孟德尔随机化在胰腺癌研究中的应用现状与展望

杜凯豪; 侯立朝; 罗兰明慧; 东小鸽; 蒋威; 王展

doi:10.12449/JCH241033

孟德尔随机化在胰腺癌研究中的应用现状与展望

DOI: 10.12449/JCH241033

杜凯豪¹,
侯立朝^2a,
罗兰明慧¹,
东小鸽¹,
蒋威¹,
王展^2b, , ,

1.
青海大学临床医学院，西宁 810000
2a.
青海大学附属医院普通外科，西宁 810000
2b.
青海大学附属医院医工结合与转化应用部，西宁 810000

基金项目:

国家自然科学基金 (82160131);

青海省科技厅项目 (2021-ZJ-963Q)

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：杜凯豪负责课题设计，论文的撰写与修改；罗兰明慧、东小鸽、蒋威参与文献搜集，修改论文；王展、侯立朝负责拟定写作思路，指导撰写文章，并最后定稿。

详细信息

通信作者:
王展， ufofu01@163.com （ORCID： 0000-0002-4225-8136）

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出版历程
- 收稿日期: 2024-01-21
- 录用日期: 2024-04-19
- 出版日期: 2024-10-25

Current status and prospects of the application of Mendelian randomization in pancreatic cancer research

1.
Clinical Medical College，Qinghai University，Xining 810000，China
2a.
Department of General Surgery，The Affiliated Hospital of Qinghai University，Xining 810000，China
2b.
Department of Medical Engineering Integration and Translational Application，The Affiliated Hospital of Qinghai University，Xining 810000，China

Research funding:

National Natural Science Foundation of China (82160131);

Qinghai Provincial Department of Science and Technology Project (2021-ZJ-963Q)

More Information

Corresponding author: WANG Zhan， ufofu01@163.com （ORCID： 0000-0002-4225-8136）

摘要

摘要: 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。
- 胰腺肿瘤 /
- 孟德尔随机化分析 /
- 因果律
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.
- Pancreatic Neoplasms /
- Mendelian Randomization Analysis /
- Causality

HTML全文

图 1 两样本MR模型

Figure 1. Model of the two-samples MR analysis

下载: 全尺寸图片幻灯片

注： β5为中介效应值，计算公式：β1×β4；中介效应百分比：（β1×β4）/β3。

图 2 因果路径图

Figure 2. Causal path diagram

下载: 全尺寸图片幻灯片

表 1 肠道菌群MR

Table 1. Gut microbiota MR

GM	OR	95%CI	P值
研究1^［25］
Senegalimassilia	0.635	0.406～0.998	0.049
Odoribacter	1.899	1.157～3.116	0.011
Ruminiclostridium 9	1.976	1.128～3.461	0.017
Ruminococcaceae（UCG011）	1.433	1.072～1.916	0.015
Streptococcus	1.712	1.071～1.736	0.025
研究2^［26］
Lentisphaerae	0.726	0.553～0.953	0.021
Lentisphaeria	0.697	0.521～0.932	0.015
Victivallales	0.697	0.521～0.932	0.015
Lachnospiraceae UCG004	0.655	0.434～0.988	0.043
Erysipelotrichia	1.616	1.025～2.547	0.039
Erysipelotrichales	1.616	1.025～2.547	0.039
Erysipelotrichaceae	1.616	1.025～2.547	0.039
Flavonifractor	1.857	1.120～3.079	0.016
Terrisporobacter	1.671	1.061～2.632	0.027

下载: 导出CSV

表 2 代谢MR

Table 2. Metabolic MR

参考文献	暴露	结局	OR	95%CI	P值	β值
Shen等^［58］	2型糖尿病	胰腺癌	1.240	1.090～1.140	0.001
Li等^［65］	CRP		1.348	1.004～1.809	<0.05	β2=0.3
	控制BMI后CRP		1.441	1.064～1.950	<0.05	β4=0.37
	控制CRP后BMI		1.341	0.884～2.037	>0.05	β5=0.29
	BMI		1.484	1.021～2.157	<0.05	β3=0.39
	BMI	CRP	1.393	1.320～1.469	<0.05	β1=0.32

下载: 导出CSV

表 3 炎症、免疫因素MR

Table 3. Inflammatory and immune factors MR

暴露	结局	方法	OR	95%CI	P值
溃疡性结肠炎	胰腺癌	IVW	0.946	0.830～1.079	0.409
		WM	0.910	0.753～1.101	0.332
		MR-Egger	0.951	0.648～1.397	0.799
克罗恩病	胰腺癌	IVW	1.111	1.015～1.213	0.022
		WM	1.120	0.970～1.292	0.119
		MR-Egger	1.338	1.064～1.683	0.015
IL-1RA	急性胰腺炎	IVW	0.87	0.77～0.97	0.003
	慢性胰腺炎		0.73	0.65～0.82	2.93×10^-8
	胰腺癌		0.86	0.77～0.96	0.009

下载: 导出CSV

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