孟德尔随机化在胰腺癌研究中的应用现状与展望

Abstract

胰腺癌的发病隐匿性强，治疗困难，早期诊断和治疗存在诸多局限性。本文总结了孟德尔随机化 (MR) 在胰腺癌风险因素探索中的应用进展，特别分析了肠道微生态、生活方式、代谢性疾病等因素的因果关系。通过大规模基因组关联研究 (GWAS) 数据，MR分析揭示了若干与胰腺癌风险相关的生物标志物。双样本MR是当前研究中的常用方法，包括逆方差加权法、加权中位数、MR-Egger法等，有助于从遗传角度解释疾病的因果网络。尽管MR策略可以为理解胰腺癌的病因学提供新视角，但其在数据合成、工具变量选择及多效性评估方面仍需谨慎。新兴分析模型如贝叶斯加权MR (BWMR) 、CAUSE和多变量MR (MVMR) 等，为综合评估多重风险因素及其相互关系带来新的可能。未来，结合上述方法及累积增多的遗传流行病学资料，MR分析预期能够为发现胰腺癌的潜在治疗靶点和制订预防策略提供更为坚实的证据基础。

Alternate abstract:

Pancreatic cancer often has an insidious onset and difficulties in treatment, with various limitations in early diagnosis and treatment. This article reviews the application of Mendelian randomization (MR) in exploring the risk factors for pancreatic cancer, with a special focus on the causal relationships of factors such as gut microbiota, lifestyle, and metabolic diseases. Leveraging data from large-scale genome-wide association studies (GWAS), MR analysis has revealed several biomarkers associated with the risk of pancreatic cancer. The two-sample MR approach is commonly used in current research, including the methods such as Inverse Variance Weighted, Weighted Median, and MR-Egger, which helps to explain the causal network of the disease from a genetic perspective. While MR strategy provides a new perspective for understanding the etiology of pancreatic cancer, caution is still needed in data synthesis, selection of instrumental variables, and pleiotropy assessment. The use of emerging analytical models such as BWMR, CAUSE, and MVMR offers new possibilities for the comprehensive evaluation of multiple risk factors and their interaction. In the future, with the combination of these methods and the ever-increasing genetic epidemiological data, MR analysis is expected to provide more solid evidence for identifying potential therapeutic targets for pancreatic cancer and formulating prevention strategies.