蛋白质组范围内孟德尔随机化确定前列腺癌发展中的因果血浆蛋白。

IF 3.8 3区医学 Q2 GENETICS & HEREDITY

Human Genomics Pub Date : 2025-02-24 DOI:10.1186/s40246-025-00724-x

Jian Wu, Zitong Yang, Jiafeng Ding, Sida Hao, Hong Chen, Ke Jin, Cheng Zhang, Xiangyi Zheng

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We also replicated the findings in the FinnGen consortium.Results: Genetically proxied levels of 4 plasma proteins (CREB3L4, HDGF, SERPINA3, GNPNAT1) were identified as positively correlated with an increased risk of prostate cancer, while an increase in genetically proxied levels of 5 plasma proteins (TNFRSF6B, GSK3A, EIF4B, CLIC1, SMAD2) were significantly associated with a decreased risk of prostate cancer in the PRACTICAL consortium. 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引用次数: 0

摘要

背景：前列腺癌的病因尚不清楚，血浆蛋白水平是否与前列腺癌相关尚不清楚。方法：我们使用顺式蛋白数量性状位点（cis-pQTL）变异作为血浆蛋白的工具变量，顺式表达数量性状位点（cis-eQTL）作为循环基因表达的工具变量，在PRACTICAL联盟数据集中进行孟德尔随机化分析，以计算血浆蛋白对前列腺癌风险的因果关系。我们也在FinnGen联盟中重复了这一发现。结果：4种血浆蛋白（CREB3L4、HDGF、SERPINA3、GNPNAT1）的基因表达水平与前列腺癌风险增加呈正相关，而5种血浆蛋白（TNFRSF6B、GSK3A、EIF4B、CLIC1、SMAD2）的基因表达水平升高与前列腺癌风险降低显著相关。在鉴定的蛋白中，CREB3L4、HDGF、SERPINA3、TNFRSF6B、EIF4B和SMAD2 6种蛋白的因果效应在FinnGen联盟的复制分析中以及与meta分析相结合时仍然显著(SMAD2: OR 0.710, 95% CI 0.578-0.873， p值= 0.001；CREB3L4: OR 1.260, 95% CI 1.164 ~ 1.364， p值-4；CREB3L4: OR 1.219, 95% CI 1.033-1.438， p值= 0.019)。结论：我们一致的结果强调了血浆SMAD2和CREB3L4在前列腺癌风险中的重要作用。对这些蛋白的进一步研究可能会揭示它们在预防和治疗前列腺癌方面的潜力。

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Proteome-wide Mendelian randomization identifies causal plasma proteins in prostate cancer development.

Background: The etiology of prostate cancer remained elusive, whether plasma protein levels are associated with prostate cancer is still unknown.

Methods: We have performed Mendelian randomization analyses to calculate the causal effects of plasma proteins on the risk of prostate cancer in the PRACTICAL consortium dataset using cis-protein quantitative trait loci (cis-pQTL) variants as instrumental variables for plasma proteins, and cis-expression quantitative trait locus (cis-eQTL) for the circulating gene expression. We also replicated the findings in the FinnGen consortium.

Results: Genetically proxied levels of 4 plasma proteins (CREB3L4, HDGF, SERPINA3, GNPNAT1) were identified as positively correlated with an increased risk of prostate cancer, while an increase in genetically proxied levels of 5 plasma proteins (TNFRSF6B, GSK3A, EIF4B, CLIC1, SMAD2) were significantly associated with a decreased risk of prostate cancer in the PRACTICAL consortium. Among the identified proteins, the causal effects of six proteins including CREB3L4, HDGF, SERPINA3, TNFRSF6B, EIF4B, and SMAD2 remained significant in the replication analyses in the FinnGen consortium and when combined with meta-analyses (SMAD2: OR 0.710, 95% CI 0.578-0.873, p-value = 0.001; CREB3L4: OR 1.260, 95% CI 1.164-1.364, p-value < 0.0001; HDGF: OR 1.072, 95% CI 1.021-1.125, p-value = 0.005; SERPINA3: OR 1.138, 95% CI 1.091-1.187, p-value < 0.0001; TNFRSF6B: OR 0.656, 95% CI 0.496-0.869, p-value = 0.003; EIF4B: OR 0.701, 95% CI 0.618-0.796, p-value < 0.0001). SMAD2 and CREB3L4 gene expressions proxied with cis-expression quantitative trait loci are also significantly associated with the risk of prostate cancer in both consortiums and when combined with meta-analyses (SMAD2: OR 0.787, 95% CI 0.719-0.861, p-value = 1.00 × 10^-4; CREB3L4: OR 1.219, 95% CI 1.033-1.438, p-value = 0.019).

Conclusions: Our consistent results highlighted the important roles of plasma SMAD2 and CREB3L4 in the risk of prostate cancer. Further investigations on these proteins may reveal their potential in the prevention and treatment of prostate cancer.

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来源期刊

Human Genomics GENETICS & HEREDITY-

CiteScore

6.00

自引率

2.20%

发文量

审稿时长

11 weeks

期刊介绍： Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.