Dataset Information

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk.

ABSTRACT:

Background

The etiology of colorectal cancer is not fully understood.

Methods

Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.

Results

Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.

Conclusions

This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.

Impact

The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

SUBMITTER: Shu X

PROVIDER: S-EPMC9354799 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk.

Shu Xiang X Chen Zhishan Z Long Jirong J Guo Xingyi X Yang Yaohua Y Qu Conghui C Ahn Yoon-Ok YO Cai Qiuyin Q Casey Graham G Gruber Stephen B SB Huyghe Jeroen R JR Jee Sun Ha SH Jenkins Mark A MA Jia Wei-Hua WH Jung Keum Ji KJ Kamatani Yoichiro Y Kim Dong-Hyun DH Kim Jeongseon J Kweon Sun-Seog SS Le Marchand Loic L Matsuda Koichi K Matsuo Keitaro K Newcomb Polly A PA Oh Jae Hwan JH Ose Jennifer J Oze Isao I Pai Rish K RK Pan Zhi-Zhong ZZ Pharoah Paul D P PDP Playdon Mary C MC Ren Ze-Fang ZF Schoen Robert E RE Shin Aesun A Shin Min-Ho MH Shu Xiao-Ou XO Sun Xiaohui X Tangen Catherine M CM Tanikawa Chizu C Ulrich Cornelia M CM van Duijnhoven Franzel J B FJB Van Guelpen Bethany B Wolk Alicja A Woods Michael O MO Wu Anna H AH Peters Ulrike U Zheng Wei W

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20220601 6

<h4>Background</h4>The etiology of colorectal cancer is not fully understood.<h4>Methods</h4>Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximatel ...[more]

PMID: 35266989

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Dataset Information

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk.

Background

Methods

Results

Conclusions

Impact

Publications

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk.

Similar Datasets

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