Association study of metabolite-related genetic variants and colorectal cancer risk

HE Chunyi; LI Yanmin; MA Qianying; CAI Yimin; ZHU Ying; TIAN Jianbo; MIAO Xiaoping

doi:10.16462/j.cnki.zhjbkz.2025.07.001

Volume 29 Issue 7

Jul. 2025

Turn off MathJax

Article Contents

Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2025 > 29(7): 745-749

HE Chunyi, LI Yanmin, MA Qianying, CAI Yimin, ZHU Ying, TIAN Jianbo, MIAO Xiaoping. Association study of metabolite-related genetic variants and colorectal cancer risk[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(7): 745-749. doi: 10.16462/j.cnki.zhjbkz.2025.07.001

Citation:

HE Chunyi, LI Yanmin, MA Qianying, CAI Yimin, ZHU Ying, TIAN Jianbo, MIAO Xiaoping. Association study of metabolite-related genetic variants and colorectal cancer risk[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(7): 745-749. doi: 10.16462/j.cnki.zhjbkz.2025.07.001

Citation:

PDF( 1298 KB)

Association study of metabolite-related genetic variants and colorectal cancer risk

doi: 10.16462/j.cnki.zhjbkz.2025.07.001

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan 430071, China

Funds:

National Key Research and Development Plan 2024YFC3405804

National Natural Science Foundation of China 82130098

More Information

Corresponding author: MIAO Xiaoping, E-mail: xpmiao@whu.edu.cn
Received Date: 2025-01-20
Rev Recd Date: 2025-04-13

Available Online: 2025-08-11

Publish Date: 2025-07-10

Abstract

Abstract

Objective To identify genetic variants that influence the levels of metabolites in colorectal cancer (CRC), providing a theoretical basis for the prevention and diagnosis of CRC. Methods A linear regression model was used to systematically identify genetic variants significantly associated with plasma metabolite levels in a large sample of European CRC patients, defining these variants as CRC plasma metabolism quantitative trait loci (metQTLs). Subsequently, enrichment analysis was used to assess the contribution of metQTLs to the genetic risk of CRC. Polygenic risk score (PRS) model was constructed to evaluate the application value of metQTLs in identifying high-risk populations for CRC. Finally, the association between metQTLs and CRC susceptibility was verified in an independent case-control study among Chinese individuals. Results A total of 1 687 metQTL associations were identified (all false discovery rate < 0.05) among 1 198 CRC patients, and CRC metQTLs were significantly enriched in CRC susceptibility regions identified by previous genome-wide association studies (OR=1.95, 95% CI: 1.25-3.06, P=0.002). In PRS model, the risk of CRC was 1.53 times higher in the high score group compared to the low score group (HR=1.53, 95% CI: 1.43-1.63, P < 0.001). Finally, validation in the Chinese population revealed that the rs174574 A>C increased the risk of CRC in both additive (OR=1.66, 95% CI: 1.47-1.90), dominant (OR=1.28, 95% CI: 1.17-1.41), and recessive models (OR=1.57, 95% CI: 1.38-1.77). Conclusions Genetic variants affecting metabolite levels are associated with CRC susceptibility, and may be applied to stratify CRC risk and to identify high-risk populations.
- Colorectal cancer,
- Genetic variant,
- Metabolomics,
- Polygenic risk score

FullText(HTML)

References(24)

References

[1]	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]	Spaander MCW, Zauber AG, Syngal S, et al. Young-onset colorectal cancer[J]. Nat Rev Dis Primers, 2023, 9: 21. DOI: 10.1038/s41572-023-00432-7.
[3]	Chen ZS, Guo XY, Tao R, et al. Fine-mapping analysis including over 254, 000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes[J]. Nat Commun, 2024, 15(1): 3557. DOI: 10.1038/s41467-024-47399-x.
[4]	Consortium G. The GTEx Consortium atlas of genetic regulatory effects across human tissues[J]. Science, 2020, 369(6509): 1318-1330. DOI: 10.1126/science.aaz1776.
[5]	Zhang M, Chen C, Lu ZQ, et al. Genetic control of alternative splicing and its distinct role in colorectal cancer mechanisms[J]. Gastroenterology, 2023, 165(5): 1151-1167. DOI: 10.1053/j.gastro.2023.07.019.
[6]	Chen KX, Nan JH, Xiong XS. Genetic regulation of m6A RNA methylation and its contribution in human complex diseases[J]. Sci China Life Sci, 2024, 67(8): 1591-1600. DOI: 10.1007/s11427-024-2609-8.
[7]	Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward[J]. Nat Rev Cancer, 2021, 21(10): 669-680. DOI: 10.1038/s41568-021-00378-6.
[8]	Dey P, Kimmelman AC, DePinho RA. Metabolic codependencies in the tumor microenvironment[J]. Cancer Discov, 2021, 11(5): 1067-1081. DOI: 10.1158/2159-8290.CD-20-1211.
[9]	Zhang JP, Zou SM, Fang LK. Metabolic reprogramming in colorectal cancer: regulatory networks and therapy[J]. Cell Biosci, 2023, 13(1): 25. DOI: 10.1186/s13578-023-00977-w.
[10]	Wong CC, Xu JY, Bian XQ, et al. In colorectal cancer cells with mutant KRAS, SLC25A22-mediated glutaminolysis reduces DNA demethylation to increase WNT signaling, stemness, and drug resistance[J]. Gastroenterology, 2020, 159(6): 2163-2180. e6. DOI: 10.1053/j.gastro.2020.08.016.
[11]	Jing ZL, Liu QM, He XY, et al. NCAPD3 enhances Warburg effect through c-myc and E2F1 and promotes the occurrence and progression of colorectal cancer[J]. J Exp Clin Cancer Res CR, 2022, 41(1): 198. DOI: 10.1186/s13046-022-02412-3.
[12]	Najumudeen AK, Ceteci F, Fey SK, et al. The amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer[J]. Nat Genet, 2021, 53(1): 16-26. DOI: 10.1038/s41588-020-00753-3.
[13]	Ma QL, Zhang WY, Wu KM, et al. The roles of KRAS in cancer metabolism, tumor microenvironment and clinical therapy[J]. Mol Cancer, 2025, 24(1): 14. DOI: 10.1186/s12943-024-02218-1.
[14]	Allen NE, Lacey B, Lawlor DA, et al. Prospective study design and data analysis in UK Biobank[J]. Sci Transl Med, 2024, 16(729): eadf4428. DOI: 10.1126/scitranslmed.adf4428.
[15]	Hu CC, Fan Y, Lin ZF, et al. Metabolomic landscape of overall and common cancers in the UK Biobank: a prospective cohort study[J]. Int J Cancer, 2024, 155(1): 27-39. DOI: 10.1002/ijc.34884.
[16]	Sun BB, Chiou J, Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank[J]. Nature, 2023, 622(7982): 329-338. DOI: 10.1038/s41586-023-06592-6.
[17]	Fernandez-Rozadilla C, Timofeeva M, Chen ZS, et al. Deciphering colorectal cancer genetics through multi-omic analysis of 100, 204 cases and 154, 587 controls of European and East Asian ancestries[J]. Nat Genet, 2023, 55(1): 89-99. DOI: 10.1038/s41588-022-01222-9.
[18]	Choi SW, Mak TS, O'Reilly PF. Tutorial: a guide to performing polygenic risk score analyses[J]. Nat Protoc, 2020, 15(9): 2759-2772. DOI: 10.1038/s41596-020-0353-1.
[19]	Cai YM, Lu ZQ, Chen C, et al. An atlas of genetic effects on cellular composition of the tumor microenvironment[J]. Nat Immunol, 2024, 25(10): 1959-1975. DOI: 10.1038/s41590-024-01945-3.
[20]	Sedlak JC, Yilmaz ÖH, Roper J. Metabolism and colorectal cancer[J]. Annu Rev Pathol, 2023, 18: 467-492. DOI: 10.1146/annurev-pathmechdis-031521-041113.
[21]	Karjalainen MK, Karthikeyan S, Oliver-Williams C, et al. Genome-wide characterization of circulating metabolic biomarkers[J]. Nature, 2024, 628(8006): 130-138. DOI: 10.1038/s41586-024-07148-y.
[22]	Tahir UA, Katz DH, Avila-Pachecho J, et al. Whole genome association study of the plasma metabolome identifies metabolites linked to cardiometabolic disease in black individuals[J]. Nat Commun, 2022, 13(1): 4923. DOI: 10.1038/s41467-022-32275-3.
[23]	Xin JY, Du ML, Gu DY, et al. Risk assessment for colorectal cancer via polygenic risk score and lifestyle exposure: a large-scale association study of East Asian and European populations[J]. Genome Med, 2023, 15(1): 4. DOI: 10.1186/s13073-023-01156-9.
[24]	Xu CJ, Gu L, Hu LP, et al. FADS1-arachidonic acid axis enhances arachidonic acid metabolism by altering intestinal microecology in colorectal cancer[J]. Nat Commun, 2023, 14(1): 2042. DOI: 10.1038/s41467-023-37590-x.