Insights into Cancer Metabolism from Metabolomics

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 24172

Special Issue Editors


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Guest Editor
Department of Otolaryngology-Head & Neck Surgery, Indiana University School of Medicine, 1130 W. Michigan Ave, Indianapolis, IN 46202, USA
Interests: metabolomics; biomarkers; cancer and chemotherapy induced cachexia; microbiota metabolism; metabolic reprogramming
School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Interests: metabolomics; carbon source metabolism and its regulation in cancer cells; gut-microbial-host co-metabolism in gastrointestinal cancers
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Special Issue Information

Dear Colleagues,

There is an increasing recognition that, beyond somatic mutations, cancer can also be considered a metabolic disease. Proliferating tumors hijack normal metabolic processes to support increased bioenergetic and biosynthetic demands. Metabolomics investigations have made significant contributions to the mechanistic understanding of dysregulated cancer metabolism. In this Special Issue, we will publish reviews and original research articles that demonstrate the power of advanced metabolomics studies to identify diagnostic and/or prognostic biomarkers as well as reveal the detailed metabolic perturbations associated with cancer mechanisms.

Prof. Dr. Thomas M. O'Connell
Dr. Wei Jia
Guest Editors

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Keywords

  • metabolomics
  • cancer
  • metabolic reprogramming
  • mitochondrial dysfunction
  • metabolic biomarkers

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Published Papers (6 papers)

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Research

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17 pages, 4929 KiB  
Article
Integrative Metabolomic and Lipidomic Profiling of Lung Squamous Cell Carcinoma for Characterization of Metabolites and Intact Lipid Species Related to the Metastatic Potential
by Heayyean Lee, Hwanhui Lee, Sujeong Park, Myeongsun Kim, Ji Young Park, Hanyong Jin, Kyungsoo Oh, Jeehyeon Bae, Young Yang and Hyung-Kyoon Choi
Cancers 2021, 13(16), 4179; https://doi.org/10.3390/cancers13164179 - 19 Aug 2021
Cited by 4 | Viewed by 3263
Abstract
SQCC is a major type of NSCLC, which is a major cause of cancer-related deaths, and there were no reports regarding the prediction of metastatic potential of lung SQCC by metabolomic and lipidomic profiling. In this study, metabolomic and lipidomic profiling of lung [...] Read more.
SQCC is a major type of NSCLC, which is a major cause of cancer-related deaths, and there were no reports regarding the prediction of metastatic potential of lung SQCC by metabolomic and lipidomic profiling. In this study, metabolomic and lipidomic profiling of lung SQCC were performed to predict its metastatic potential and to suggest potential therapeutic targets for the inhibition of lung SQCC metastasis. Human bronchial epithelial cells and four lung SQCC cell lines with different metastatic potentials were analyzed using gas chromatography–mass spectrometry and direct infusion-mass spectrometry. Based on the obtained metabolic and lipidomic profiles, we constructed models to predict the metastatic potential of lung SQCC; glycerol, putrescine, β-alanine, hypoxanthine, inosine, myo-inositol, phosphatidylinositol (PI) 18:1/18:1, and PI 18:1/20:4 were suggested as characteristic metabolites and intact lipid species associated with lung SQCC metastatic potential. In this study, we established predictive models for the metastatic potential of lung SQCC; furthermore, we identified metabolites and intact lipid species relevant to lung SQCC metastatic potential that may serve as potential therapeutic targets for the inhibition of lung SQCC metastasis. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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15 pages, 502 KiB  
Article
Association between Pre-Diagnostic Serum Bile Acids and Hepatocellular Carcinoma: The Singapore Chinese Health Study
by Claire E. Thomas, Hung N. Luu, Renwei Wang, Guoxiang Xie, Jennifer Adams-Haduch, Aizhen Jin, Woon-Puay Koh, Wei Jia, Jaideep Behari and Jian-Min Yuan
Cancers 2021, 13(11), 2648; https://doi.org/10.3390/cancers13112648 - 28 May 2021
Cited by 36 | Viewed by 3584
Abstract
Hepatocellular carcinoma (HCC) is a commonly diagnosed malignancy with poor prognosis. Rising incidence of HCC may be due to rising prevalence of metabolic dysfunction-associated fatty liver disease, where altered bile acid metabolism may be implicated in HCC development. Thirty-five bile acids were quantified [...] Read more.
Hepatocellular carcinoma (HCC) is a commonly diagnosed malignancy with poor prognosis. Rising incidence of HCC may be due to rising prevalence of metabolic dysfunction-associated fatty liver disease, where altered bile acid metabolism may be implicated in HCC development. Thirty-five bile acids were quantified using ultra-performance liquid chromatography triple-quadrupole mass spectrometry assays in pre-diagnostic serum of 100 HCC cases and 100 matched controls from the Singapore Chinese Health Study. Conditional logistic regression was used to assess associations for bile acid levels with risk of HCC. Conjugated primary bile acids were significantly elevated whereas the ratios of secondary bile acids over primary bile acids were significantly lower in HCC cases than controls. The respective odds ratios and 95% confidence intervals of HCC were 6.09 (1.75–21.21) for highest vs. lowest tertile of cholic acid species and 30.11 (5.88–154.31) for chenodeoxycholic acid species. Doubling ratio of taurine-over glycine-conjugated chenodeoxycholic acid was associated significantly with 40% increased risk of HCC whereas doubling ratio of secondary over primary bile acid species was associated with 30–40% reduced risk of HCC. In conclusion, elevated primary bile acids and taurine over glycine-conjugated ratios were strongly associated with HCC risk whereas the ratios of secondary bile acids over primary bile acids were inversely associated with HCC risk. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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23 pages, 20919 KiB  
Article
Systematic Identification of MACC1-Driven Metabolic Networks in Colorectal Cancer
by Jan Lisec, Dennis Kobelt, Wolfgang Walther, Margarita Mokrizkij, Carsten Grötzinger, Carsten Jaeger, Katharina Baum, Mareike Simon, Jana Wolf, Nicola Beindorff, Winfried Brenner and Ulrike Stein
Cancers 2021, 13(5), 978; https://doi.org/10.3390/cancers13050978 - 26 Feb 2021
Cited by 5 | Viewed by 2863
Abstract
MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and [...] Read more.
MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and show the comprehensive analysis of MACC1-driven metabolic networks. We analyzed concentration-dependent changes in nutrient use, nutrient depletion, metabolic tracing employing 13C-labeled substrates, and in vivo studies. We found that MACC1 permits numerous effects on cancer metabolism. Most of those effects increased nutrient uptake. Furthermore, MACC1 alters metabolic pathways by affecting metabolite production or turnover from metabolic substrates. MACC1 supports use of glucose, glutamine and pyruvate via their increased depletion or altered distribution within metabolic pathways. In summary, we demonstrate that MACC1 is an important regulator of metabolism in cancer cells. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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22 pages, 6231 KiB  
Article
Targeted Metabolomics Identifies Plasma Biomarkers in Mice with Metabolically Heterogeneous Melanoma Xenografts
by Daniela D. Weber, Maheshwor Thapa, Sepideh Aminzadeh-Gohari, Anna-Sophia Redtenbacher, Luca Catalano, René G. Feichtinger, Peter Koelblinger, Guido Dallmann, Michael Emberger, Barbara Kofler and Roland Lang
Cancers 2021, 13(3), 434; https://doi.org/10.3390/cancers13030434 - 23 Jan 2021
Cited by 11 | Viewed by 4175
Abstract
Melanomas are genetically and metabolically heterogeneous, which influences therapeutic efficacy and contributes to the development of treatment resistance in patients with metastatic disease. Metabolite phenotyping helps to better understand complex metabolic diseases, such as melanoma, and facilitates the development of novel therapies. Our [...] Read more.
Melanomas are genetically and metabolically heterogeneous, which influences therapeutic efficacy and contributes to the development of treatment resistance in patients with metastatic disease. Metabolite phenotyping helps to better understand complex metabolic diseases, such as melanoma, and facilitates the development of novel therapies. Our aim was to characterize the tumor and plasma metabolomes of mice bearing genetically different melanoma xenografts. We engrafted the human melanoma cell lines A375 (BRAF mutant), WM47 (BRAF mutant), WM3000 (NRAS mutant), and WM3311 (BRAF, NRAS, NF1 triple-wildtype) and performed a broad-spectrum targeted metabolomics analysis of tumor and plasma samples obtained from melanoma-bearing mice as well as plasma samples from healthy control mice. Differences in ceramide and phosphatidylcholine species were observed between melanoma subtypes irrespective of the genetic driver mutation. Furthermore, beta-alanine metabolism differed between melanoma subtypes and was significantly enriched in plasma from melanoma-bearing mice compared to healthy mice. Moreover, we identified beta-alanine, p-cresol sulfate, sarcosine, tiglylcarnitine, two dihexosylceramides, and one phosphatidylcholine as potential melanoma biomarkers in plasma. The present data reflect the metabolic heterogeneity of melanomas but also suggest a diagnostic biomarker signature for melanoma screening. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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13 pages, 1494 KiB  
Article
Metabolites as Prognostic Markers for Metastatic Non-Small Cell Lung Cancer (NSCLC) Patients Treated with First-Line Platinum-Doublet Chemotherapy
by Desirée Hao, Arjun Sengupta, Keyue Ding, ER Ubeydullah, Saikumari Krishnaiah, Natasha B. Leighl, Frances A. Shepherd, Lesley Seymour and Aalim Weljie
Cancers 2020, 12(7), 1926; https://doi.org/10.3390/cancers12071926 - 16 Jul 2020
Cited by 9 | Viewed by 3010
Abstract
The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear [...] Read more.
The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear magnetic resonance (NMR) spectroscopy (n = 341) and ultra-high performance liquid chromatography – mass spectrometry (UPLC-MS) (n = 297). Distributions of time to event outcomes were estimated by Kaplan-Meier analysis, and baseline characteristics adjusted Cox regression modeling was used to correlate markers’ levels to time to event outcomes. Sixteen polar metabolites were significantly correlated with overall survival (OS) by univariate analysis (p < 0.025). Formate, 2-hydroxybutyrate, glycine and myo-inositol were selected for a multivariate model. The median OS was 6.6 months in the high-risk group compared to 11.4 months in the low risk group HR (Hazard Ratio) = 1.99, 95% C.I. (Confidence Interval) 1.45–2.68; p < 0.0001). Modeling of lipids by class (sphingolipids, acylcarnitines and lysophosphatidylcholines) revealed a median OS = 5.7 months vs. 11. 9 months for the high vs. low risk group. (HR: 2.23, 95% C.I. 1.55–3.20; p < 0.0001). These results demonstrate that metabolic profiles from pre-treatment samples may be useful to stratify clinical outcomes for mNSCLC patients receiving chemotherapy. Genomic and longitudinal measurements pre- and post-treatment may yield addition information to personalize treatment decisions further. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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Review

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22 pages, 3051 KiB  
Review
The Distinctive Serum Metabolomes of Gastric, Esophageal and Colorectal Cancers
by Zhenxing Ren, Cynthia Rajani and Wei Jia
Cancers 2021, 13(4), 720; https://doi.org/10.3390/cancers13040720 - 10 Feb 2021
Cited by 13 | Viewed by 5967
Abstract
Three of the most lethal cancers in the world are the gastrointestinal cancers—gastric (GC), esophageal (EC) and colorectal cancer (CRC)—which are ranked as third, sixth and fourth in cancer deaths globally. Early detection of these cancers is difficult, and a quest is currently [...] Read more.
Three of the most lethal cancers in the world are the gastrointestinal cancers—gastric (GC), esophageal (EC) and colorectal cancer (CRC)—which are ranked as third, sixth and fourth in cancer deaths globally. Early detection of these cancers is difficult, and a quest is currently on to find non-invasive screening tests to detect these cancers. The reprogramming of energy metabolism is a hallmark of cancer, notably, an increased dependence on aerobic glycolysis which is often referred to as the Warburg effect. This metabolic change results in a unique metabolic profile that distinguishes cancer cells from normal cells. Serum metabolomics analyses allow one to measure the end products of both host and microbiota metabolism present at the time of sample collection. It is a non-invasive procedure requiring only blood collection which encourages greater patient compliance to have more frequent screenings for cancer. In the following review we will examine some of the most current serum metabolomics studies in order to compare their results and test a hypothesis that different tumors, notably, from EC, GC and CRC, have distinguishing serum metabolite profiles. Full article
(This article belongs to the Special Issue Insights into Cancer Metabolism from Metabolomics)
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