基于全血浆蛋白质组的孟德尔随机化方法探究胆囊癌的风险标志物

doi:10.3969/j.issn.1674-8115.2026.07.016

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (7): 972-980.doi: 10.3969/j.issn.1674-8115.2026.07.016

• 论著 · 循证医学 • 上一篇

基于全血浆蛋白质组的孟德尔随机化方法探究胆囊癌的风险标志物

汤秋义¹^,², 董文君¹^,², 胡春楠¹^,², 龚伟¹^,²()

^1.上海交通大学医学院附属新华医院普外科，上海 200092
^2.上海市胆道疾病研究重点实验室，上海 200092

收稿日期:2025-11-04 接受日期:2026-03-20 出版日期:2026-07-28 发布日期:2026-07-28
通讯作者: 龚　伟，主任医师，博士；电子信箱：gongwei@xinhuamed.com.cn。
基金资助:
国家自然科学基金(82473043);上海市“科技创新行动计划”医学创新研究专项项目(23Y11905600);上海交通大学医学院“双百人”项目(20151001)

Identification of risk biomarkers of gallbladder cancer through plasma proteome-wide Mendelian randomization analysis

Tang Qiuyi¹^,², Dong Wenjun¹^,², Hu Chunnan¹^,², Gong Wei¹^,²()

^1.Department of Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
^2.Shanghai Key Laboratory of Biliary Tract Disease Research, Shanghai 200092, China

Received:2025-11-04 Accepted:2026-03-20 Online:2026-07-28 Published:2026-07-28
Contact: Gong Wei, E-mail: gongwei@xinhuamed.com.cn.
Supported by:
National Natural Science Foundation of China(82473043);Shanghai “Science and Technology Innovation Action Plan” Special Project for Medical Innovation Research(23Y11905600);“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20151001)

摘要/Abstract

摘要：

目的·通过全血浆蛋白质组的孟德尔随机化分析，系统评估血浆蛋白质水平与胆囊癌发生风险之间的潜在因果关联。方法·利用日本、韩国、芬兰和英国的胆囊癌全基因组关联研究（genome-wide association study，GWAS）汇总数据进行多族裔荟萃分析，获得胆囊癌的遗传效应统计数据。将deCODE和UKBPPP 2项大规模血浆蛋白质组学研究的GWAS汇总数据作为暴露变量，采用广义孟德尔随机化（generalized summary-data-based Mendelian randomization，GSMR）方法分析血浆蛋白质水平与胆囊癌风险的因果关联，并通过两样本孟德尔随机化（two-sample Mendelian randomization，TSMR）方法对显著关联结果进行验证。同时，对所发现的与胆囊癌风险显著关联的血浆蛋白质，采用共定位分析进行验证。结果·GSMR分析结果显示，deCODE研究的血浆蛋白质中共鉴定出56种与胆囊癌风险存在显著因果关联（P_adj<0.001），其中31种蛋白在UKBPPP研究数据中得到重复验证。TSMR进一步证实其中2种蛋白质与胆囊癌风险存在稳健的因果关联。这2种蛋白中，血浆补体受体2（complement receptor 2，CR2）水平升高与胆囊癌风险降低显著相关（OR=0.258，95%CI 0.071~0.935，P=0.039），而血浆神经细胞黏附分子1（neural cell adhesion molecule 1，NCAM1）水平升高与胆囊癌风险升高显著相关（OR=2.306，95%CI 1.432~3.713，P<0.001）。贝叶斯共定位分析显示CR2和NCAM1蛋白数量性状相关突变与胆囊癌风险突变存在较强共定位。结论·在全血浆蛋白质组中鉴定出CR2和NCAM1 2种胆囊癌的潜在风险相关生物标志物。CR2水平升高可能降低胆囊癌风险；而NCAM1水平升高可能增加胆囊癌风险。

关键词: 胆囊癌, 孟德尔随机化分析, 蛋白质组学, 生物标志物, 补体受体2, 神经细胞黏附分子1

Abstract:

Objective ·To systematically evaluate the potential causal associations between plasma protein levels and the risk of gallbladder cancer (GBC) through proteome-wide Mendelian randomization analysis. Methods ·GBC genome-wide association study (GWAS) summary data from Japan, Republic of Korea, Finland, and the UK were used for a multi-ethnic meta-analysis to obtain genetic effect estimates for GBC. Summary-level GWAS data of plasma proteomes from two large-scale proteomic studies (deCODE and UKBPPP) were used as exposure variables. A generalized summary-data-based Mendelian randomization (GSMR) analysis was performed to assess the causal associations between each plasma protein level and GBC risk, and significant associations were validated by two-sample Mendelian randomization (TSMR). Significant associations discovered between plasma proteins and GBC risk were also validated using colocalization analysis. Results ·GSMR analysis identified 56 plasma proteins significantly causally associated with GBC risk in the deCODE cohort (P_adj<0.001), of which 31 proteins were replicated in another independent proteomic cohort (UKBPPP). TSMR further confirmed two proteins with robust causal associations with GBC. Among these two proteins, higher plasma complement receptor 2 (CR2) levels were significantly associated with a reduced risk of GBC (OR=0.258, 95%CI 0.071‒0.935, P=0.039), whereas higher plasma neural cell adhesion molecule 1 (NCAM1) levels were associated with an increased risk of GBC (OR=2.306, 95%CI 1.432‒3.713, P<0.001). The Bayesian colocalization analysis showed strong colocalization between protein quantitative trait loci (pQTL) variants of CR2 and NCAM1 and GBC risk variants. Conclusion ·Two potential risk-related biomarkers for GBC, CR2, and NCAM1, were identified in the whole plasma proteome. The elevated CR2 level is linked to a lower risk of GBC, while the elevated NCAM1 level corresponds to a higher risk.

Key words: gallbladder cancer, Mendelian randomization analysis, proteomics, biomarker, complement receptor 2 (CR2), neural cell adhesion molecule 1 (NCAM1)

中图分类号:

R735.8

汤秋义, 董文君, 胡春楠, 龚伟. 基于全血浆蛋白质组的孟德尔随机化方法探究胆囊癌的风险标志物[J]. 上海交通大学学报（医学版）, 2026, 46(7): 972-980.

Tang Qiuyi, Dong Wenjun, Hu Chunnan, Gong Wei. Identification of risk biomarkers of gallbladder cancer through plasma proteome-wide Mendelian randomization analysis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(7): 972-980.

图/表 7

图1 研究设计示意图Note： A. Basic assumptions of MR analysis. ① The instrumental variables should be robustly associated with exposure factors. ② The instrumental variable should not be associated with potential confounders. ③ The instrumental variables should affect the risk of outcome merely through the exposure factors, rather than through alternative pathways. B. The process of analysis. GBC—gallbladder cancer.

Fig 1 Study design schematic diagram

表1 胆囊癌GWAS汇总数据来源

Tab 1 Sources of GWAS summary data for gallbladder cancer

Phenotype	Region	Population	Case/n	Sample size/n	DOI
GBC	Japan	East Asia	41	907	10.1038/jhg.2012.9
GBC	Republic of Korea	East Asia	12	72 298	10.1016/j.xgen.2022.100189
GBC	Finland	Europe	84	259 667	10.1038/s41586-022-05473-8
GBC	The UK	Europe	289	400 583	10.5281/zenodo.8011558

图2 胆囊癌GWAS荟萃分析后的曼哈顿图和Q-Q图

Fig 2 Manhattan plot and the Q-Q plot of GWAS meta-analysis of gallbladder cancer

表2 经TSMR验证与胆囊癌存在因果关联的血浆蛋白（IVW法）

Tab 2 Plasma proteins validated to be causally associated with gallbladder cancer by TSMR （IVW method ）

Gene	β	SE	P value	OR(95%CI)	SNP/n
CR2	-1.356	0.657	0.039	0.258 (0.071‒0.935)	4
NCAM1	0.836	0.243	<0.001	2.306 (1.432‒3.713)	4

表3 以英国生物银行数据为胆囊癌结局的血浆CR2和NCAM1蛋白水平TSMR分析结果

Tab 3 TSMR analysis of plasma CR2 and NCAM1 protein levels with gallbladder cancer risk using UK Biobank data

Analysis	Exposure	Source	Outcome	Estimate β	SE	OR(95%CI)	P value
TSMR	CR2	deCODE	GBC.UKB	-0.304	0.254	0.738 (0.449‒1.214)	0.231
TSMR	CR2	UKBPPP	GBC.UKB	-0.302	0.219	0.739 (0.481‒1.136)	0.168
TSMR	NCAM1	deCODE	GBC.UKB	0.617	0.253	1.853 (1.129‒3.043)	0.015
TSMR	NCAM1	UKBPPP	GBC.UKB	0.459	0.231	1.582 (1.006‒2.489)	0.047

图3 血浆蛋白CR2（A）和NCAM1（B）与胆囊癌的变异共定位Note： pQTL—protein quantitative trait locus.

Fig 3 Colocalization plots of plasma proteins CR2 (A) and NCAM1 (B) on gallbladder cancer

图4 TSMR的敏感性分析（留一法）Note： A. CR2. B. NCAM1.

Fig 4 Sensitivity analysis of TSMR (leave-one-out method)

参考文献 29

[1]	Roa J C, García P, Kapoor V K, et al. Gallbladder cancer[J]. Nat Rev Dis Primers, 2022, 8(1): 69.
[2]	孙旭恒, 王一钧, 张薇, 等. 中国胆囊癌流行病学特征与诊治及预后分析(附6 159例报告)[J]. 中华消化外科杂志, 2022, 21(1): 114-128.
	Sun X H, Wang Y J, Zhang W, et al. Epidemiological characteristics, diagnosis, treatment and prognosis of gallbladder cancer in China: a report of 6 159 cases[J]. Chinese Journal of Digestive Surgery, 2022, 21(1): 114-128.
[3]	Lazcano-Ponce E C, Miquel J F, Muñoz N, et al. Epidemiology and molecular pathology of gallbladder cancer[J]. CA A Cancer J Clin, 2001, 51(6): 349-364.
[4]	Torre L A, Siegel R L, Islami F, et al. Worldwide burden of and trends in mortality from gallbladder and other biliary tract cancers[J]. Clin Gastroenterol Hepatol, 2018, 16(3): 427-437.
[5]	Sekula P, Del Greco M F, Pattaro C, et al. Mendelian randomization as an approach to assess causality using observational data[J]. J Am Soc Nephrol, 2016, 27(11): 3253-3265.
[6]	Deutsch E W, Omenn G S, Sun Z, et al. Advances and utility of the human plasma proteome[J]. J Proteome Res, 2021, 20(12): 5241-5263.
[7]	Chen J, Xu F Z, Ruan X X, et al. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses[J]. eBioMedicine, 2023, 89: 104494.
[8]	Zhang L, Xiong Y J, Zhang J, et al. Systematic proteome-wide Mendelian randomization using the human plasma proteome to identify therapeutic targets for lung adenocarcinoma[J]. J Transl Med, 2024, 22(1): 330.
[9]	Yuan S, Xu F Z, Li X, et al. Plasma proteins and onset of type 2 diabetes and diabetic complications: proteome-wide Mendelian randomization and colocalization analyses[J]. Cell Rep Med, 2023, 4(9): 101174.
[10]	Wu M H, Zhang M H, Hu X D, et al. Proteome-wide Mendelian randomization and therapeutic targets for bladder cancer[J]. BMC Urol, 2024, 24(1): 273.
[11]	Sun J, Zhao J H, Jiang F Y, et al. Identification of novel protein biomarkers and drug targets for colorectal cancer by integrating human plasma proteome with genome[J]. Genome Med, 2023, 15(1): 75.
[12]	Bourgault J, Abner E, Manikpurage H D, et al. Proteome-wide Mendelian randomization identifies causal links between blood proteins and acute pancreatitis[J]. Gastroenterology, 2023, 164(6): 953-965.e3.
[13]	Nam K, Kim J, Lee S. Genome-wide study on 72 298 individuals in Korean biobank data for 76 traits[J]. Cell Genom, 2022, 2(10): 100189.
[14]	Cha P C, Zembutsu H, Takahashi A, et al. A genome-wide association study identifies SNP in DCC is associated with gallbladder cancer in the Japanese population[J]. J Hum Genet, 2012, 57(4): 235-237.
[15]	Ferkingstad E, Sulem P, Atlason B A, et al. Large-scale integration of the plasma proteome with genetics and disease[J]. Nat Genet, 2021, 53(12): 1712-1721.
[16]	Sun B B, Maranville J C, Peters J E, et al. Genomic atlas of the human plasma proteome[J]. Nature, 2018, 558(7708): 73-79.
[17]	Candia J. SomaScan bioinformatics: normalization, quality control, and assessment of pre-analytical variation[J]. Methods Mol Biol, 2025, 2929: 107-127.
[18]	Coorens T H H, Spencer Chapman M, Williams N, et al. Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples[J]. Nat Protoc, 2024, 19(6): 1866-1886.
[19]	He Y, Koido M, Shimmori Y, et al. GWASLab: a Python package for processing and visualizing GWAS summary statistics[PP/OL]. Jxiv(2023-05-01)[2025-06-19]. https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/370.
[20]	Xue A L, Zhu Z H, Wang H W, et al. Unravelling the complex causal effects of substance use behaviours on common diseases[J]. Commun Med, 2024, 4(1): 43.
[21]	Yang J, Lee S H, Goddard M E, et al. GCTA: a tool for genome-wide complex trait analysis[J]. Am J Hum Genet, 2011, 88(1): 76-82.
[22]	Kamat M A, Blackshaw J A, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics, 2019, 35(22): 4851-4853.
[23]	Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. eLife, 2018, 7: e34408.
[24]	Giambartolomei C, Vukcevic D, Schadt E E, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics[J]. PLoS Genet, 2014, 10(5): e1004383.
[25]	Ding Q, Xu W Q, Yang H X, et al. The causal impact of complement C3d receptor 2 on head and neck cancer microenvironment and its implications for immunotherapy response prediction[J]. Heliyon, 2024, 10(7): e29312.
[26]	Fehervari Z. Complement swings the balance[J]. Nat Immunol, 2020, 21(5): 488.
[27]	Seki H, Koyama K, Tanaka J, et al. Neural cell adhesion molecule and perineural invasion in gallbladder cancer[J]. J Surg Oncol, 1995, 58(2): 97-100.
[28]	Sowparani S, Mahalakshmi P, Sweety J P, et al. Ubiquitous neural cell adhesion molecule (NCAM): potential mechanism and valorisation in cancer pathophysiology, drug targeting and molecular transductions[J]. Mol Neurobiol, 2022, 59(9): 5902-5924.
[29]	Malmström E, Malmström L, Hauri S, et al. Human proteome distribution atlas for tissue-specific plasma proteome dynamics[J]. Cell, 2025, 188(10): 2810-2822.e16.

基于全血浆蛋白质组的孟德尔随机化方法探究胆囊癌的风险标志物

Identification of risk biomarkers of gallbladder cancer through plasma proteome-wide Mendelian randomization analysis

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