Discovery and Validation of Novel Biomarkers for Colorectal Neoplasia Detection via Plasma Metabolomics

Abstract

Metabolic disturbance plays a critical role in the initiation of colorectal cancer (CRC), yet the identification of metabolites that are useful for early detection of CRC and its precursor lesions remains elusive. We conducted an untargeted plasma metabolomic profiling by liquid chromatography-mass spectrometry in a two-stage case–control study, including 219 CRC cases, 164 colorectal adenoma (CRA) cases, and 219 normal controls (NC) as a training set, and 91 CRC, 115 CRA, and 109 NC as a validation set. Among 891 named metabolites, 239 were significantly altered in CRC versus NC, 26 in CRA versus NC, and 88 in CRC versus CRA within the training set. The results were stable when adjusting for potential confounders. A panel of 10 metabolites, including six lipid species, one benzenoid, one organoheterocyclic compound, one organic acid derivative, and one organic oxygen compound, showed optimal performance in discriminating CRC from NC (AUC = 0.81) in the validation. Moreover, a panel of seven metabolites exhibited optimal performance in discriminating CRA from NC, with an AUC of 0.89. Our findings provide novel evidence supporting specific plasma metabolites, particularly those implicated in lipid metabolism, as promising biomarkers for the early detection of CRC.

Keywords

biomarkers / colorectal adenoma / colorectal cancer / early diagnosis / metabolomics

References

[1] F. Bray, M. Laversanne, H. Sung, et al., “Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 74 (2024): 229-263.

[2] B. Shao, M. Zhu, K. Shen, et al., “Disease Burden of Total and Early-Onset Colorectal Cancer in China From 1990 to 2019 and Predictions of Cancer Incidence and Mortality,” Clinical Epidemiology 15 (2023): 151-163.

[3] J. F. Shi, L. Wang, J. C. Ran, et al., “Clinical Characteristics, Medical Service Utilization, and Expenditure for Colorectal Cancer in China, 2005 to 2014: Overall Design and Results From a Multicenter Retrospective Epidemiologic Survey,” Cancer 127 (2021): 1880-1893.

[4] M. F. Kaminski, D. J. Robertson, C. Senore, and D. K. Rex, “Optimizing the Quality of Colorectal Cancer Screening Worldwide,” Gastroenterology 158 (2020): 404-417.

[5] H. Chen, N. Li, J. Ren, et al., “Participation and Yield of a Population-Based Colorectal Cancer Screening Programme in China,” Gut 68 (2019): 1450-1457.

[6] G. C. Zhong, W. P. Sun, L. Wan, J. J. Hu, and F. B. Hao, “Efficacy and Cost-effectiveness of Fecal Immunochemical Test Versus Colonoscopy in Colorectal Cancer Screening: A Systematic Review and Meta-Analysis,” Gastrointestinal Endoscopy 91 (2020): 684-697. e15.

[7] S. Aniwan, R. Rerknimitr, P. Kongkam, et al., “A Combination of Clinical Risk Stratification and Fecal Immunochemical Test Results to Prioritize Colonoscopy Screening in Asymptomatic Participants,” Gastrointestinal Endoscopy 81 (2015): 719-727.

[8] T. F. Imperiale, R. N. Gruber, T. E. Stump, T. W. Emmett, and P. O. Monahan, “Performance Characteristics of Fecal Immunochemical Tests for Colorectal Cancer and Advanced Adenomatous Polyps: A Systematic Review and Meta-Analysis,” Annals of Internal Medicine 170 (2019): 319-329.

[9] L. M. Helsingen, P. O. Vandvik, H. C. Jodal, et al., “Colorectal Cancer Screening With Faecal Immunochemical Testing, Sigmoidoscopy or Colonoscopy: A Clinical Practice Guideline,” Bmj 367 (2019): l5515.

[10] M. Buskermolen, D. R. Cenin, L. M. Helsingen, et al., “Colorectal Cancer Screening With Faecal Immunochemical Testing, Sigmoidoscopy or Colonoscopy: A Microsimulation Modelling Study,” Bmj 367 (2019): l5383.

[11] Y. Zhou, L. Tao, J. Qiu, et al., “Tumor Biomarkers for Diagnosis, Prognosis and Targeted Therapy,” Signal Transduction and Targeted Therapy 9 (2024): 132.

[12] K. Yamashita and M. Watanabe, “Clinical Significance of Tumor Markers and an Emerging Perspective on Colorectal Cancer,” Cancer Science 100 (2009): 195-199.

[13] L. Rasmussen, H. J. Nielsen, and I. J. Christensen, “Early Detection and Recurrence of Colorectal Adenomas by Combination of Eight Cancer-Associated Biomarkers in Plasma,” Clinical and Experimental Gastroenterology 13 (2020): 273-284.

[14] M. Wilhelmsen, I. J. Christensen, L. Rasmussen, et al., “Detection of Colorectal Neoplasia: Combination of Eight Blood-based, Cancer-Associated Protein Biomarkers,” International Journal of Cancer 140 (2017): 1436-1446.

[15] D. Hanahan, “Hallmarks of Cancer: New Dimensions,” Cancer Discovery 12 (2022): 31-46.

[16] D. S. Wishart, “Emerging Applications of Metabolomics in Drug Discovery and Precision Medicine,” Nat Rev Drug Discovery 15 (2016): 473-484.

[17] J. Li, J. Li, H. Wang, L. W. Qi, Y. Zhu, and M. Lai, “Tyrosine and Glutamine-Leucine Are Metabolic Markers of Early-Stage Colorectal Cancers,” Gastroenterology 157 (2019): 257-259.

[18] F. Chen, X. Dai, C. C. Zhou, et al., “Integrated Analysis of the Faecal Metagenome and Serum Metabolome Reveals the Role of Gut Microbiome-Associated Metabolites in the Detection of Colorectal Cancer and Adenoma,” Gut 71 (2022): 1315-1325.

[19] O. O. Coker, C. Liu, W. K. K. Wu, et al., “Altered Gut Metabolites and Microbiota Interactions Are Implicated in Colorectal Carcinogenesis and Can be Non-Invasive Diagnostic Biomarkers,” Microbiome 10 (2022): 35.

[20] M. Wang, Z. Long, W. Xue, et al., “Discovery of Plasma Biomarkers for Colorectal Cancer Diagnosis via Untargeted and Targeted Quantitative Metabolomics,” Clinical and Translational Medicine 12 (2022): e805.

[21] T. F. Imperiale, D. F. Ransohoff, S. H. Itzkowitz, et al., “Multitarget Stool DNA Testing for Colorectal-Cancer Screening,” New England Journal of Medicine 370 (2014): 1287-1297.

[22] J. Tevini, S. K. Eder, U. Huber-Schonauer, et al., “Changing Metabolic Patterns Along the Colorectal Adenoma-Carcinoma Sequence,” Journal of Clinical Medicine 11 (2022): 721.

[23] R. Riscal, N. Skuli, and M. C. Simon, “Even Cancer Cells Watch Their Cholesterol!,” Molecular Cell 76 (2019): 220-231.

[24] S. Shen, L. Yang, L. Li, Y. Bai, C. Cai, and H. Liu, “A Plasma Lipidomics Strategy Reveals Perturbed Lipid Metabolic Pathways and Potential Lipid Biomarkers of Human Colorectal Cancer,” Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences 1068-1069 (2017): 41-48.

[25] A. Geijsen, S. Brezina, P. Keski-Rahkonen, et al., “Plasma Metabolites Associated With Colorectal Cancer: A Discovery-Replication Strategy,” International Journal of Cancer 145 (2019): 1221-1231.

[26] X. Shu, Y. B. Xiang, N. Rothman, et al., “Prospective Study of Blood Metabolites Associated With Colorectal Cancer Risk,” International Journal of Cancer 143 (2018): 527-534.

[27] H. Zhou, Y. Nong, Y. Zhu, et al., “Serum Untargeted Lipidomics by UHPLC-ESI-HRMS Aids the Biomarker Discovery of Colorectal Adenoma,” BMC Cancer 22 (2022): 314.

[28] S. Li, B. Guo, J. Song, et al., “Plasma Choline-Containing Phospholipids: Potential Biomarkers for Colorectal Cancer Progression,” Metabolomics 9 (2013): 202-212.

[29] K. Uchiyama, N. Yagi, K. Mizushima, et al., “Serum Metabolomics Analysis for Early Detection of Colorectal Cancer,” Journal of Gastroenterology 52 (2017): 677-694.

[30] R. Acquaviva, B. Tomasello, C. Di Giacomo, et al., “Protocatechuic Acid, a Simple Plant Secondary Metabolite, Induced Apoptosis by Promoting Oxidative Stress Through HO-1 Downregulation and p21 Upregulation in Colon Cancer Cells,” Biomolecules 11 (2021): 1485.

[31] Y. S. Kurniawan, K. T. A. Priyangga, Jumina, et al., “An Update on the Anticancer Activity of Xanthone Derivatives: A Review,” Pharmaceuticals (Basel) 14 (2021): 1144.

[32] J. Liu, W. Geng, H. Sun, et al., “Integrative Metabolomic Characterisation Identifies Altered Portal Vein Serum Metabolome Contributing to human Hepatocellular Carcinoma,” Gut 71 (2022): 1203-1213.

[33] M. C. Chambers, B. Maclean, R. Burke, et al., “A Cross-Platform Toolkit for Mass Spectrometry and Proteomics,” Nature Biotechnology 30 (2012): 918-920.

[34] C. A. Smith, E. J. Want, G. O'Maille, R. Abagyan, and G. Siuzdak, “XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification,” Analytical Chemistry 78 (2006): 779-787.

[35] Z. Pang, J. Chong, G. Zhou, et al., “MetaboAnalyst 5.0: Narrowing the Gap Between Raw Spectra and Functional Insights,” Nucleic Acids Research 49 (2021): W388-W396.

[36] D. Das, X. Wang, Y. C. Chiu, et al., “Integrative Multi-Omics Characterization of Hepatocellular Carcinoma in Hispanic Patients,” JNCI: Journal of the National Cancer Institute 116 (2024): 1961-1978.

[37] T. Robert, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society Series B (Methodological) 58 (1996): 267-288.

[38] M. B. Kursa and W. R. Rudnicki, “Feature Selection With the Boruta Package,” Journal of Statistical Software 36 (2010): 1-13.

RIGHTS & PERMISSIONS

2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.