in ADH1B and ALDH2 coupled with alcohol and smoking synergistically enhance esophageal cancer risk. Gastroenterology 2009, 137, 1768–1775. [Google Scholar] [CrossRef] [PubMed]
  33. Baquet, C.R.; Commiskey, P.; Mack, K.; Meltzer, S.; Mishra, S.I. Esophageal cancer epidemiology in blacks and whites: Racial and gender disparities in incidence, mortality, survival rates and histology. J. Natl. Med. Assoc. 2005, 97, 1471–1478. [Google Scholar]
  34. Prabhu, A.; Obi, K.O.; Rubenstein, J.H. Systematic review with meta-analysis: Race-specific effects of alcohol and tobacco on the risk of oesophageal squamous cell carcinoma. Aliment. Pharmacol. Ther. 2013, 38, 1145–1155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Dong, E.; Duan, L.; Wu, B.U. Racial and Ethnic Minorities at Increased Risk for Gastric Cancer in a Regional US Population Study. Clin. Gastroenterol. Hepatol. 2017, 15, 511–517. [Google Scholar] [CrossRef] [PubMed]
  36. Etemadi, A.; Safiri, S.; Sepanlou, S.G.; Ikuta, K.; Bisignano, C.; Shakeri, R.; Amani, M.; Fitzmaurice, C.; Nixon, M.; Abbasi, N.; et al. The global, regional, and national burden of stomach cancer in 195 countries, 1990-2017: A systematic analysis for the Global Burden of Disease study 2017. Lancet Gastroenterol. Hepatol. 2020, 5, 42–54. [Google Scholar] [CrossRef] [Green Version]
  37. Huang, R.J.; Ende, A.R.; Singla, A.; Higa, J.T.; Choi, A.Y.; Lee, A.B.; Whang, S.G.; Gravelle, K.; D’Andrea, S.; Bang, S.J.; et al. Prevalence, risk factors, and surveillance patterns for gastric intestinal metaplasia among patients undergoing upper endoscopy with biopsy. Gastrointest. Endosc. 2020, 91, 70–77.e1. [Google Scholar] [CrossRef]
  38. Lyons, K.; Le, L.C.; Pham, Y.T.; Borron, C.; Park, J.Y.; Tran, C.T.D.; Tran, T.V.; Tran, H.T.; Vu, K.T.; Do, C.D.; et al. Gastric cancer: Epidemiology, biology, and prevention: A mini review. Eur. J. Cancer Prev. 2019, 28, 397–412. [Google Scholar] [CrossRef]
  39. Uemura, N.; Okamoto, S.; Yamamoto, S.; Matsumura, N.; Yamaguchi, S.; Yamakido, M.; Taniyama, K.; Sasaki, N.; Schlemper, R.J. Helicobacter pylori infection and the development of gastric cancer. N. Engl. J. Med. 2001, 345, 784–789. [Google Scholar] [CrossRef]
  40. Nomura, A.; Stemmermann, G.N.; Chyou, P.H.; Kato, I.; Perez-Perez, G.I.; Blaser, M.J. Helicobacter pylori infection and gastric carcinoma among Japanese Americans in Hawaii. N. Engl. J. Med. 1991, 325, 1132–1136. [Google Scholar] [CrossRef]
  41. Hooi, J.K.Y.; Lai, W.Y.; Ng, W.K.; Suen, M.M.Y.; Underwood, F.E.; Tanyingoh, D.; Malfertheiner, P.; Graham, D.Y.; Wong, V.W.S.; Wu, J.C.Y.; et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology 2017, 153, 420–429. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Lui, F.H.; Tuan, B.; Swenson, S.L.; Wong, R.J. Ethnic disparities in gastric cancer incidence and survival in the USA: An updated analysis of 1992–2009 SEER data. Dig. Dis. Sci. 2014, 59, 3027–3034. [Google Scholar] [CrossRef]
  43. Murphy, N.; Moreno, V.; Hughes, D.J.; Vodicka, L.; Vodicka, P.; Aglago, E.K.; Gunter, M.J.; Jenab, M. Lifestyle and dietary environmental factors in colorectal cancer susceptibility. Mol. Aspects Med. 2019, 69, 2–9. [Google Scholar] [CrossRef] [PubMed]
  44. Abualkhair, W.H.; Zhou, M.; Ahnen, D.; Yu, Q.; Wu, X.C.; Karlitz, J.J. Trends in Incidence of Early-Onset Colorectal Cancer in the United States Among Those Approaching Screening Age. JAMA Netw. Open 2020, 3, e1920407. [Google Scholar] [CrossRef] [Green Version]
  45. Sampaio, A.P.N.; de Souza, L.P.; de Lima Moreira, J.P.; Luiz, R.R.; Fogaca, H.S.; de Souza, H.S. Geographic Distribution and Time Trends of Colorectal Cancer in Brazil from 2005 to 2018. Dig. Dis. Sci. 2022, 67, 4708–4718. [Google Scholar] [CrossRef]
  46. Brenner, D.R.; Heer, E.; Sutherland, R.L.; Ruan, Y.; Tinmouth, J.; Heitman, S.J.; Hilsden, R.J. National Trends in Colorectal Cancer Incidence Among Older and Younger Adults in Canada. JAMA Netw. Open 2019, 2, e198090. [Google Scholar] [CrossRef] [Green Version]
  47. Blair, A.; Datta, G.D. Associations between area-level deprivation, rural residence, physician density, screening policy and late-stage colorectal cancer in Canada. Cancer Epidemiol. 2020, 64, 101654. [Google Scholar] [CrossRef]
  48. McGlynn, K.A.; Petrick, J.L.; El-Serag, H.B. Epidemiology of Hepatocellular Carcinoma. Hepatology 2021, 73 (Suppl. 1), 4–13. [Google Scholar] [CrossRef] [PubMed]
  49. Balbi, E.; Moreira, J.P.L.; Luiz, R.R.; Perez, R.M.; de Souza, H.S.P. Time trends and geographic distribution of hepatocellular carcinoma in Brazil: An ecological study. Medicine 2022, 101, e30614. [Google Scholar] [CrossRef]
  50. Zhang, X.; El-Serag, H.B.; Thrift, A.P. Sex and Race Disparities in the Incidence of Hepatocellular Carcinoma in the United States Examined through Age-Period-Cohort Analysis. Cancer Epidemiol. Biomark. Prev. 2020, 29, 88–94. [Google Scholar] [CrossRef]
  51. Allemani, C.; Matsuda, T.; Di Carlo, V.; Harewood, R.; Matz, M.; Niksic, M.; Bonaventure, A.; Valkov, M.; Johnson, C.J.; Esteve, J.; et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 2018, 391, 1023–1075. [Google Scholar] [CrossRef] [Green Version]
  52. Rahib, L.; Smith, B.D.; Aizenberg, R.; Rosenzweig, A.B.; Fleshman, J.M.; Matrisian, L.M. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014, 74, 2913–2921. [Google Scholar] [CrossRef] [Green Version]
  53. Murray, C.J.; Aravkin, A.Y.; Zheng, P.; Abbafati, C.; Abbas, K.M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abdelalim, A.; Abdollahi, M.; Abdollahpour, I.; et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 1223–1249. [Google Scholar] [CrossRef]
  54. Friedenreich, C.M.; Ryder-Burbidge, C.; McNeil, J. Physical activity, obesity and sedentary behavior in cancer etiology: Epidemiologic evidence and biologic mechanisms. Mol. Oncol. 2021, 15, 790–800. [Google Scholar] [CrossRef]
  55. Allen, L.; Williams, J.; Townsend, N.; Mikkelsen, B.; Roberts, N.; Foster, C.; Wickramasinghe, K. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: A systematic review. Lancet Glob. Health 2017, 5, e277–e289. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Perrotta de Souza, L.M.; Moreira, J.P.L.; Fogaca, H.S.; Luiz, R.R.; de Souza, H.S. Pancreatic Cancer Incidence and Lethality Rates in Brazil: An Ecological Study. Pancreas 2017, 46, 699–706. [Google Scholar] [CrossRef]
  57. Klein, A.P. Pancreatic cancer epidemiology: Understanding the role of lifestyle and inherited risk factors. Nat. Rev. Gastroenterol. Hepatol. 2021, 18, 493–502. [Google Scholar] [CrossRef] [PubMed]
  58. Manthey, J.; Shield, K.D.; Rylett, M.; Hasan, O.S.M.; Probst, C.; Rehm, J. Global alcohol exposure between 1990 and 2017 and forecasts until 2030: A modelling study. Lancet 2019, 393, 2493–2502. [Google Scholar] [CrossRef] [PubMed]
  59. Rock, C.L.; Thomson, C.; Gansler, T.; Gapstur, S.M.; McCullough, M.L.; Patel, A.V.; Andrews, K.S.; Bandera, E.V.; Spees, C.K.; Robien, K.; et al. American Cancer Society guideline for diet and physical activity for cancer prevention. CA Cancer J. Clin. 2020, 70, 245–271. [Google Scholar] [CrossRef]
  60. Clinton, S.K.; Giovannucci, E.L.; Hursting, S.D. The World Cancer Research Fund/American Institute for Cancer Research Third Expert Report on Diet, Nutrition, Physical Activity, and Cancer: Impact and Future Directions. J. Nutr. 2020, 150, 663–671. [Google Scholar] [CrossRef]
  61. Bagnardi, V.; Rota, M.; Botteri, E.; Tramacere, I.; Islami, F.; Fedirko, V.; Scotti, L.; Jenab, M.; Turati, F.; Pasquali, E.; et al. Alcohol consumption and site-specific cancer risk: A comprehensive dose-response meta-analysis. Br. J. Cancer 2015, 112, 580–593. [Google Scholar] [CrossRef] [Green Version]
  62. Rumgay, H.; Murphy, N.; Ferrari, P.; Soerjomataram, I. Alcohol and Cancer: Epidemiology and Biological Mechanisms. Nutrients 2021, 13, 3173. [Google Scholar] [CrossRef] [PubMed]
  63. Caliri, A.W.; Tommasi, S.; Besaratinia, A. Relationships among smoking, oxidative stress, inflammation, macromolecular damage, and cancer. Rev. Mutat. Res. 2021, 787, 108365. [Google Scholar] [CrossRef] [PubMed]
  64. Prabhu, A.; Obi, K.O.; Rubenstein, J.H. The synergistic effects of alcohol and tobacco consumption on the risk of esophageal squamous cell carcinoma: A meta-analysis. Am. J. Gastroenterol. 2014, 109, 822–827. [Google Scholar] [CrossRef] [PubMed]
  65. Nishino, Y.; Inoue, M.; Tsuji, I.; Wakai, K.; Nagata, C.; Mizoue, T.; Tanaka, K.; Tsugane, S. Tobacco smoking and gastric cancer risk: An evaluation based on a systematic review of epidemiologic evidence among the Japanese population. Jpn. J. Clin. Oncol. 2006, 36, 800–807. [Google Scholar] [CrossRef] [Green Version]
  66. Ladeiras-Lopes, R.; Pereira, A.K.; Nogueira, A.; Pinheiro-Torres, T.; Pinto, I.; Santos-Pereira, R.; Lunet, N. Smoking and gastric cancer: Systematic review and meta-analysis of cohort studies. Cancer Causes Control. 2008, 19, 689–701. [Google Scholar] [CrossRef]
  67. Sitarz, R.; Skierucha, M.; Mielko, J.; Offerhaus, G.J.A.; Maciejewski, R.; Polkowski, W.P. Gastric cancer: Epidemiology, prevention, classification, and treatment. Cancer Manag. Res. 2018, 10, 239–248. [Google Scholar] [CrossRef] [Green Version]
  68. Botteri, E.; Iodice, S.; Bagnardi, V.; Raimondi, S.; Lowenfels, A.B.; Maisonneuve, P. Smoking and colorectal cancer: A meta-analysis. JAMA 2008, 300, 2765–2778. [Google Scholar] [CrossRef]
  69. Alberg, A.J.; Shopland, D.R.; Cummings, K.M. The 2014 Surgeon General’s report: Commemorating the 50th Anniversary of the 1964 Report of the Advisory Committee to the US Surgeon General and updating the evidence on the health consequences of cigarette smoking. Am. J. Epidemiol. 2014, 179, 403–412. [Google Scholar] [CrossRef] [Green Version]
  70. Iodice, S.; Gandini, S.; Maisonneuve, P.; Lowenfels, A.B. Tobacco and the risk of pancreatic cancer: A review and meta-analysis. Langenbeck’s Arch. Surg. 2008, 393, 535–545. [Google Scholar] [CrossRef]
  71. Koyanagi, Y.N.; Ito, H.; Matsuo, K.; Sugawara, Y.; Hidaka, A.; Sawada, N.; Wada, K.; Nagata, C.; Tamakoshi, A.; Lin, Y.; et al. Smoking and Pancreatic Cancer Incidence: A Pooled Analysis of 10 Population-Based Cohort Studies in Japan. Cancer Epidemiol. Biomark. Prev. 2019, 28, 1370–1378. [Google Scholar] [CrossRef]
  72. Bosetti, C.; Lucenteforte, E.; Silverman, D.T.; Petersen, G.; Bracci, P.M.; Ji, B.T.; Negri, E.; Li, D.; Risch, H.A.; Olson, S.H.; et al. Cigarette smoking and pancreatic cancer: An analysis from the International Pancreatic Cancer Case-Control Consortium (Panc4). Ann. Oncol. 2012, 23, 1880–1888. [Google Scholar] [CrossRef]
  73. Levy, D.; de Almeida, L.M.; Szklo, A. The Brazil SimSmoke policy simulation model: The effect of strong tobacco control policies on smoking prevalence and smoking-attributable deaths in a middle income nation. PLoS Med. 2012, 9, e1001336. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Yang, J.J.; Song, M.; Yoon, H.S.; Lee, H.W.; Lee, Y.; Lee, S.A.; Choi, J.Y.; Lee, J.K.; Kang, D. What Are the Major Determinants in the Success of Smoking Cessation: Results from the Health Examinees Study. PLoS ONE 2015, 10, e0143303. [Google Scholar] [CrossRef] [PubMed]
  75. Allen, L.N.; Feigl, A.B. Reframing non-communicable diseases as socially transmitted conditions. Lancet Glob. Health 2017, 5, e644–e646. [Google Scholar] [CrossRef] [Green Version]
  76. Alipour, M. Molecular Mechanism of Helicobacter pylori-Induced Gastric Cancer. J. Gastrointest. Cancer 2021, 52, 23–30. [Google Scholar] [CrossRef]
  77. Sun, Y.; Zhang, J. Helicobacter pylori recrudescence and its influencing factors. J. Cell. Mol. Med. 2019, 23, 7919–7925. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. McGlynn, K.A.; Petrick, J.L.; London, W.T. Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clin. Liver Dis. 2015, 19, 223–238. [Google Scholar] [CrossRef] [Green Version]
  79. DeGregori, J. Evolved tumor suppression: Why are we so good at not getting cancer? Cancer Res. 2011, 71, 3739–3744. [Google Scholar] [CrossRef] [Green Version]
  80. Ahmad, R.; Singh, J.K.; Wunnava, A.; Al-Obeed, O.; Abdulla, M.; Srivastava, S.K. Emerging trends in colorectal cancer: Dysregulated signaling pathways (Review). Int. J. Mol. Med. 2021, 47, 14. [Google Scholar] [CrossRef] [PubMed]
  81. Curtius, K.; Wright, N.A.; Graham, T.A. An evolutionary perspective on field cancerization. Nat. Rev. Cancer 2018, 18, 19–32. [Google Scholar] [CrossRef] [PubMed]
  82. Giardiello, F.M.; Allen, J.I.; Axilbund, J.E.; Boland, C.R.; Burke, C.A.; Burt, R.W.; Church, J.M.; Dominitz, J.A.; Johnson, D.A.; Kaltenbach, T.; et al. Guidelines on genetic evaluation and management of Lynch syndrome: A consensus statement by the US Multi-Society Task Force on colorectal cancer. Gastroenterology 2014, 147, 502–526. [Google Scholar] [CrossRef] [Green Version]
  83. Syngal, S.; Brand, R.E.; Church, J.M.; Giardiello, F.M.; Hampel, H.L.; Burt, R.W. ACG clinical guideline: Genetic testing and management of hereditary gastrointestinal cancer syndromes. Am. J. Gastroenterol. 2015, 110, 223–262, quiz 263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Malkin, D. Li-fraumeni syndrome. Genes Cancer 2011, 2, 475–484. [Google Scholar] [CrossRef] [PubMed]
  85. Nahon, P.; Bamba-Funck, J.; Layese, R.; Trepo, E.; Zucman-Rossi, J.; Cagnot, C.; Ganne-Carrie, N.; Chaffaut, C.; Guyot, E.; Ziol, M.; et al. Integrating genetic variants into clinical models for hepatocellular carcinoma risk stratification in cirrhosis. J. Hepatol. 2022. [Google Scholar] [CrossRef]
  86. Rustgi, A.K. The genetics of hereditary colon cancer. Genes Dev. 2007, 21, 2525–2538. [Google Scholar] [CrossRef] [Green Version]
  87. Klein, A.P. Genetic susceptibility to pancreatic cancer. Mol. Carcinog. 2012, 51, 14–24. [Google Scholar] [CrossRef] [Green Version]
  88. Marwitz, T.; Huneburg, R.; Spier, I.; Lau, J.F.; Kristiansen, G.; Lingohr, P.; Kalff, J.C.; Aretz, S.; Nattermann, J.; Strassburg, C.P. Hereditary Diffuse Gastric Cancer: A Comparative Cohort Study According to Pathogenic Variant Status. Cancers 2020, 12, 3726. [Google Scholar] [CrossRef]
  89. Ku, G.Y.; Kemel, Y.; Maron, S.B.; Chou, J.F.; Ravichandran, V.; Shameer, Z.; Maio, A.; Won, E.S.; Kelsen, D.P.; Ilson, D.H.; et al. Prevalence of Germline Alterations on Targeted Tumor-Normal Sequencing of Esophagogastric Cancer. JAMA Netw. Open 2021, 4, e2114753. [Google Scholar] [CrossRef]
  90. Jung, G.; Hernandez-Illan, E.; Moreira, L.; Balaguer, F.; Goel, A. Epigenetics of colorectal cancer: Biomarker and therapeutic potential. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 111–130. [Google Scholar] [CrossRef]
  91. Jones, P.A.; Baylin, S.B. The epigenomics of cancer. Cell 2007, 128, 683–692. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Boland, C.R.; Goel, A. Microsatellite instability in colorectal cancer. Gastroenterology 2010, 138, 2073–2087. [Google Scholar] [CrossRef] [PubMed]
  93. Ratti, M.; Lampis, A.; Hahne, J.C.; Passalacqua, R.; Valeri, N. Microsatellite instability in gastric cancer: Molecular bases, clinical perspectives, and new treatment approaches. Cell. Mol. Life Sci. 2018, 75, 4151–4162. [Google Scholar] [CrossRef] [PubMed]
  94. Liu, J.B.; Qiang, F.L.; Dong, J.; Cai, J.; Zhou, S.H.; Shi, M.X.; Chen, K.P.; Hu, Z.B. Plasma DNA methylation of Wnt antagonists predicts recurrence of esophageal squamous cell carcinoma. World J. Gastroenterol. 2011, 17, 4917–4921. [Google Scholar] [CrossRef] [PubMed]
  95. Li, F.; Zhang, L.; Li, W.; Deng, J.; Zheng, J.; An, M.; Lu, J.; Zhou, Y. Circular RNA ITCH has inhibitory effect on ESCC by suppressing the Wnt/beta-catenin pathway. Oncotarget 2015, 6, 6001–6013. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Wang, Z.; Zhao, T.; Zhang, S.; Wang, J.; Chen, Y.; Zhao, H.; Yang, Y.; Shi, S.; Chen, Q.; Liu, K. The Wnt signaling pathway in tumorigenesis, pharmacological targets, and drug development for cancer therapy. Biomark Res 2021, 9, 68. [Google Scholar] [CrossRef] [PubMed]
  97. Wallace, D.C. Mitochondria and cancer. Nat. Rev. Cancer 2012, 12, 685–698. [Google Scholar] [CrossRef] [Green Version]
  98. Jones, R.G.; Thompson, C.B. Tumor suppressors and cell metabolism: A recipe for cancer growth. Genes Dev. 2009, 23, 537–548. [Google Scholar] [CrossRef] [Green Version]
  99. Wise, D.R.; Thompson, C.B. Glutamine addiction: A new therapeutic target in cancer. Trends Biochem. Sci. 2010, 35, 427–433. [Google Scholar] [CrossRef] [Green Version]
  100. Chandel, N.S.; McClintock, D.S.; Feliciano, C.E.; Wood, T.M.; Melendez, J.A.; Rodriguez, A.M.; Schumacker, P.T. Reactive oxygen species generated at mitochondrial complex III stabilize hypoxia-inducible factor-1alpha during hypoxia: A mechanism of O2 sensing. J. Biol. Chem. 2000, 275, 25130–25138. [Google Scholar] [CrossRef] [Green Version]
  101. Liu, H.; Colavitti, R.; Rovira, I.I.; Finkel, T. Redox-dependent transcriptional regulation. Circ. Res. 2005, 97, 967–974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Yip, H.Y.K.; Papa, A. Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments. Cells 2021, 10, 659. [Google Scholar] [CrossRef] [PubMed]
  103. Aparicio, J.; Esposito, F.; Serrano, S.; Falco, E.; Escudero, P.; Ruiz-Casado, A.; Manzano, H.; Fernandez-Montes, A. Metastatic Colorectal Cancer. First Line Therapy for Unresectable Disease. J. Clin. Med. 2020, 9, 3889. [Google Scholar] [CrossRef]
  104. Rocken, C. Predictive biomarkers in gastric cancer. J. Cancer Res. Clin. Oncol. 2022, 149, 467–481. [Google Scholar] [CrossRef]
  105. Malumbres, M.; Barbacid, M. RAS oncogenes: The first 30 years. Nat. Rev. Cancer 2003, 3, 459–465. [Google Scholar] [CrossRef]
  106. Simanshu, D.K.; Nissley, D.V.; McCormick, F. RAS Proteins and Their Regulators in Human Disease. Cell 2017, 170, 17–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Harvey, A.E.; Lashinger, L.M.; Hursting, S.D. The growing challenge of obesity and cancer: An inflammatory issue. Ann. N. Y. Acad. Sci. 2011, 1229, 45–52. [Google Scholar] [CrossRef]
  108. Greten, F.R.; Eckmann, L.; Greten, T.F.; Park, J.M.; Li, Z.W.; Egan, L.J.; Kagnoff, M.F.; Karin, M. IKKbeta links inflammation and tumorigenesis in a mouse model of colitis-associated cancer. Cell 2004, 118, 285–296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Grivennikov, S.; Karin, E.; Terzic, J.; Mucida, D.; Yu, G.Y.; Vallabhapurapu, S.; Scheller, J.; Rose-John, S.; Cheroutre, H.; Eckmann, L.; et al. IL-6 and Stat3 are required for survival of intestinal epithelial cells and development of colitis-associated cancer. Cancer Cell 2009, 15, 103–113. [Google Scholar] [CrossRef] [Green Version]
  110. Karin, M. Nuclear factor-kappaB in cancer development and progression. Nature 2006, 441, 431–436. [Google Scholar] [CrossRef]
  111. Farooqi, A.A.; de la Roche, M.; Djamgoz, M.B.A.; Siddik, Z.H. Overview of the oncogenic signaling pathways in colorectal cancer: Mechanistic insights. In Seminars in Cancer Biology; Academic Press: Cambridge, MA, USA, 2019; Volume 58, pp. 65–79. [Google Scholar] [CrossRef]
  112. Booth, A.; Magnuson, A.; Fouts, J.; Foster, M.T. Adipose tissue: An endocrine organ playing a role in metabolic regulation. Horm. Mol. Biol. Clin. Investig. 2016, 26, 25–42. [Google Scholar] [CrossRef]
  113. Deng, T.; Lyon, C.J.; Bergin, S.; Caligiuri, M.A.; Hsueh, W.A. Obesity, Inflammation, and Cancer. Annu. Rev. Pathol. 2016, 11, 421–449. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. de Candia, P.; Prattichizzo, F.; Garavelli, S.; Alviggi, C.; La Cava, A.; Matarese, G. The pleiotropic roles of leptin in metabolism, immunity, and cancer. J. Exp. Med. 2021, 218, e20191593. [Google Scholar] [CrossRef]
  115. Koda, M.; Sulkowska, M.; Kanczuga-Koda, L.; Surmacz, E.; Sulkowski, S. Overexpression of the obesity hormone leptin in human colorectal cancer. J. Clin. Pathol. 2007, 60, 902–906. [Google Scholar] [CrossRef] [Green Version]
  116. Howard, J.M.; Pidgeon, G.P.; Reynolds, J.V. Leptin and gastro-intestinal malignancies. Obes. Rev. 2010, 11, 863–874. [Google Scholar] [CrossRef] [PubMed]
  117. Garofalo, C.; Surmacz, E. Leptin and cancer. J. Cell. Physiol. 2006, 207, 12–22. [Google Scholar] [CrossRef] [PubMed]
  118. Zhou, Y.; Rui, L. Leptin signaling and leptin resistance. Front. Med. 2013, 7, 207–222. [Google Scholar] [CrossRef] [PubMed]
  119. Sanchez-Jimenez, F.; Perez-Perez, A.; de la Cruz-Merino, L.; Sanchez-Margalet, V. Obesity and Breast Cancer: Role of Leptin. Front. Oncol. 2019, 9, 596. [Google Scholar] [CrossRef]
  120. Camps, J.; Garcia-Heredia, A.; Hernandez-Aguilera, A.; Joven, J. Paraoxonases, mitochondrial dysfunction and non-communicable diseases. Chem. Biol. Interact. 2016, 259, 382–387. [Google Scholar] [CrossRef]
  121. Huypens, P.; Sass, S.; Wu, M.; Dyckhoff, D.; Tschop, M.; Theis, F.; Marschall, S.; Hrabe de Angelis, M.; Beckers, J. Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nat. Genet. 2016, 48, 497–499. [Google Scholar] [CrossRef] [PubMed]
  122. Katada, S.; Imhof, A.; Sassone-Corsi, P. Connecting threads: Epigenetics and metabolism. Cell 2012, 148, 24–28. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. Albert, V.; Hall, M.N. mTOR signaling in cellular and organismal energetics. Curr. Opin. Cell Biol. 2015, 33, 55–66. [Google Scholar] [CrossRef] [PubMed]
  124. Hardie, D.G.; Schaffer, B.E.; Brunet, A. AMPK: An Energy-Sensing Pathway with Multiple Inputs and Outputs. Trends Cell Biol. 2016, 26, 190–201. [Google Scholar] [CrossRef] [Green Version]
  125. Hernandez-Aguilera, A.; Fernandez-Arroyo, S.; Cuyas, E.; Luciano-Mateo, F.; Cabre, N.; Camps, J.; Lopez-Miranda, J.; Menendez, J.A.; Joven, J. Epigenetics and nutrition-related epidemics of metabolic diseases: Current perspectives and challenges. Food Chem. Toxicol. 2016, 96, 191–204. [Google Scholar] [CrossRef] [PubMed]
  126. Kliemann, N.; Murphy, N.; Viallon, V.; Freisling, H.; Tsilidis, K.K.; Rinaldi, S.; Mancini, F.R.; Fagherazzi, G.; Boutron-Ruault, M.C.; Boeing, H.; et al. Predicted basal metabolic rate and cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int. J. Cancer 2020, 147, 648–661. [Google Scholar] [CrossRef] [PubMed]
  127. Andersen, B.L.; Farrar, W.B.; Golden-Kreutz, D.; Kutz, L.A.; MacCallum, R.; Courtney, M.E.; Glaser, R. Stress and immune responses after surgical treatment for regional breast cancer. J. Natl. Cancer Inst. 1998, 90, 30–36. [Google Scholar] [CrossRef] [Green Version]
  128. Zhi, X.; Li, B.; Li, Z.; Zhang, J.; Yu, J.; Zhang, L.; Xu, Z. Adrenergic modulation of AMPK-dependent autophagy by chronic stress enhances cell proliferation and survival in gastric cancer. Int. J. Oncol. 2019, 54, 1625–1638. [Google Scholar] [CrossRef] [Green Version]
  129. Zhang, X.; Zhang, Y.; He, Z.; Yin, K.; Li, B.; Zhang, L.; Xu, Z. Chronic stress promotes gastric cancer progression and metastasis: An essential role for ADRB2. Cell Death Dis. 2019, 10, 788. [Google Scholar] [CrossRef] [Green Version]
  130. Dai, S.; Mo, Y.; Wang, Y.; Xiang, B.; Liao, Q.; Zhou, M.; Li, X.; Li, Y.; Xiong, W.; Li, G.; et al. Chronic Stress Promotes Cancer Development. Front. Oncol. 2020, 10, 1492. [Google Scholar] [CrossRef]
  131. Afrisham, R.; Paknejad, M.; Soliemanifar, O.; Sadegh-Nejadi, S.; Meshkani, R.; Ashtary-Larky, D. The Influence of Psychological Stress on the Initiation and Progression of Diabetes and Cancer. Int. J. Endocrinol. Metab. 2019, 17, e67400. [Google Scholar] [CrossRef] [Green Version]
  132. Black, P.H. The inflammatory consequences of psychologic stress: Relationship to insulin resistance, obesity, atherosclerosis and diabetes mellitus, type II. Med. Hypotheses 2006, 67, 879–891. [Google Scholar] [CrossRef]
  133. Jamerson, T.A.; Li, Q.; Sreeskandarajan, S.; Budunova, I.V.; He, Z.; Kang, J.; Gudjonsson, J.E.; Patrick, M.T.; Tsoi, L.C. Roles Played by Stress-Induced Pathways in Driving Ethnic Heterogeneity for Inflammatory Skin Diseases. Front. Immunol. 2022, 13, 845655. [Google Scholar] [CrossRef] [PubMed]
  134. Eng, J.W.; Kokolus, K.M.; Reed, C.B.; Hylander, B.L.; Ma, W.W.; Repasky, E.A. A nervous tumor microenvironment: The impact of adrenergic stress on cancer cells, immunosuppression, and immunotherapeutic response. Cancer Immunol. Immunother. 2014, 63, 1115–1128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Shin, K.J.; Lee, Y.J.; Yang, Y.R.; Park, S.; Suh, P.G.; Follo, M.Y.; Cocco, L.; Ryu, S.H. Molecular Mechanisms Underlying Psychological Stress and Cancer. Curr. Pharm. Des. 2016, 22, 2389–2402. [Google Scholar] [CrossRef] [PubMed]
  136. Bruzzone, A.; Pinero, C.P.; Castillo, L.F.; Sarappa, M.G.; Rojas, P.; Lanari, C.; Luthy, I.A. Alpha2-adrenoceptor action on cell proliferation and mammary tumour growth in mice. Br. J. Pharmacol. 2008, 155, 494–504. [Google Scholar] [CrossRef] [Green Version]
  137. Yang, H.; Xia, L.; Chen, J.; Zhang, S.; Martin, V.; Li, Q.; Lin, S.; Chen, J.; Calmette, J.; Lu, M.; et al. Stress-glucocorticoid-TSC22D3 axis compromises therapy-induced antitumor immunity. Nat. Med. 2019, 25, 1428–1441. [Google Scholar] [CrossRef]
  138. Clemente, J.C.; Ursell, L.K.; Parfrey, L.W.; Knight, R. The impact of the gut microbiota on human health: An integrative view. Cell 2012, 148, 1258–1270. [Google Scholar] [CrossRef] [Green Version]
  139. Duffy, L.C.; Raiten, D.J.; Hubbard, V.S.; Starke-Reed, P. Progress and challenges in developing metabolic footprints from diet in human gut microbial cometabolism. J. Nutr. 2015, 145, 1123S–1130S. [Google Scholar] [CrossRef] [Green Version]
  140. Zhernakova, A.; Kurilshikov, A.; Bonder, M.J.; Tigchelaar, E.F.; Schirmer, M.; Vatanen, T.; Mujagic, Z.; Vila, A.V.; Falony, G.; Vieira-Silva, S.; et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016, 352, 565–569. [Google Scholar] [CrossRef] [Green Version]
  141. Vatanen, T.; Plichta, D.R.; Somani, J.; Munch, P.C.; Arthur, T.D.; Hall, A.B.; Rudolf, S.; Oakeley, E.J.; Ke, X.; Young, R.A.; et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat. Microbiol. 2019, 4, 470–479. [Google Scholar] [CrossRef] [Green Version]
  142. Bach, J.F. The hygiene hypothesis in autoimmunity: The role of pathogens and commensals. Nat. Rev. Immunol. 2018, 18, 105–120. [Google Scholar] [CrossRef]
  143. Lewis, J.D.; Abreu, M.T. Diet as a Trigger or Therapy for Inflammatory Bowel Diseases. Gastroenterology 2017, 152, 398–414.e6. [Google Scholar] [CrossRef]
  144. Hughes, L.A.E.; Simons, C.; van den Brandt, P.A.; van Engeland, M.; Weijenberg, M.P. Lifestyle, Diet, and Colorectal Cancer Risk According to (Epi)genetic Instability: Current Evidence and Future Directions of Molecular Pathological Epidemiology. Curr. Color. Cancer Rep. 2017, 13, 455–469. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. He, J.; Stram, D.O.; Kolonel, L.N.; Henderson, B.E.; Le Marchand, L.; Haiman, C.A. The association of diabetes with colorectal cancer risk: The Multiethnic Cohort. Br. J. Cancer 2010, 103, 120–126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Karahalios, A.; English, D.R.; Simpson, J.A. Weight change and risk of colorectal cancer: A systematic review and meta-analysis. Am. J. Epidemiol. 2015, 181, 832–845. [Google Scholar] [CrossRef]
  147. Silva, A.; Faria, G.; Araujo, A.; Monteiro, M.P. Impact of adiposity on staging and prognosis of colorectal cancer. Crit. Rev. Oncol. Hematol. 2020, 145, 102857. [Google Scholar] [CrossRef] [PubMed]
  148. Cross, A.J.; Ferrucci, L.M.; Risch, A.; Graubard, B.I.; Ward, M.H.; Park, Y.; Hollenbeck, A.R.; Schatzkin, A.; Sinha, R. A large prospective study of meat consumption and colorectal cancer risk: An investigation of potential mechanisms underlying this association. Cancer Res. 2010, 70, 2406–2414. [Google Scholar] [CrossRef] [Green Version]
  149. Sugimura, T.; Wakabayashi, K.; Nakagama, H.; Nagao, M. Heterocyclic amines: Mutagens/carcinogens produced during cooking of meat and fish. Cancer Sci. 2004, 95, 290–299. [Google Scholar] [CrossRef]
  150. Joosen, A.M.; Kuhnle, G.G.; Aspinall, S.M.; Barrow, T.M.; Lecommandeur, E.; Azqueta, A.; Collins, A.R.; Bingham, S.A. Effect of processed and red meat on endogenous nitrosation and DNA damage. Carcinogenesis 2009, 30, 1402–1407. [Google Scholar] [CrossRef]
  151. Crimarco, A.; Landry, M.J.; Gardner, C.D. Ultra-processed Foods, Weight Gain, and Co-morbidity Risk. Curr. Obes. Rep. 2022, 11, 80–92. [Google Scholar] [CrossRef]
  152. Monteiro, C.A.; Cannon, G.; Levy, R.B.; Moubarac, J.C.; Louzada, M.L.; Rauber, F.; Khandpur, N.; Cediel, G.; Neri, D.; Martinez-Steele, E.; et al. Ultra-processed foods: What they are and how to identify them. Public Health Nutr. 2019, 22, 936–941. [Google Scholar] [CrossRef]
  153. Chen, X.; Zhang, Z.; Yang, H.; Qiu, P.; Wang, H.; Wang, F.; Zhao, Q.; Fang, J.; Nie, J. Consumption of ultra-processed foods and health outcomes: A systematic review of epidemiological studies. Nutr. J. 2020, 19, 86. [Google Scholar] [CrossRef]
  154. Hang, D.; Wang, L.; Fang, Z.; Du, M.; Wang, K.; He, X.; Khandpur, N.; Rossato, S.L.; Wu, K.; Hu, Z.; et al. Ultra-processed food consumption and risk of colorectal cancer precursors: Results from three prospective cohorts. J. Natl. Cancer Inst. 2022, 115, 155–164. [Google Scholar] [CrossRef]
  155. Zhong, G.C.; Zhu, Q.; Cai, D.; Hu, J.J.; Dai, X.; Gong, J.P.; Sun, W.P. Ultra-processed food consumption and the risk of pancreatic cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Int. J. Cancer 2022, 152, 835–844. [Google Scholar] [CrossRef] [PubMed]
  156. Jafari, F.; Yarmand, S.; Nouri, M.; Nejad, E.T.; Ramezani, A.; Sohrabi, Z.; Rashidkhani, B. Ultra-Processed Food Intake and Risk of Colorectal Cancer: A Matched Case-Control Study. Nutr. Cancer 2022, 1–10. [Google Scholar] [CrossRef] [PubMed]
  157. Tabung, F.K.; Liu, L.; Wang, W.; Fung, T.T.; Wu, K.; Smith-Warner, S.A.; Cao, Y.; Hu, F.B.; Ogino, S.; Fuchs, C.S.; et al. Association of Dietary Inflammatory Potential With Colorectal Cancer Risk in Men and Women. JAMA Oncol. 2018, 4, 366–373. [Google Scholar] [CrossRef] [Green Version]
  158. Granado, F.S.; Maia, E.G.; Mendes, L.L.; Claro, R.M. Reduction of traditional food consumption in Brazilian diet: Trends and forecasting of bean consumption (2007–2030). Public Health Nutr. 2021, 24, 1185–1192. [Google Scholar] [CrossRef] [PubMed]
  159. Gaesser, G.A. Whole Grains, Refined Grains, and Cancer Risk: A Systematic Review of Meta-Analyses of Observational Studies. Nutrients 2020, 12, 3756. [Google Scholar] [CrossRef]
  160. Liu, H.; Wang, J.; He, T.; Becker, S.; Zhang, G.; Li, D.; Ma, X. Butyrate: A Double-Edged Sword for Health? Adv. Nutr. 2018, 9, 21–29. [Google Scholar] [CrossRef] [Green Version]
  161. Fung, K.Y.; Cosgrove, L.; Lockett, T.; Head, R.; Topping, D.L. A review of the potential mechanisms for the lowering of colorectal oncogenesis by butyrate. Br. J. Nutr. 2012, 108, 820–831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  162. Dzutsev, A.; Goldszmid, R.S.; Viaud, S.; Zitvogel, L.; Trinchieri, G. The role of the microbiota in inflammation, carcinogenesis, and cancer therapy. Eur. J. Immunol. 2015, 45, 17–31. [Google Scholar] [CrossRef]
  163. Vogtmann, E.; Goedert, J.J. Epidemiologic studies of the human microbiome and cancer. Br. J. Cancer 2016, 114, 237–242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  164. Moss, S.F. The Clinical Evidence Linking Helicobacter pylori to Gastric Cancer. Cell. Mol. Gastroenterol. Hepatol. 2017, 3, 183–191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  165. Lee, Y.C.; Chiang, T.H.; Chou, C.K.; Tu, Y.K.; Liao, W.C.; Wu, M.S.; Graham, D.Y. Association Between Helicobacter pylori Eradication and Gastric Cancer Incidence: A Systematic Review and Meta-analysis. Gastroenterology 2016, 150, 1113–1124.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Matson, V.; Chervin, C.S.; Gajewski, T.F. Cancer and the Microbiome-Influence of the Commensal Microbiota on Cancer, Immune Responses, and Immunotherapy. Gastroenterology 2021, 160, 600–613. [Google Scholar] [CrossRef]
  167. Huybrechts, I.; Zouiouich, S.; Loobuyck, A.; Vandenbulcke, Z.; Vogtmann, E.; Pisanu, S.; Iguacel, I.; Scalbert, A.; Indave, I.; Smelov, V.; et al. The Human Microbiome in Relation to Cancer Risk: A Systematic Review of Epidemiologic Studies. Cancer Epidemiol. Biomark. Prev. 2020, 29, 1856–1868. [Google Scholar] [CrossRef]
  168. Colombo, F.; Illescas, O.; Noci, S.; Minnai, F.; Pintarelli, G.; Pettinicchio, A.; Vannelli, A.; Sorrentino, L.; Battaglia, L.; Cosimelli, M.; et al. Gut microbiota composition in colorectal cancer patients is genetically regulated. Sci. Rep. 2022, 12, 11424. [Google Scholar] [CrossRef]
  169. Kaakoush, N.O.; Deshpande, N.P.; Man, S.M.; Burgos-Portugal, J.A.; Khattak, F.A.; Raftery, M.J.; Wilkins, M.R.; Mitchell, H.M. Transcriptomic and proteomic analyses reveal key innate immune signatures in the host response to the gastrointestinal pathogen Campylobacter concisus. Infect. Immun. 2015, 83, 832–845. [Google Scholar] [CrossRef] [Green Version]
  170. Dalmasso, G.; Cougnoux, A.; Delmas, J.; Darfeuille-Michaud, A.; Bonnet, R. The bacterial genotoxin colibactin promotes colon tumor growth by modifying the tumor microenvironment. Gut Microbes 2014, 5, 675–680. [Google Scholar] [CrossRef] [Green Version]
  171. Armstrong, D.; Dregan, A.; Ashworth, M.; White, P.; McGee, C.; de Lusignan, S. The association between colorectal cancer and prior antibiotic prescriptions: Case control study. Br. J. Cancer 2020, 122, 912–917. [Google Scholar] [CrossRef]
  172. Geller, L.T.; Barzily-Rokni, M.; Danino, T.; Jonas, O.H.; Shental, N.; Nejman, D.; Gavert, N.; Zwang, Y.; Cooper, Z.A.; Shee, K.; et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 2017, 357, 1156–1160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  173. Pushalkar, S.; Hundeyin, M.; Daley, D.; Zambirinis, C.P.; Kurz, E.; Mishra, A.; Mohan, N.; Aykut, B.; Usyk, M.; Torres, L.E.; et al. The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov. 2018, 8, 403–416. [Google Scholar] [CrossRef] [Green Version]
  174. Dapito, D.H.; Mencin, A.; Gwak, G.Y.; Pradere, J.P.; Jang, M.K.; Mederacke, I.; Caviglia, J.M.; Khiabanian, H.; Adeyemi, A.; Bataller, R.; et al. Promotion of hepatocellular carcinoma by the intestinal microbiota and TLR4. Cancer Cell 2012, 21, 504–516. [Google Scholar] [CrossRef] [Green Version]
  175. Luo, W.; Guo, S.; Zhou, Y.; Zhao, J.; Wang, M.; Sang, L.; Chang, B.; Wang, B. Hepatocellular Carcinoma: How the Gut Microbiota Contributes to Pathogenesis, Diagnosis, and Therapy. Front. Microbiol. 2022, 13, 873160. [Google Scholar] [CrossRef]
  176. Ma, C.; Han, M.; Heinrich, B.; Fu, Q.; Zhang, Q.; Sandhu, M.; Agdashian, D.; Terabe, M.; Berzofsky, J.A.; Fako, V.; et al. Gut microbiome-mediated bile acid metabolism regulates liver cancer via NKT cells. Science 2018, 360, eaan5931. [Google Scholar] [CrossRef] [Green Version]
  177. Krautkramer, K.A.; Kreznar, J.H.; Romano, K.A.; Vivas, E.I.; Barrett-Wilt, G.A.; Rabaglia, M.E.; Keller, M.P.; Attie, A.D.; Rey, F.E.; Denu, J.M. Diet-Microbiota Interactions Mediate Global Epigenetic Programming in Multiple Host Tissues. Mol. Cell 2016, 64, 982–992. [Google Scholar] [CrossRef] [Green Version]
  178. Jequier, E. Pathways to obesity. Int. J. Obes. 2002, 26 (Suppl. 2), S12–S17. [Google Scholar] [CrossRef] [Green Version]
  179. Turnbaugh, P.J.; Hamady, M.; Yatsunenko, T.; Cantarel, B.L.; Duncan, A.; Ley, R.E.; Sogin, M.L.; Jones, W.J.; Roe, B.A.; Affourtit, J.P.; et al. A core gut microbiome in obese and lean twins. Nature 2009, 457, 480–484. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  180. Remely, M.; Haslberger, A.G. The microbial epigenome in metabolic syndrome. Mol. Aspects Med. 2017, 54, 71–77. [Google Scholar] [CrossRef]
  181. Thangaraju, M.; Cresci, G.A.; Liu, K.; Ananth, S.; Gnanaprakasam, J.P.; Browning, D.D.; Mellinger, J.D.; Smith, S.B.; Digby, G.J.; Lambert, N.A.; et al. GPR109A is a G-protein-coupled receptor for the bacterial fermentation product butyrate and functions as a tumor suppressor in colon. Cancer Res. 2009, 69, 2826–2832. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  182. Chen, X.; Ding, J.; Li, H.; Carr, P.R.; Hoffmeister, M.; Brenner, H. The power of a healthy lifestyle for cancer prevention: The example of colorectal cancer. Cancer Biol. Med. 2022, 19, 1586–1597. [Google Scholar] [CrossRef]
  183. Boytar, A.N.; Nitert, M.D.; Morrision, M.; Skinner, T.L.; Jenkins, D.G. Exercise-induced changes to the human gut microbiota and implications for colorectal cancer: A narrative review. J. Physiol. 2022, 600, 5189–5201. [Google Scholar] [CrossRef] [PubMed]
  184. Hand, T.W.; Vujkovic-Cvijin, I.; Ridaura, V.K.; Belkaid, Y. Linking the Microbiota, Chronic Disease, and the Immune System. Trends Endocrinol. Metab. 2016, 27, 831–843. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  185. De Filippo, C.; Cavalieri, D.; Di Paola, M.; Ramazzotti, M.; Poullet, J.B.; Massart, S.; Collini, S.; Pieraccini, G.; Lionetti, P. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl. Acad. Sci. USA 2010, 107, 14691–14696. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Yatsunenko, T.; Rey, F.E.; Manary, M.J.; Trehan, I.; Dominguez-Bello, M.G.; Contreras, M.; Magris, M.; Hidalgo, G.; Baldassano, R.N.; Anokhin, A.P.; et al. Human gut microbiome viewed across age and geography. Nature 2012, 486, 222–227. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  187. Costello, E.K.; Stagaman, K.; Dethlefsen, L.; Bohannan, B.J.; Relman, D.A. The application of ecological theory toward an understanding of the human microbiome. Science 2012, 336, 1255–1262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  188. Sonnenburg, J.L.; Sonnenburg, E.D. Vulnerability of the industrialized microbiota. Science 2019, 366, eaaw9255. [Google Scholar] [CrossRef] [Green Version]
  189. Logan, A.C.; Prescott, S.L.; Haahtela, T.; Katz, D.L. The importance of the exposome and allostatic load in the planetary health paradigm. J. Physiol. Anthropol. 2018, 37, 15. [Google Scholar] [CrossRef]
  190. Waters, C.N.; Turner, S.D. Defining the onset of the Anthropocene. Science 2022, 378, 706–708. [Google Scholar] [CrossRef] [PubMed]
  191. Elias, N.; Schroöter, M. The Society of Individuals; Continuum: New York, NY, USA, 2001; p. 247. [Google Scholar]
  192. Griffin, N.W.; Ahern, P.P.; Cheng, J.; Heath, A.C.; Ilkayeva, O.; Newgard, C.B.; Fontana, L.; Gordon, J.I. Prior Dietary Practices and Connections to a Human Gut Microbial Metacommunity Alter Responses to Diet Interventions. Cell Host Microbe 2017, 21, 84–96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  193. Gilbert, J.A. Social behavior and the microbiome. Elife 2015, 4, e07322. [Google Scholar] [CrossRef] [PubMed]
  194. Meyer, K.M.; Deines, P.; Wei, Z.; Busby, P.E.; Lindow, S.E.; Bohannan, B.J.M. Editorial: The role of dispersal and transmission in structuring microbial communities. Front. Microbiol. 2022, 13, 1054498. [Google Scholar] [CrossRef]
  195. Chase, J.M.; McGill, B.J.; McGlinn, D.J.; May, F.; Blowes, S.A.; Xiao, X.; Knight, T.M.; Purschke, O.; Gotelli, N.J. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 2018, 21, 1737–1751. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  196. Narula, R.; Asmussen, C.G.; Chi, T.; Kundu, S.K. Applying and advancing internalization theory: The multinational enterprise in the twenty-first century. J. Int. Bus. Stud. 2019, 50, 21. [Google Scholar] [CrossRef] [Green Version]
  197. Kandt, J. Social practice, plural lifestyles and health inequalities in the United Kingdom. Sociol. Health Illn. 2018, 40, 1294–1311. [Google Scholar] [CrossRef] [Green Version]
  198. Marmot, M.; Friel, S.; Bell, R.; Houweling, T.A.; Taylor, S. Closing the gap in a generation: Health equity through action on the social determinants of health. Lancet 2008, 372, 1661–1669. [Google Scholar] [CrossRef]
  199. Shrestha, S.; Bauer, C.X.C.; Hendricks, B.; Stopka, T.J. Spatial epidemiology: An empirical framework for syndemics research. Soc. Sci. Med. 2022, 295, 113352. [Google Scholar] [CrossRef]
  200. Mendenhall, E.; Kohrt, B.A.; Logie, C.H.; Tsai, A.C. Syndemics and clinical science. Nat. Med. 2022, 28, 1359–1362. [Google Scholar] [CrossRef]
  201. Reel, P.S.; Reel, S.; Pearson, E.; Trucco, E.; Jefferson, E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol. Adv. 2021, 49, 107739. [Google Scholar] [CrossRef]
  202. Leng, D.; Zheng, L.; Wen, Y.; Zhang, Y.; Wu, L.; Wang, J.; Wang, M.; Zhang, Z.; He, S.; Bo, X. A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol. 2022, 23, 171. [Google Scholar] [CrossRef]
  203. Majarian, T.D.; Bentley, A.R.; Laville, V.; Brown, M.R.; Chasman, D.I.; de Vries, P.S.; Feitosa, M.F.; Franceschini, N.; Gauderman, W.J.; Marchek, C.; et al. Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption. Front. Genet. 2022, 13, 954713. [Google Scholar] [CrossRef] [PubMed]
  204. Elgart, M.; Goodman, M.O.; Isasi, C.; Chen, H.; Morrison, A.C.; de Vries, P.S.; Xu, H.; Manichaikul, A.W.; Guo, X.; Franceschini, N.; et al. Correlations between complex human phenotypes vary by genetic background, gender, and environment. Cell Rep. Med. 2022, 3, 100844. [Google Scholar] [CrossRef] [PubMed]
  205. Maitre, L.; Bustamante, M.; Hernandez-Ferrer, C.; Thiel, D.; Lau, C.E.; Siskos, A.P.; Vives-Usano, M.; Ruiz-Arenas, C.; Pelegri-Siso, D.; Robinson, O.; et al. Multi-omics signatures of the human early life exposome. Nat. Commun. 2022, 13, 7024. [Google Scholar] [CrossRef] [PubMed]
  206. Wang, S.; Wang, S.; Wang, Z. A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer. Front. Med. 2022, 9, 1109365. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jardim, S.R.; de Souza, L.M.P.; de Souza, H.S.P. The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress? Int. J. Environ. Res. Public Health 2023, 20, 3640. https://doi.org/10.3390/ijerph20043640

AMA Style

Jardim SR, de Souza LMP, de Souza HSP. The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress? International Journal of Environmental Research and Public Health. 2023; 20(4):3640. https://doi.org/10.3390/ijerph20043640

Chicago/Turabian Style

Jardim, Silvia Rodrigues, Lucila Marieta Perrotta de Souza, and Heitor Siffert Pereira de Souza. 2023. "The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress?" International Journal of Environmental Research and Public Health 20, no. 4: 3640. https://doi.org/10.3390/ijerph20043640

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop