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Systematic investigation of genetically determined plasma and urinary metabolites to discover potential interventional targets for colorectal cancer.
Sun, Jing; Zhao, Jianhui; Zhou, Siyun; Li, Xinxuan; Li, Tengfei; Wang, Lijuan; Yuan, Shuai; Chen, Dong; Law, Philip J; Larsson, Susanna C; Farrington, Susan M; Houlston, Richard S; Dunlop, Malcolm G; Theodoratou, Evropi; Li, Xue.
Afiliação
  • Sun J; Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Zhao J; Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Zhou S; Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Li X; Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Li T; Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Wang L; Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
  • Yuan S; Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
  • Chen D; Department of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Law PJ; Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
  • Larsson SC; Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
  • Farrington SM; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
  • Houlston RS; Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
  • Dunlop MG; Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
  • Theodoratou E; Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
  • Li X; Colon Cancer Genetics Group, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
J Natl Cancer Inst ; 116(8): 1303-1312, 2024 Aug 01.
Article em En | MEDLINE | ID: mdl-38648753
ABSTRACT

BACKGROUND:

We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC.

METHODS:

Metabolite quantitative trait loci were derived from 2 published metabolomics genome-wide association studies, and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale genome-wide association study meta-analysis (100 204 cases, 154 587 controls) and the FinnGen cohort (4957 cases, 304 197 controls). Mendelian randomization and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable Mendelian randomization and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC.

RESULTS:

The study identified 30 plasma metabolites and 4 urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, olipudase alfa, tilactase). Thirteen modifiable risk factors were associated with 9 metabolites, and 8 of these modifiable risk factors were associated with CRC risk. These 9 metabolites mediated the effect of modifiable risk factors (Actinobacteria, body mass index, waist to hip ratio, fasting insulin, smoking initiation) on CRC.

CONCLUSION:

This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Locos de Características Quantitativas / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Female / Humans / Male Idioma: En Revista: J Natl Cancer Inst Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Locos de Características Quantitativas / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Female / Humans / Male Idioma: En Revista: J Natl Cancer Inst Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China