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癌症研究中的孟德尔随机化:机遇与挑战

Mendelian randomization in cancer research: opportunities and challenges.

作者信息

Tang Mengyao, Chen Lanlan

机构信息

Department of Epidemiology, Columbia University Mailman School of Public Health, 722 W 168Th St., New York, NY, 10032, USA.

Department of Hepatology and Gastroenterology, Charité - Universitätsmedizin Berlin, Campus Virchow-Klinikum and Campus Charité Mitte, 13353, Berlin, Germany.

出版信息

Infect Agent Cancer. 2025 Jun 15;20(1):37. doi: 10.1186/s13027-025-00672-0.

Abstract

Mendelian Randomization (MR) is increasingly used in cancer research to infer causal relationships by leveraging genetic variants as instrumental variables. While the growth of genome-wide association studies and biobank data has expanded the utility of MR, this surge-particularly pronounced in China-raises concerns about methodological rigor. The widespread adoption may be partly driven by the Chinese translation of key MR literature. Recent advances such as multivariable MR, mediation analysis, and integration with AI and omics data have enhanced the robustness and biological interpretability of MR studies. However, challenges persist, including horizontal pleiotropy, weak instrument bias, and misinterpretation of biomarkers as causal exposures. To improve MR study credibility, frameworks like STROBE-MR and MR-GRADE are being adopted. This article reviews methodological improvements and persistent pitfalls in MR, especially within cancer epidemiology, and highlights strategies for ensuring validity in this rapidly evolving field.

摘要

孟德尔随机化(MR)在癌症研究中越来越多地被用于通过利用基因变异作为工具变量来推断因果关系。虽然全基因组关联研究和生物样本库数据的增长扩大了MR的应用范围,但这种激增——在中国尤为明显——引发了对方法严谨性的担忧。这种广泛采用可能部分是由关键MR文献的中文翻译推动的。多变量MR、中介分析以及与人工智能和组学数据整合等最新进展增强了MR研究的稳健性和生物学可解释性。然而,挑战依然存在,包括水平多效性、弱工具偏倚以及将生物标志物误判为因果暴露。为提高MR研究的可信度,正在采用诸如STROBE-MR和MR-GRADE等框架。本文回顾了MR方法的改进和持续存在的陷阱,特别是在癌症流行病学领域,并强调了在这个快速发展的领域确保有效性的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5e2/12168377/4f3d2ceab194/13027_2025_672_Fig1_HTML.jpg

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