Potential drug targets for ovarian cancer identified through Mendelian randomization and colocalization analysis.

IF 4.2 3区 医学 Q1 REPRODUCTIVE BIOLOGY Journal of Ovarian Research Pub Date : 2025-02-19 DOI:10.1186/s13048-025-01620-7
Sicong Liu, Hao Lin, Ke Zhang, Quan Zhou, Yang Shen
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Abstract

Background: The existing drugs for ovarian cancer (OC) are unsatisfactory and thus new drug targets are urgently required. We conducted proteome-wide Mendelian randomization (MR) and colocalization analysis to pinpoint potential targets for OC.

Methods: Data on protein quantitative trait loci (pQTL) for 734 plasma proteins were obtained from large genome-proteome-wide association studies. Genetic associations with OC were derived from the Ovarian Cancer Association Consortium, which included 25,509 cases and 40,941 controls. MR analysis was performed to evaluate the association between the proteins and the OC risk. Colocalization analysis was conducted to check whether the identified proteins and OC shared causal variants. In addition, the phenome-wide MR analysis was performed to clarify protein associations across the phenotype, and drug target databases were examined for target validation.

Results: Genetically predicted circulating levels of 44 proteins were associated with OC risk at Benjamini-Hochberg correction. Genetically predicted 17 proteins had evidence of the increased risk of OC (CLEC11A, MFAP2, TYMP, PDIA3, IL1R1, SPINK1, PLAU, DKK2, IL6ST, DLK1, LRRC15, CDON, ANGPTL1, SEMA4D, AKR1A1, TNFAIP6, and FCGR2B); 27 proteins decreased the risk of OC(SIGLEC9, RARRES1, SPINT3, TMEM132A, HAVCR2, CNTN2, TGFBI, GSTA1, HGFAC, TREML2, GRAMD1C, ASAH2, CPNE1, CCL25, MAPKAPK2, POFUT1, PREP, NTNG1, CA10, CACNA2D3, CA8, MAN1C1, MRC2, IL10RB, RBP4, GP5 and CALCOCO2). Bayesian colocalization demonstrated that GRAMD1C, RBP4, PLAU, PDIA3, MFAP2, POFUT1, MAN1C1 and DKK2 shared the same variant with OC. The phe-MR analyses assessed the side effects of these 44 identified proteins, and the drug target database offered information on both approved and investigational indications.

Conclusion: This study provides proof of a causal relationship between genetically predicted 44 proteins associated with OC risk, which could serve as promising drug targets for OC.

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通过孟德尔随机化和共定位分析确定卵巢癌的潜在药物靶点。
背景:现有治疗卵巢癌的药物效果不理想,迫切需要新的药物靶点。我们进行了蛋白质组范围的孟德尔随机化(MR)和共定位分析,以确定OC的潜在靶点。方法:从大基因组-蛋白组关联研究中获得734种血浆蛋白的蛋白数量性状位点(pQTL)数据。卵巢癌的遗传关联来源于卵巢癌协会联合会,其中包括25509例和40941例对照。进行MR分析以评估蛋白质与OC风险之间的关系。进行共定位分析以检查鉴定的蛋白质和OC是否有共同的因果变异。此外,进行了全表型MR分析以澄清整个表型的蛋白质关联,并检查了药物靶标数据库以进行靶标验证。结果:在benjamin - hochberg校正中,44种蛋白的遗传预测循环水平与OC风险相关。遗传预测的17种蛋白(cle11a、MFAP2、TYMP、PDIA3、IL1R1、SPINK1、PLAU、DKK2、IL6ST、DLK1、LRRC15、CDON、ANGPTL1、SEMA4D、AKR1A1、TNFAIP6和FCGR2B)具有增加OC风险的证据;27种蛋白(SIGLEC9、RARRES1、SPINT3、TMEM132A、HAVCR2、CNTN2、TGFBI、GSTA1、HGFAC、TREML2、GRAMD1C、ASAH2、CPNE1、CCL25、MAPKAPK2、POFUT1、PREP、NTNG1、CA10、CACNA2D3、CA8、MAN1C1、MRC2、IL10RB、RBP4、GP5和CALCOCO2)降低了OC的风险。贝叶斯共定位表明,GRAMD1C、RBP4、PLAU、PDIA3、MFAP2、POFUT1、MAN1C1和DKK2与OC具有相同的变异。phe-MR分析评估了这44种鉴定蛋白的副作用,药物靶标数据库提供了已批准和正在研究的适应症的信息。结论:本研究提供了基因预测的44种与OC风险相关的蛋白之间的因果关系的证据,这些蛋白可能作为有希望的OC药物靶点。
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来源期刊
Journal of Ovarian Research
Journal of Ovarian Research REPRODUCTIVE BIOLOGY-
CiteScore
6.20
自引率
2.50%
发文量
125
审稿时长
>12 weeks
期刊介绍: Journal of Ovarian Research is an open access, peer reviewed, online journal that aims to provide a forum for high-quality basic and clinical research on ovarian function, abnormalities, and cancer. The journal focuses on research that provides new insights into ovarian functions as well as prevention and treatment of diseases afflicting the organ. Topical areas include, but are not restricted to: Ovary development, hormone secretion and regulation Follicle growth and ovulation Infertility and Polycystic ovarian syndrome Regulation of pituitary and other biological functions by ovarian hormones Ovarian cancer, its prevention, diagnosis and treatment Drug development and screening Role of stem cells in ovary development and function.
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