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Article

Protective Effects of Influenza Vaccine against Colorectal Cancer in Populations with Chronic Kidney Disease: A Nationwide Population-Based Cohort Study

1
Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
2
Taipei Heart Institute, Taipei Medical University, Taipei 11031, Taiwan
3
Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
4
Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
5
School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung City 404333, Taiwan
6
Division of Cardiovascular Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
7
Department of Emergency, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
8
Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
9
TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei 110301, Taiwan
10
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City 23561, Taiwan
11
Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 110301, Taiwan
12
Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
13
Department of Surgery, MacKay Memorial Hospital, Taipei 10449, Taiwan
14
Division of Cardiology, Department of Internal Medicine and Cardiovascular Research Center, Taipei Medical University Hospital, Taipei 110301, Taiwan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2023, 15(8), 2398; https://doi.org/10.3390/cancers15082398
Submission received: 28 February 2023 / Revised: 13 April 2023 / Accepted: 18 April 2023 / Published: 21 April 2023
(This article belongs to the Section Cancer Epidemiology and Prevention)

Abstract

:

Simple Summary

Colorectal cancer, chronic kidney disease, and influenza infection were corelated through the chronic inflammation pathway and dysbiosis in the gut. Influenza infections aggravate the chronic inflammation status and dysbiosis in the gut, which may result in tumorigenesis and cause colorectal cancer. Seasonal influenza vaccination is a health policy in Taiwan. The aim of our study was to investigate whether influenza vaccination had a protective effect against colorectal cancer. A total of 12,985 patients with chronic kidney disease are listed in Taiwan’s National Health Insurance Research Database. The present study demonstrated that the influenza vaccine provides a potential protective effect against colorectal cancer in a population with chronic kidney disease.

Abstract

Chronic kidney disease (CKD) is associated with malignancy, including colorectal cancer, via the potential mechanism of chronic inflammation status. This study aimed to determine whether influenza vaccines can reduce the risk of colorectal cancer in patients with CKD. Our cohort study enrolled 12,985 patients older than 55 years with a diagnosis of CKD in Taiwan from the National Health Insurance Research Database at any time from 1 January 2001 to 31 December 2012. Patients enrolled in the study were divided into a vaccinated and an unvaccinated group. In this study, 7490 and 5495 patients were unvaccinated and vaccinated, respectively. A propensity score was utilized to reduce bias and adjust the results. Cox proportional hazards regression was used to estimate the correlation between the influenza vaccine and colorectal cancer in patients with CKD. The results showed that the influenza vaccine exerted a protective effect against colorectal cancer in populations with CKD. The incidence rate of colon cancer in the vaccinated group was significantly lower than in the unvaccinated group, with an adjusted hazard rate (HR) of 0.38 (95% CI: 0.30–0.48, p < 0.05). After the propensity score was adjusted for Charlson comorbidity index, age, sex, dyslipidemia, hypertension, diabetes, monthly income, and level of urbanization, the dose-dependent effect was found, and it revealed adjusted HRs of 0.74 (95% CI: 0.54–1.00, p < 0.05), 0.41 (95% CI: 0.30–0.57, p < 0.001), 0.16 (95% CI: 0.11–0.25, p < 0.001) for one, two to three, and four or more vaccinations, respectively. In summary, the influenza vaccine was found to be associated with a reduced risk of colorectal cancer in CKD patients. This study highlights the potential chemopreventive effect of influenza vaccination among patients with CKD. Future studies are required to determine whether the aforementioned relationship is a causal one.

1. Introduction

Chronic kidney disease (CKD) has multiple physiopathology pathways that cause various symptoms affecting the patient’s quality of life. Kidney function may be destroyed as the disease progresses. Approximately 11% of the population in high-income countries has been reported to have CKD. CKD is typically related to anemia, uremic syndrome, and cardiovascular disease; however, reports have revealed patients with CKD have a greater risk of cancer. Furthermore, bowel cancer ranks as the second most prevalent cancer among CKD patients and as the third most frequent cause of cancer-related mortality [1,2].
Patients with CKD are twice as likely to develop colorectal cancer than the general population [3]. Compared with that in the general population, the standardized incidence ratio of colorectal cancer in patients with predialytic CKD was reported to be 1.60 (95% CI: 1.45–1.74) [4]. Individuals in the CKD population not undergoing dialysis have a greater risk of colorectal cancer (hazard ratio, 1.79; 95% confidence interval [CI] 1.41–2.27) [5].
Chronic inflammation status is a symptom of CKD [6] and a key factor linked to various stages of tumor growth [6,7]. Moreover, abnormal metabolic status and uremic toxin retention may alter the gut microbiome in patients with CKD [6,8]. Studies have revealed that the gut microbiome plays a major role in inflammation and tumorigenesis due to the interaction between the bacterium and the host’s immune system [9,10]. Virus-induced chronic inflammation significantly increases the likelihood of cancer development by activating inflammatory signaling pathways [11]. A cross-sectional study reported that the richness and diversity of microbiome communities were significantly lower in patients with influenza H1N1 than in a healthy control group. A positive correlation was also observed between inflammatory cytokines and H1N1-enriched bacteria [12]. Through the suppression of inflammation, the risk of colorectal cancer could be reduced, and colorectal cancer carcinogenesis could be regulated [13]. Previous preclinical studies have indicated that an administrated intratumoral influenza vaccine can increase antitumor CD8+, decrease the level of regulatory B cells within the tumor, increase T cell microenvironment infiltration, increase local IFN-ƴ, and reduce the tumor mass [14,15]. Influenza vaccines also enhance the function of NK cells, promoting tumor cell elimination [14,16,17]. Patients undergoing surgical treatment of a tumor who had received an influenza vaccine had lower overall mortality (HR = 0.89, 95% CI = 0.81–0.99, p = 0.03) and cancer-related mortality (HR = 0.82, 95% CI = 0.72–0.94, p = 0.007) [18].
Seasonal influenza vaccination is a cost-effective health policy in Taiwan. The influenza vaccine is linked to decreases in the prevalence of chronic inflammation and mortality among postsurgical cancer patients [18]. Our study aimed to explore whether the administration of the influenza vaccine could reduce the occurrence of colorectal cancer in individuals with CKD. To examine the potential protective effects of the influenza vaccine against colorectal cancer in the CKD population in Taiwan, we conducted a cohort study based on population data obtained from Taiwan’s National Health Insurance Research Database (NHIRD) and its reimbursement claims.

2. Methods

2.1. Data Source

Taiwan established its National Health Insurance (NHI) program in 1995, which has achieved an enrollment rate of around 99% among the country’s 23.7 million residents. Additionally, over 97% of clinics in Taiwan participate in the NHI system. The NHIRD contains comprehensive registration records and files of all enrolled patients, with the data accessed by researchers deidentified to protect patient privacy and prevent the disclosure of specific people or institutions. All diagnoses were documented by physicians based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) system [19,20]. The study protocol was approved by both the research committee of the NHIRD and the Taipei Medical University Joint Institutional Review Board (TMU-JIRB No. N201804043).

2.2. Participants

Our study cohort comprised patients with a diagnosis of CKD, according to the ICD-9-CM, who had visited medical care institutions at any time from 1 January 2001, to 31 December 2012, in Taiwan (n = 32,844). We excluded patients without admission records or a second visit within 12 months to the outpatient or emergency department for CKD after the first visit (n = 9353). We also excluded patients younger than 55 years old (n = 6432) and patients with any inpatient or outpatient record of any malignancy-related disease diagnosed before the enrollment date of our cohort (n = 2780). Patients vaccinated in the 6 months before cohort entry (n = 1294) were also excluded. Therefore, we enrolled 12,985 patients diagnosed with CKD in Taiwan at any time between 2001 and 2012 in our study (Figure 1).

2.3. Potential Confounder

We classified the baseline characteristics and established the comorbidity history for each participant. The characteristics were as follows: age (categorized into four groups: 55–64, 65–74, ≥75 years old), sex (male, female), level of urbanization (urban, suburban, and rural area), monthly income (NTD 0, NTD 1–NTD 21,000, NTD 21,000–NTD 33,300, ≥NTD 33,301 corresponding to USD 0, USD 0.03–USD 700, USD 700–USD 1100, and ≥USD 1100, respectively), Charlson Comorbidity Index (CCI, categorized into four groups: 0, 1, 2, and ≥3) [19,20,21], diabetes (ICD-9-CM code 250), hypertension (ICD-9-CM codes 401–405), and hyperlipidemia (ICD-9-CM code 272). We also recorded the patient’s usage of medication, namely statin, metformin, the renin-angiotensin-aldosterone system [RAA] (such as aldosterone-receptor antagonists, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers), and aspirin [22,23,24].

2.4. Statical Analysis

Propensity scores (PS) were employed to account for age, gender, Charlson Comorbidity Index (CCI), comorbidities, medication use, monthly income (0, NTD 1 to NTD 21,000, NTD 21,000 to NTD 33,300, and >NTD 33,300), and urbanization level (urban, suburban, and rural) as covariates to achieve balance between the vaccinated and unvaccinated groups and minimize potential bias [25]. Characteristics such as age (55–64, 65–74, and ≥75 years old), gender, comorbidities, medication use, degree of urbanization, and monthly income were compared between the vaccination and comparison cohorts using the chi-squared test. At test was used for comparisons of the continuous variable of mean age. The primary endpoint of our study was the occurrence of colon cancer. In addition to newly diagnosed colon cancer, patients were followed until they dropped out from the NHI program, were lost to follow-up, died, or until 31 December 2012. We used the chi-square test to compare the difference in colon cancer ratios between vaccinated and unvaccinated individuals. The Kaplan–Meier method was used for investigating the colon cancer-free survival rate in patients with CKD who were vaccinated and unvaccinated. Cox proportional hazards regression was employed to determine the hazard ratios (HR) and 95% confidence intervals (CI) to investigate the association between influenza vaccination and the incidence rate of colorectal cancer. A stratified analysis was conducted to assess the effects of vaccination by age and gender. To investigate the dose-dependent effects of influenza vaccination on the incidence of colorectal cancer, we analyzed four patient categories: those who did not receive vaccination and those who received one, two to three, or more than four rounds of vaccination. The total number of vaccinations is defined by the total number of vaccinations a patient received from the index day to primary end point. We conducted a sensitivity analysis to improve our understanding of the effects of covariates on outcomes [20]. Therefore, we analyzed the associations of age; sex; CCI score; diabetes; hypertension; dyslipidemia; and the usage of a statin, metformin, RAA, and aspirin with colorectal cancer incidence in various models in the sensitivity analysis. All statistical analyses were conducted using SPSS Version 22.0 (SPSS, Chicago, IL, USA) and SAS 9.4 software (SAS Institute, Cary, NC, USA). The significance criterion was set at p < 0.05.

3. Result

3.1. Baseline Characteristics among Vaccinated and Unvaccinated Groups

A total of 12,985 individuals diagnosed with CKD between 2001 and 2012 enrolled in our cohort, with 5495 influenza-vaccinated (42.3%) and 7490 unvaccinated people (57.7%). The vaccinated and unvaccinated groups significantly differed in their age distribution, CCI index, level of urbanization, and monthly income. Vaccinated individuals tended to be older than unvaccinated individuals (Table 1). The patients in the unvaccinated group lived in urban areas more than the vaccinated group. We found significant differences between the vaccinated and unvaccinated groups in the prevalence of hypertension, dyslipidemia, and diabetes, and the medical usage of statin, metformin, aspirin, and RAA. The patients in the vaccinated group had taken chronic disease medication longer than those in the unvaccinated group. The total follow-up periods for the unvaccinated and vaccinated groups were 21,919.2 and 33,990.2 person-years, respectively.

3.2. Age and Sex among Vaccinated and Unvaccinated Groups

The incidence rate of colon cancer in the vaccinated group was significantly lower than in the unvaccinated group, with an adjusted HR of 0.38 (95% CI: 0.30–0.48, p < 0.05) (Table 2 and Figure 2). In a subgroup analysis by age, the vaccinated group had a lower incidence rate of colon cancer at the ages of 65–74 and older than 75 years, with HRs of 0.31 (95% CI: 0.22–0.44, p < 0.05) and 0.35 (95% CI: 0.23–0.52, p < 0.05), respectively. Similar effects were seen in both sexes, with HRs of 0.34 (95% CI: 0.22–0.52, p < 0.05) and 0.40 (95% CI: 0.30–0.53, p < 0.05) in women and men, respectively. The incidence rate of colorectal cancer in both groups increased with age. The incidence rates of colorectal cancer in the unvaccinated group at the ages of 55–64, 65–74, and >75 years were 361.5 (95% CI: 248.1–475.0), 1222.7 (95% CI: 945.9–1499.4), and 1220.6 (95% CI: 914.3–1526.9) per 105 person-years, respectively. However, the incidence rates were 277.8 (95% CI: 168.9–386.8), 391.9 (95% CI: 294.3–489.4), and 447.1 (95% CI: 310.2–584.0) per 105 person-years in the vaccinated group at ages 55–64, 65–74, and >75 years, respectively. Women had a lower incidence rate than men in both the vaccinated and unvaccinated groups. The incidence rates were 643.4 (95% CI: 483.2–803.5) and 247.0 (95% CI:167.4–326.6) for women in the unvaccinated and vaccinated groups, respectively. The incidence rates were 920.0 (95% CI:750.4–1089.6) and 478.7 (95% CI:380.3–577.0) for men in the unvaccinated and vaccinated groups, respectively.

3.3. Sensitivity Analysis

Multiple covariates (age, sex, comorbidity, urbanization level, socioeconomic factor) had their PSs adjusted for in a sensitivity analysis. The influenza vaccine exerted a significant protective effect in the main model and subgroups of covariates (Table 3). The dose-dependent effect of a vaccine was further determined in our study. The adjusted HRs of the main model were 0.74 (95% CI: 0.54–1.00, p < 0.05), 0.41 (95% CI: 0.30–0.57, p < 0.001), 0.16 (95% CI: 0.11–0.25, p < 0.001), for one, two to three, and four or more rounds of vaccination, respectively. The administration of more than two vaccination shots provided a strong protective effect against colorectal cancer. At younger ages (55–64 years old), patients had to receive more than four shots of vaccination for the protective effect of the influenza vaccine to become significant. The adjusted HRs of age for 55–64 years old were 0.86 (95% CI: 0.43–1.74), 0.76 (95% CI: 0.36–1.57), 0.39 (95% CI: 0.16–0.94, p < 0.05) for one, two to three, and four or more rounds of vaccination, respectively. However, in the older age group (≥65 years old), the administration of two or more vaccination shots could significantly protect against colorectal cancer. The adjusted HRs for age from 65–74 years old were 0.71 (95% CI: 0.45–1.11), 0.36 (95% CI: 0.22–0.58, p < 0.001), and 0.12 (95% CI: 0.07–0.22, p < 0.001) for one, two to three, and four or more rounds of vaccination, respectively. The adjusted HRs for those older than 75 years were 0.63 (95% CI: 0.38–1.06), 0.31 (95% CI: 0.17–0.55, p < 0.001), and 0.17 (95% CI: 0.08–0.37, p < 0.001) for one, two to three, and ≥ four vaccinations, respectively. In the subgroup of CCI ≥ 3, even one shot of influenza vaccination could protect the patient against colorectal cancer. The adjusted HRs for CCI ≥ 3 were 0.55 (95% CI: 0.35–0.84, p < 0.01), 0.30 (95% CI: 0.18–0.48, p < 0.001), and 0.08 (95% CI: 0.03–0.17, p < 0.001), for one, two to three, and four or more rounds of vaccination, respectively (Table 3, Figure 3).

4. Discussion

This is the first study to demonstrate the protective effect of the influenza vaccine against colorectal cancer in patients with CKD. In the present study, we found that the incidence rate of colorectal cancer decreased by approximately 50% for those with CKD who received the influenza vaccination. The dose-dependent effect indicated that patients who received more shots of the influenza vaccine might acquire a stronger protective effect. In the younger age subgroup (55–64 years old), the patient may receive four or more shots of the influenza vaccine to acquire protective effects against colorectal cancer. The protective effect is superior in patients with a CCI ≥ 3.
Studies describing the association between the influenza vaccine and colorectal cancer have indicated that receiving an influenza vaccine 6–12 months before colorectal surgery may reduce the risk of tumor recurrence (HR: 0.78, 95% CI: 0.67–0.90) [14]. Another study revealed that receiving an influenza vaccine 0–30 days after the surgery of a solid tumor could reduce the incidence rate of overall (HR: 0.73, 95% CI: 0.60–0.89, p = 0.002) and cancer-related mortality (HR: 0.70, 95% CI: 0.53–0.91, p = 0.009) [18].
Several mechanisms can be proposed as explanations for our results. First, multiple factors resulting in dysbiosis may be at work among patients with CKD; these factors include uremic toxin retention, metabolic acidosis, and fluid overload with intestinal wall congestion. Drug intake (antibiotics, iron supplements, and polymer phosphate binders) and diet (decreased fiber intake) are also critical factors that change the composition of the microbiome community in the gut [26]. Metabolic changes in patients with CKD may upset the balance of symbionts and pathobionts; therefore, they promote the overgrowth of pathobionts and induce inflammation and the loss of intestinal barrier function. Second, innate immunity was activated, and proinflammatory cytokines were produced due to metabolic changes [27]. The impaired intestinal barrier resulted in increased intestinal colonic permeability and an abnormal colonic epithelial barrier (leaky gut) [8]. In this situation, bacteria may activate myeloid differentiation factor 88 (MyD88) by engaging toll-like receptors on tumor-infiltrating myeloid cells and increasing the production of interleukin (IL)-23. IL-23 could activate IL-17A, IL-6, IL-22, and nuclear factor-κB (NFκB) with a signal transducer and the transcription-3 (STAT3) pathway. These reactions would eventually lead to tumor cell proliferation [28,29,30]. The increase in IL-17C caused by commensal bacteria would induce the expression of B-cell lymphoma-2 and Bcl-xL in intestinal epithelial cells, leading to tumor cell survival and tumorigenesis [30].
Besides, bacterial overgrowth and advanced antibiotic usage may lead to tumorigenesis. A study reported that antibiotics such as aminoglycoside, beta-lactam, and quinolones may affect mitochondrial function, which may result in DNA damage by reactive oxygen species (ROS) produced from mitochondria [31]. Moreover, not only antibiotics but also bacteria could produce ROS directly [10]. The bacteria may release various substances, such as exotoxin and lipopolysaccharide. These molecules resulted in the production of ROS, which caused DNA damage, tumorigenesis, and the inactivation of tumor suppressors [10,31].
However, a study revealed that influenza infection could also affect the microbiome in the gut and lead to dysbiosis. Influenza lung infection may change the composition of the microbiome in the gut due to type 1 interferons produced in the respiratory tract [12,32]. Furthermore, the intestine would be injured while the lung was infected by influenza. Lung-derived CCR9 + CD4 + T cells may migrate to the intestine and increase the production of IFN-γ [33]. Additionally, the reduced amount of butyrate-producing bacteria affected by influenza may cause an overgrowth of pathogenic bacteria. The microbiome alteration may result in a leaky gut and elevated endotoxin concentrations in the blood. Finally, the inflammatory status would be further activated, exacerbating cytokine release [34,35].
This mechanism may explain why the CKD population has a higher incidence rate of colorectal cancer [3,4]. The influenza vaccine may be a convenient and cost-effective method to protect individuals with CKD. However, the dose-dependent effect in our cohort indicated that in younger populations or populations with a lower CCI index, more shots of influenza vaccination may be required to achieve a strong protective effect.
The safety of influenza vaccine injection among patients with cancer receiving immune checkpoint inhibitors, immunotherapy, or chemotherapy has been reported. A meta-analysis demonstrated that the mortality rate (OR = 1.25, 95% CI = 0.81–1.92), p = non-significant) and the incidence of immune-related adverse events (OR = 0.82, 95% CI = 0.63–1.08, p = non-significant) in cancer patients receiving immune checkpoint inhibitors with influenza vaccination had no significant outcome compared to nonvaccinated patients [36].
Another study reported that applying the influenza vaccine to cancer patients receiving immunotherapy had better overall survival if the patient received the flu vaccine and/or developed influenza syndrome (HR = 0.71, 95%CI = 0.50–0.99, p = 0.044) [37].
In the colorectal cancer population with active chemotherapy, influenza vaccination also provides enough serological immune response. A study showed no difference in the serological response to the influenza vaccine between active chemotherapy patients and the patients on surveillance (OR = 0.78, p = 0.8) [38].
Although our study showed that the influenza vaccine provided a potential protective effect against colorectal cancer in the CKD population, not like the human papillomavirus vaccine, which could block the tumorigenesis pathway directly, the actual mechanisms and pathways may need more research and evidence to prove the causal relationship between the vaccine and the disease. Further research and clinical trials need to be conducted in the future for more solid evidence and pathophysiology mechanisms.
Our study had some limitations. First, our data were all electronic; therefore, coding errors, a common problem in NHIRD cohorts, could not be avoided entirely. In response, we set stricter criteria to improve the accuracy of the diagnosis. The patients enrolled in our study had to be recorded twice for CKD ICD-9CM within 12 months of their first admission or outpatient electronic profile. Second, we could not obtain data on several confounding factors from the NHIRD database, such as diet, body mass index, alcohol consumption, smoking status, and estimated glomerular filtration rate. Thus, we used the PS method to weaken the influence of bias and applied it in the nonrandomized control group to adjust the intervention results between the intervention and control groups [25,39]. Third, the influenza vaccination policy in Taiwan is a form of social welfare for older residents. Therefore, we only enrolled patients older than 55 years in our study. Future studies must include younger patients. Fourth, the observational study tends to make the outcome more effective, thus the PS method was used to reduce the potential bias and make the result more reliable. Finally, our retrospective cohort study design precluded causal inference; thus, future clinical trials should be conducted.

5. Conclusions

Influenza vaccination is associated with a lower risk of colorectal cancer among patients with CKD. Sensitivity analysis with multiple covariates revealed a dose-dependent effect of the vaccine in our study. Our study highlights the potential benefits of influenza vaccination in reducing the risk of colorectal cancer in high-risk populations with CKD. Further clinical trials should be conducted to provide more solid evidence and to identify the underlying pathophysiology mechanisms.

Author Contributions

Conceptualization, C.-C.C. (Chun-Chao Chen) and H.-J.H.; methodology, T.-L.Y. and W.-R.H.; validation, H.-J.H., K.-J.L. and C.-C.C. (Chun-Chih Chiu); formal analysis, Y.-A.F. and Y.-H.L.; investigation, Y.-A.F. and W.J.; resources, J.-C.L.; data curation, M.-Y.C. and M.-H.H.; writing—original draft preparation, S.-C.L. and C.-C.C. (Chun-Chao Chen); writing—review and editing, C.-C.C. (Chun-Chao Chen), T.-L.Y. and Y.-H.L.; supervision, J.-C.L.; project administration, T.-Y.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, the Taipei Medical University-National Taiwan University Hospital Joint Research Program, (111-TMU085), and 111FRP-02 from the Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare.

Institutional Review Board Statement

The study was approved by the Joint Institutional Review Board of Taipei Medical University (approval no. N201804043).

Informed Consent Statement

The Joint Institutional Review Board of Taipei Medical University waived the requirement of an informed consent statement because all of the individual data was de-identified according to the ethical guidelines of NHIRD.

Data Availability Statement

The data supporting the findings of this research were sourced from NHIRD in Taiwan. Owing to the legal restrictions imposed by the Government of Taiwan related to the Personal Information Protection Act, the database cannot be made publicly available.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Webster, A.C.; Nagler, E.V.; Morton, R.L.; Masson, P. Chronic Kidney Disease. Lancet 2017, 389, 1238–1252. [Google Scholar] [CrossRef] [PubMed]
  2. James, L.J.; Wong, G.; Craig, J.C.; Ju, A.; Williams, N.; Lim, W.H.; Cross, N.; Tong, A. Beliefs and Attitudes to Bowel Cancer Screening in Patients with CKD: A Semistructured Interview Study. Clin. J. Am. Soc. Nephrol. 2017, 12, 568–576. [Google Scholar] [CrossRef] [PubMed]
  3. Au, E.H.; Wong, G.; Howard, K.; Chapman, J.R.; Castells, A.; Roger, S.D.; Bourke, M.J.; Macaskill, P.; Turner, R.; Lim, W.H.; et al. Factors Associated With Advanced Colorectal Neoplasia in Patients with CKD. Am. J. Kidney Dis. 2022, 79, 549–560. [Google Scholar] [CrossRef] [PubMed]
  4. Oh, H.J.; Lee, H.A.; Moon, C.M.; Ryu, D.-R. Incidence risk of various types of digestive cancers in patients with pre-dialytic chronic kidney disease: A nationwide population-based cohort study. PLoS ONE 2018, 13, e0207756. [Google Scholar] [CrossRef] [PubMed]
  5. Wu, M.-Y.; Chang, T.-C.; Chao, T.-Y.; Huang, M.-T.; Lin, H.-W. Risk of Colorectal Cancer in Chronic Kidney Disease: A Matched Cohort Study Based on Administrative Data. Ann. Surg. Oncol. 2013, 20, 3885–3891. [Google Scholar] [CrossRef]
  6. Cobo, G.; Lindholm, B.; Stenvinkel, P. Chronic inflammation in end-stage renal disease and dialysis. Nephrol. Dial. Transplant. 2018, 33 (Suppl. S3), iii35–iii40. [Google Scholar] [CrossRef]
  7. Singh, N.; Baby, D.; Rajguru, J.P.; Patil, P.B.; Thakkannavar, S.S.; Pujari, V.B. Inflammation and cancer. Ann. Afr. Med. 2019, 18, 121–126. [Google Scholar] [CrossRef]
  8. Evenepoel, P.; Poesen, R.; Meijers, B. The gut-kidney axis. Pediatr. Nephrol. 2017, 32, 2005–2014. [Google Scholar] [CrossRef]
  9. Arthur, J.C.; Perez-Chanona, M.E.; Mühlbauer, S.; Tomkovich, J.M.; Uronis, T.J.; Fan, B.J.; Campbell, T.; Abujamel, B.; Dogan, A.B.; Rogers, A.B.; et al. Intestinal Inflammation Targets Cancer-Inducing Activity of the Microbiota. Science 2012, 338, 120–123. [Google Scholar] [CrossRef]
  10. Cheng, Y.; Ling, Z.; Li, L. The Intestinal Microbiota and Colorectal Cancer. Front. Immunol. 2020, 11, 3100. [Google Scholar] [CrossRef]
  11. Read, S.A.; Douglas, M.W. Virus induced inflammation and cancer development. Cancer Lett. 2014, 345, 174–181. [Google Scholar] [CrossRef]
  12. Gu, S.; Chen, Y.; Wu, Z.; Chen, Y.; Gao, H.; Lv, L.; Guo, F.; Zhang, X.; Luo, R.; Huang, C.; et al. Alterations of the Gut Microbiota in Patients with Coronavirus Disease 2019 or H1N1 Influenza. Clin. Infect. Dis. 2020, 71, 2669–2678. [Google Scholar] [CrossRef]
  13. Guo, C.; Guo, D.; Fang, L.; Sang, T.; Wu, J.; Guo, C.; Wang, Y.; Wang, Y.; Chen, C.; Chen, J.; et al. Ganoderma lucidum polysaccharide modulates gut microbiota and immune cell function to inhibit inflammation and tumorigenesis in colon. Carbohydr. Polym. 2021, 267, 118231. [Google Scholar] [CrossRef]
  14. Gögenur, M.; Fransgård, T.; Krause, T.G.; Thygesen, L.C.; Gögenur, I. Association of influenza vaccine and risk of recurrence in patients undergoing curative surgery for colorectal cancer. Acta. Oncol. 2021, 60, 1507–1512. [Google Scholar] [CrossRef]
  15. Newman, J.H.; Chesson, C.B.; Herzog, N.L.; Bommareddy, P.K.; Aspromonte, S.M.; Pepe, R.; Estupinian, R.; Aboelatta, M.M.; Buddhadev, S.; Tarabichi, S.; et al. Intratumoral injection of the seasonal flu shot converts immunologically cold tumors to hot and serves as an immunotherapy for cancer. Proc. Natl. Acad. Sci. USA 2020, 117, 1119–1128. [Google Scholar] [CrossRef]
  16. Tai, L.-H.; Zhang, J.; Auer, R.C. Preventing surgery-induced NK cell dysfunction and cancer metastases with influenza vaccination. Oncoimmunology 2013, 2, e26618. [Google Scholar] [CrossRef]
  17. Tai, L.-H.; Zhang, J.; Scott, K.J.; de Souza, C.T.; Alkayyal, A.A.; Ananth, A.A.; Sahi, S.; Adair, R.A.; Mahmoud, A.B.; Sad, S.; et al. Perioperative Influenza Vaccination Reduces Postoperative Metastatic Disease by Reversing Surgery-Induced Dysfunction in Natural Killer Cells. Clin. Cancer Res. 2013, 19, 5104–5115. [Google Scholar] [CrossRef]
  18. Gögenur, M.; Fransgård, T.; Krause, T.G.; Thygesen, L.C.; Gögenur, I. Association of postoperative influenza vaccine on overall mortality in patients undergoing curative surgery for solid tumors. Int. J. Cancer 2021, 148, 1821–1827. [Google Scholar] [CrossRef]
  19. Chen, C.-C.; Lin, C.-H.; Yang, T.Y.; Wang, T.-J.; Li, S.-J.; Fang, Y.-A.; Chen, T.-J.; Tzeng, H.-E.; Chiu, C.-C.; Hao, W.-R.; et al. Association between sleep disorder and atrial fibrillation: A nationwide population-based cohort study. Sleep Med. 2022, 96, 50–56. [Google Scholar] [CrossRef]
  20. Chen, C.-C.; Wu, C.-H.; Lin, C.-H.; Chiu, C.-C.; Yang, T.-Y.; Lei, M.-H.; Yeh, H.-T.; Jian, W.; Fang, Y.-A.; Hao, W.-R.; et al. Influenza Vaccination and Risk of Lung Cancer in Patients with Chronic Kidney Disease: A Nationwide, Population-Based Cohort Study. Cancers 2022, 14, 2926. [Google Scholar] [CrossRef]
  21. Charlson, M.E.; Pompei, P.; Ales, K.L.; MacKenzie, C.R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 1987, 40, 373–383. [Google Scholar] [CrossRef] [PubMed]
  22. Rothwell, P.M.; Wilson, M.; Elwin, C.-E.; Norrving, B.; Algra, A.; Warlow, C.P.; Meade, T.W. Long-term effect of aspirin on colorectal cancer incidence and mortality: 20-year follow-up of five randomised trials. Lancet 2010, 376, 1741–1750. [Google Scholar] [CrossRef] [PubMed]
  23. Seretis, A.; Cividini, S.; Markozannes, G.; Tseretopoulou, X.; Lopez, D.S.; Ntzani, E.E.; Tsilidis, K.K. Association between blood pressure and risk of cancer development: A systematic review and meta-analysis of observational studies. Sci. Rep. 2019, 9, 8565. [Google Scholar] [CrossRef]
  24. Yao, X.; Tian, Z. Dyslipidemia and colorectal cancer risk: A meta-analysis of prospective studies. Cancer Causes Control. 2015, 26, 257–268. [Google Scholar] [CrossRef] [PubMed]
  25. D’Agostino, R.B., Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 1998, 17, 2265–2281. [Google Scholar] [CrossRef]
  26. Maslowski, K.M.; Mackay, C.R. Diet, gut microbiota and immune responses. Nat. Immunol. 2011, 12, 5–9. [Google Scholar] [CrossRef]
  27. Anders, H.-J.; Andersen, K.; Stecher, B. The intestinal microbiota, a leaky gut, and abnormal immunity in kidney disease. Kidney Int. 2013, 83, 1010–1016. [Google Scholar] [CrossRef]
  28. Cremonesi, E.; Governa, V.; Garzon, J.F.G.; Mele, V.; Amicarella, F.; Muraro, M.G.; Trella, E.; Galati-Fournier, V.; Oertli, D.; Däster, S.R.; et al. Gut microbiota modulate T cell trafficking into human colorectal cancer. Gut 2018, 67, 1984–1994. [Google Scholar] [CrossRef]
  29. Grivennikov, S.I.; Wang, D.K.; Mucida, C.A.; Stewart, B.; Schnabl, D.; Jauch, K.; Taniguchi, G.Y.; Yu, C.H.; Osterreicher, K.E.; Hung, K.E.; et al. Adenoma-linked barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth. Nature 2012, 491, 254–258. [Google Scholar] [CrossRef]
  30. Song, X.; Gao, H.; Lin, Y.; Yao, Y.; Zhu, S.; Wang, J.; Liu, Y.; Yao, X.; Meng, G.; Shen, N.; et al. Alterations in the Microbiota Drive Interleukin-17C Production from Intestinal Epithelial Cells to Promote Tumorigenesis. Immunity 2014, 40, 140–152. [Google Scholar] [CrossRef]
  31. Hosainzadegan, M.; Eftekhari, A.; Khalilov, R.; Nasibova, A.; Hasanzadeh, A.; Vahedi, P. Are Microbial Infections and Some Antibiotics Causes Cancer? Adv. Biol. Earth Sci. 2020, 5, 58–61. [Google Scholar]
  32. Deriu, E.; Boxx, G.M.; He, X.; Pan, C.; Benavidez, S.D.; Cen, L.; Rozengurt, N.; Shi, W.; Cheng, G. Influenza Virus Affects Intestinal Microbiota and Secondary Salmonella Infection in the Gut through Type I Interferons. PLoS Pathog. 2016, 12, e1005572. [Google Scholar] [CrossRef]
  33. Wang, J.; Li, F.; Wei, H.; Lian, Z.X.; Sun, R.; Tian, Z. Respiratory influenza virus infection induces intestinal immune injury via microbiota-mediated Th17 cell-dependent inflammation. J. Exp. Med. 2014, 211, 2397–2410. [Google Scholar] [CrossRef]
  34. Antunes, K.H.; Fachi, J.L.; De Paula, R.; Da Silva, E.F.; Pral, L.P.; DOS Santos, A.; Dias, G.B.M.; Vargas, J.E.; Puga, R.; Mayer, F.Q.; et al. Microbiota-derived acetate protects against respiratory syncytial virus infection through a GPR43-type 1 interferon response. Nat. Commun. 2019, 10, 3273. [Google Scholar] [CrossRef]
  35. Haase, S.; Haghikia, A.; Wilck, N.; Müller, D.N.; Linker, R.A. Impacts of microbiome metabolites on immune regulation and autoimmunity. Immunology 2018, 154, 230–238. [Google Scholar] [CrossRef]
  36. Tsiakos, K.; Kyriakoulis, K.G.; Kollias, A.; Kyriakoulis, I.G.; Poulakou, G.; Syrigos, K. Influenza Vaccination in Cancer Patients Treated with Immune Checkpoint Inhibitors: A Systematic Review and Meta-analysis. J. Immunother. 2022, 45, 291–298. [Google Scholar] [CrossRef]
  37. Bersanelli, M.; Buti, S.; Banna, G.L.; De Giorgi, U.; Cortellini, A.; Rebuzzi, S.E.; Tiseo, M.; Fornarini, G.; Mazzoni, F.; Panni, S.; et al. Impact of influenza syndrome and flu vaccine on survival of cancer patients during immunotherapy in the INVIDIa study. Immunotherapy 2020, 12, 151–159. [Google Scholar] [CrossRef]
  38. Puthillath, A.; Trump, D.L.; Andrews, C.; Bir, A.; Romano, K.; Wisniewski, M.; Fakih, M.G. Serological immune responses to influenza vaccine in patients with colorectal cancer. Cancer Chemother. Pharmacol. 2011, 67, 111–115. [Google Scholar] [CrossRef]
  39. Rosenbaum, P.R.; Rubin, D.B. The central role of the propensity score in observational studies for causal effects. Biometrika 1983, 70, 41–55. [Google Scholar] [CrossRef]
Figure 1. Data selection process.
Figure 1. Data selection process.
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Figure 2. Free of colon cancer survival rate in vaccinated and unvaccinated group.
Figure 2. Free of colon cancer survival rate in vaccinated and unvaccinated group.
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Figure 3. Free of colon cancer survival rate in different numbers of vaccination.
Figure 3. Free of colon cancer survival rate in different numbers of vaccination.
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Table 1. Characteristics of sample population.
Table 1. Characteristics of sample population.
Whole Cohort
(n = 12,985)
Unvaccinated
(n = 7490)
Vaccinated
(n = 5495)
p a
n%n%n%
Age, years (mean ± SD)70.98 (9.40)70.09 (10.26)72.18 (7.90)<0.001
55–64398930.72 287738.41 111220.24 <0.001
65–74454134.97 213928.56 240243.71
≥75445534.31 247433.03 198136.05
Gender
Female 571243.99 333344.50 237943.29 0.172
Male727356.01 415755.50 311656.71
CCI Index +
0149111.48 87611.70 61511.19 0.013
1204315.73 116615.57 87715.96
2287622.15 158921.21 128723.42
≥3657550.64 385951.52 271649.43
Diabetes
No631048.59 335544.79 295553.78 <0.001
Yes667551.41 413555.21 254046.22
Hypertension
No255519.68 138718.52 116821.26 <0.001
Yes10,43080.32 610381.48 432778.74
Dyslipidemia
No633748.80 338645.21 295153.70 <0.001
Yes664851.20 410454.79 254446.30
Statin
<28 days797261.39 478663.90 318657.98 <0.001
28–365 days268320.66 157621.04 110720.15
>365 days233017.94 112815.06 120221.87
Metformin
<28 days10,26679.06 604580.71 422176.82 <0.001
28–365 days133110.25 80410.73 5279.59
>365 days138810.69 6418.56 74713.59
RAA
<28 days411431.68 279237.28 132224.06 <0.001
28–365 days375128.89 234431.30 140725.61
>365 days512039.43 235431.43 276650.34
Aspirin
<28 days671551.71 447859.79 223740.71 <0.001
28–365 days314924.25 170222.72 144726.33
>365 days312124.04 131017.49 181132.96
Level of urbanization
Urban878567.65 535071.43 343562.51 <0.001
Suburban280621.61 148819.87 131823.99
Rural139410.74 6528.70 74213.50
Monthly income (NTD)
0159612.29 90112.03 69512.65 <0.001
1–21,000448634.55 239732.00 208938.02
21,000–33,300378829.17 199626.65 179232.61
≥33,301311523.99 219629.32 91916.72
a Comparison between unvaccinated and vaccinated. + CCI Index: Charlson Comorbidity Index. SD: standard deviation. RAA: renin-angiotensin-aldosterone system. NTD: New Taiwan Dollar.
Table 2. Risk of colon cancer among unvaccinated and vaccinated in study cohort.
Table 2. Risk of colon cancer among unvaccinated and vaccinated in study cohort.
All Group
(n = 12,985)
Unvaccinated
(Total Follow-up 21,919.2 Person-Years)
Vaccinated
(Total Follow-up 33,990.2 Person-Years)
χ2Adjusted HR
(95% C.I.)
No. of
Patients
with Cancer
Incidence Rate
(Per 105 Person-Years)
(95% C.I.)
No. of
Patients
With Cancer
Incidence Rate
(Per 105 Person-Years)
(95% C.I.)
175798.4 (680.1, 916.7) 128376.6 (311.3, 441.8) 0.0010.38 (0.30, 0.48) ***
Age, 55–64 a39361.5 (248.1, 475.0) 25277.8 (168.9, 386.8) 3.5020.65 (0.39, 1.09)
Age, 65–74 b751222.7 (945.9, 1499.4) 62391.9 (294.3, 489.4) 3.0010.31 (0.22, 0.44) ***
Age, ≥75 c611220.6 (914.3, 1526.9) 41447.1 (310.2, 584.0) 0.6040.35 (0.23, 0.52) ***
Female d62643.4 (483.2, 803.5) 37247.0 (167.4, 326.6) 0.5890.34 (0.22, 0.52) ***
Male e113920.0 (750.4, 1089.6) 91478.7 (380.3, 577.0) 0.1980.40 (0.30, 0.53) ***
a Total follow-up person-years of 10,787.5 for unvaccinated and 8998.0 for vaccinated individuals. b Total follow-up person-years of 6134.2 for unvaccinated and 15,821.9 for vaccinated individuals. c Total follow-up person-years of 4997.5 for unvaccinated and 9170.3 for vaccinated individuals. d Total follow-up person-years of 9636.6 for unvaccinated and 14,979.9 for vaccinated individuals. e Total follow-up person-years of 12,282.6 for unvaccinated and 19,010.3 for vaccinated individuals. C.I.: confidence interval. HR: hazard ratio. † Main model is adjusted for age, sex, Charlson Comorbidity Index, diabetes, hypertension, dyslipidemia, level of urbanization, and monthly income in propensity score. ***: p < 0.001. χ2: chi-square test statistic
Table 3. Sensitivity analysis of adjusted HRs of vaccination in risk reduction of colon cancer.
Table 3. Sensitivity analysis of adjusted HRs of vaccination in risk reduction of colon cancer.
UnvaccinatedVaccinatedp for Trend
12–3≥4
Adjusted HR
(95% C.I.)
Adjusted HR
(95% C.I.)
Adjusted HR
(95% C.I.)
Adjusted HR
(95% C.I.)
Main model 1.000.74 (0.54, 1.00) *0.41 (0.30, 0.57) ***0.16 (0.11, 0.25) ***<0.001
Additional covariates
Main model + statin1.000.75 (0.55, 1.01)0.42 (0.30, 0.59) ***0.17 (0.11, 0.26) ***<0.001
Main model + metformin1.000.74 (0.55, 1.01)0.42 (0.30, 0.58) ***0.17 (0.11, 0.25) ***<0.001
Main model + RAA1.000.76 (0.56, 1.04)0.43 (0.31, 0.60) ***0.18 (0.12, 0.27) ***<0.001
Main model + aspirin1.000.77 (0.57, 1.05)0.44 (0.32, 0.62) ***0.18 (0.12, 0.28) ***<0.001
Subgroup effects
Age, years
55–641.000.86 (0.43, 1.74)0.76 (0.36, 1.57)0.39 (0.16, 0.94) *0.037
65–741.000.71 (0.45, 1.11)0.36 (0.22, 0.58) ***0.12 (0.07, 0.22) ***<0.001
≥751.000.63 (0.38, 1.06)0.31 (0.17, 0.55) ***0.17 (0.08, 0.37) ***<0.001
Sex
Female1.000.57 (0.32, 1.01)0.44 (0.25, 0.77) **0.11 (0.05, 0.27) ***<0.001
Male1.000.84 (0.58, 1.21)0.40 (0.26, 0.60) ***0.19 (0.11, 0.30) ***<0.001
CCI Index +
01.000.66 (0.23, 1.94)0.80 (0.34, 1.84)0.07 (0.01, 0.49) **0.004
11.000.75 (0.31, 1.80)0.26 (0.08, 0.90) *0.31 (0.12, 0.79) *0.005
21.001.23 (0.70, 2.17)0.57 (0.30, 1.09)0.30 (0.16, 0.59) ***<0.001
≥31.000.55 (0.35, 0.84)**0.30 (0.18, 0.48) ***0.08 (0.03, 0.17) ***<0.001
Diabetes
No1.000.67 (0.44, 1.02)0.38 (0.24, 0.59) ***0.19 (0.12, 0.31) ***<0.001
Yes1.000.80 (0.51, 1.25)0.44 (0.27, 0.73) **0.10 (0.04, 0.25) ***<0.001
Dyslipidemia
No1.000.72 (0.48, 1.09)0.47 (0.31, 0.72) ***0.22 (0.14, 0.36) ***<0.001
Yes1.000.74 (0.47, 1.16)0.31 (0.18, 0.54) ***0.07 (0.03, 0.18) ***<0.001
Hypertension
No1.000.61 (0.32, 1.14)0.49 (0.27, 0.89) *0.22 (0.11, 0.43) ***<0.001
Yes1.000.78 (0.55, 1.10)0.38 (0.25, 0.56) ***0.14 (0.08, 0.23) ***<0.001
Statin
<28 days1.000.67 (0.46, 0.98) *0.41 (0.27, 0.62) ***0.17 (0.10, 0.29) ***<0.001
28–365 days1.000.95 (0.51, 1.76)0.28 (0.12, 0.64) **0.20 (0.08, 0.46) ***<0.001
>365 days1.000.89 (0.34, 2.32)0.84 (0.36, 1.98)0.15 (0.04, 0.54) **0.004
Metformin
<28 days1.000.73 (0.52, 1.02)0.40 (0.28, 0.58) ***0.15 (0.09, 0.24) ***<0.001
28–365 days1.001.30 (0.44, 3.84)0.71 (0.21, 2.47)0.34 (0.07, 1.72)0.168
>365 days1.000.61 (0.22, 1.64)0.37 (0.15, 0.96) *0.21 (0.08, 0.56) **0.001
RAA
<28 days1.000.39 (0.20, 0.76) **0.37 (0.20, 0.68) **0.22 (0.11, 0.44) ***<0.001
28–365 days1.000.91 (0.53, 1.58)0.34 (0.17, 0.68) **0.14 (0.06, 0.36) ***<0.001
>365 days1.001.09 (0.67, 1.75)0.59 (0.35, 0.98) *0.19 (0.10, 0.35) ***<0.001
Aspirin
<28 days1.000.61 (0.39, 0.94) *0.31 (0.18, 0.53) ***0.15 (0.08, 0.29) ***<0.001
28–365 days1.000.95 (0.55, 1.67)0.60 (0.34, 1.06)0.12 (0.05, 0.31) ***<0.001
>365 days1.001.29 (0.61, 2.70)0.69 (0.32, 1.48)0.39 (0.19, 0.83) *<0.006
* p < 0.05 **; p < 0.01 ***; p < 0.001. HR: hazard ratio. + CCI Index: Charlson Comorbidity Index. Main model is adjusted for age, sex, Charlson Comorbidity Index, diabetes, hypertension, dyslipidemia, level of urbanization, and monthly income in propensity score. The models were adjusted for covariates in the main model and each additionally listed covariate.
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Chen, C.-C.; Hao, W.-R.; Hong, H.-J.; Lin, K.-J.; Chiu, C.-C.; Yang, T.-Y.; Fang, Y.-A.; Jian, W.; Chen, M.-Y.; Hsu, M.-H.; et al. Protective Effects of Influenza Vaccine against Colorectal Cancer in Populations with Chronic Kidney Disease: A Nationwide Population-Based Cohort Study. Cancers 2023, 15, 2398. https://doi.org/10.3390/cancers15082398

AMA Style

Chen C-C, Hao W-R, Hong H-J, Lin K-J, Chiu C-C, Yang T-Y, Fang Y-A, Jian W, Chen M-Y, Hsu M-H, et al. Protective Effects of Influenza Vaccine against Colorectal Cancer in Populations with Chronic Kidney Disease: A Nationwide Population-Based Cohort Study. Cancers. 2023; 15(8):2398. https://doi.org/10.3390/cancers15082398

Chicago/Turabian Style

Chen, Chun-Chao, Wen-Rui Hao, Hong-Jye Hong, Kuan-Jie Lin, Chun-Chih Chiu, Tsung-Yeh Yang, Yu-Ann Fang, William Jian, Ming-Yao Chen, Min-Huei Hsu, and et al. 2023. "Protective Effects of Influenza Vaccine against Colorectal Cancer in Populations with Chronic Kidney Disease: A Nationwide Population-Based Cohort Study" Cancers 15, no. 8: 2398. https://doi.org/10.3390/cancers15082398

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