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Background <p>Prostate cancer (PCa) remains the most prevalent cancer among male globally. Despite the critical role of genetic factors in PCa pathogenesis, recent advances in metabolomics have highlighted the significant contributions of circulating metabolites to genetic risk profiles for PCa. However, the causal relationship between metabolites and PCa is not yet unclear.</p> Methods <p>We utilized a bidirectional two-sample Mendelian randomization (MR) approach, analyzing metabolite datasets from the Canadian Longitudinal Study of Aging (CLSA), the Cooperative Health Research in the Region of Augsburg (KORA) study, and the TwinsUK study and PCa dataset from the Oncoarray. Replication analyses were performed with the UK Biobank. Instrumental variables (IVs) were selected based on established MR criteria and analyzed using methods including the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out (LOO) methods.</p> Results <p>We identified causal relationships between circulating metabolites and PCa risk. After removing high influential SNPs and outliers and reanalysis, we obtained the levels of N6-carbamoylthreonyladenosine (OR 0.61, 95% CI 0.37–1.01, <i>p</i> = 0.054) and 4-ethylphenylsulfate (OR 0.66, 95% CI 0.47–0.92, <i>p</i> = 0.015) causally associated with PCa. All results passed FDR correction; 4-ethylphenylsulfate also remained significant after Bonferroni adjustment. Reverse MR analysis highlighted robust causal relationships of PCa to homovanillate (OR 1.07, 95% CI 1.03–1.10, <i>p</i> = 5.49 × 10 − 5) and X-12,627 (OR 1.03, 95% CI 1.01–1.04, <i>p</i> = 7.54 × 10<sup>−5</sup>) levels.</p> Conclusion <p>These insights underscore the etiology and risk factors of PCa, providing genetic evidence for the development of therapeutic targets and contributing to elucidating disease mechanisms, suggesting potential diagnostic biomarkers.</p>

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Associations between circulating metabolites and pca: a bidirectional two-sample Mendelian randomization study

  • 期刊论文

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  • 发表日期：

    2025年7月18日

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  • Jing Lv,
  • Yonghong Deng

摘要

Background

Prostate cancer (PCa) remains the most prevalent cancer among male globally. Despite the critical role of genetic factors in PCa pathogenesis, recent advances in metabolomics have highlighted the significant contributions of circulating metabolites to genetic risk profiles for PCa. However, the causal relationship between metabolites and PCa is not yet unclear.

Methods

We utilized a bidirectional two-sample Mendelian randomization (MR) approach, analyzing metabolite datasets from the Canadian Longitudinal Study of Aging (CLSA), the Cooperative Health Research in the Region of Augsburg (KORA) study, and the TwinsUK study and PCa dataset from the Oncoarray. Replication analyses were performed with the UK Biobank. Instrumental variables (IVs) were selected based on established MR criteria and analyzed using methods including the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out (LOO) methods.

Results

We identified causal relationships between circulating metabolites and PCa risk. After removing high influential SNPs and outliers and reanalysis, we obtained the levels of N6-carbamoylthreonyladenosine (OR 0.61, 95% CI 0.37–1.01, p = 0.054) and 4-ethylphenylsulfate (OR 0.66, 95% CI 0.47–0.92, p = 0.015) causally associated with PCa. All results passed FDR correction; 4-ethylphenylsulfate also remained significant after Bonferroni adjustment. Reverse MR analysis highlighted robust causal relationships of PCa to homovanillate (OR 1.07, 95% CI 1.03–1.10, p = 5.49 × 10 − 5) and X-12,627 (OR 1.03, 95% CI 1.01–1.04, p = 7.54 × 10−5) levels.

Conclusion

These insights underscore the etiology and risk factors of PCa, providing genetic evidence for the development of therapeutic targets and contributing to elucidating disease mechanisms, suggesting potential diagnostic biomarkers.