Plasma Proteomic Profiling Was Used to Discover a Biochemical Recurrence Prediction Model for Prostate Cancer.

IF 6.3 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Molecular & Cellular Proteomics Pub Date : 2026-07-01 Epub Date: 2026-04-14 DOI:10.1016/j.mcpro.2026.101568

Ning Xu, Linhui Zhang, Zhenmei Yao, Liqing Li, Maoping Cai, Yunzhi Wang, Xihao Wang, Subei Tan, Kai Li, Jiacheng Lyu, Hongkai Wang, Zhaoyu Qin, Jinwen Feng, Bo Dai, Yao Zhu, Hang Xiang, Xiaojian Qin, Guowen Lin, Yanzi Gu, Fei Ding, Jianyuan Zhao, Yuanyuan Qu, Chen Ding, Dingwei Ye, Wenjun Yang

{"title":"Plasma Proteomic Profiling Was Used to Discover a Biochemical Recurrence Prediction Model for Prostate Cancer.","authors":"Ning Xu, Linhui Zhang, Zhenmei Yao, Liqing Li, Maoping Cai, Yunzhi Wang, Xihao Wang, Subei Tan, Kai Li, Jiacheng Lyu, Hongkai Wang, Zhaoyu Qin, Jinwen Feng, Bo Dai, Yao Zhu, Hang Xiang, Xiaojian Qin, Guowen Lin, Yanzi Gu, Fei Ding, Jianyuan Zhao, Yuanyuan Qu, Chen Ding, Dingwei Ye, Wenjun Yang","doi":"10.1016/j.mcpro.2026.101568","DOIUrl":null,"url":null,"abstract":"<p><p>Prostate cancer (PCa) is one of the most common malignancies in men. There is limited data available regarding potential minimally-invasive biomarkers for predicting PCa outcomes and disease monitoring. Here, we investigate the proteomic profile of plasma in 222 patients with PCa and 159 healthy controls. Integrative analyses of the proteome profile and clinical features identified protein networks related to International Society of Urological Pathology (ISUP) grades and prostate-specific antigen (PSA). Proteome-based classification revealed three subtypes, PCa-I, PCa-II, and PCa-III, reflecting distinct clinical prognosis and molecular signatures. We develop a 17-protein panel and established a biochemical recurrence prediction model that effectively predicts biochemical recurrence for patients with PCa, which is better than ISUP grades and pathological stages. Finally, we validate the protein panel by parallel reaction monitoring (PRM) assay in an independent cohort. Collectively, this study portrays the plasma proteomic landscape of PCa cohort and provides a comprehensive resource for further biological and predictive research in PCa.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101568"},"PeriodicalIF":6.3000,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13321017/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular & Cellular Proteomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.mcpro.2026.101568","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/4/14 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Prostate cancer (PCa) is one of the most common malignancies in men. There is limited data available regarding potential minimally-invasive biomarkers for predicting PCa outcomes and disease monitoring. Here, we investigate the proteomic profile of plasma in 222 patients with PCa and 159 healthy controls. Integrative analyses of the proteome profile and clinical features identified protein networks related to International Society of Urological Pathology (ISUP) grades and prostate-specific antigen (PSA). Proteome-based classification revealed three subtypes, PCa-I, PCa-II, and PCa-III, reflecting distinct clinical prognosis and molecular signatures. We develop a 17-protein panel and established a biochemical recurrence prediction model that effectively predicts biochemical recurrence for patients with PCa, which is better than ISUP grades and pathological stages. Finally, we validate the protein panel by parallel reaction monitoring (PRM) assay in an independent cohort. Collectively, this study portrays the plasma proteomic landscape of PCa cohort and provides a comprehensive resource for further biological and predictive research in PCa.

查看原文本刊更多论文

应用血浆蛋白质组学分析发现前列腺癌复发的生化预测模型。

前列腺癌（PCa）是男性最常见的恶性肿瘤之一。关于预测前列腺癌预后和疾病监测的潜在微创生物标志物的可用数据有限。在这里，我们研究了222名PCa患者和159名健康对照者的血浆蛋白质组学特征。蛋白质组谱和临床特征的综合分析确定了与国际泌尿病理学学会（ISUP）分级和前列腺特异性抗原（PSA）相关的蛋白质网络。基于蛋白质组学的分类显示了三种亚型，PCa-I， PCa-II和PCa-III，反映了不同的临床预后和分子特征。我们开发了17蛋白面板，建立了生化复发预测模型，有效预测PCa患者的生化复发，优于ISUP分级和病理分期。最后，我们在一个独立的队列中通过平行反应监测（PRM）试验验证了蛋白质面板。总的来说，本研究描绘了前列腺癌队列的血浆蛋白质组学景观，为进一步的前列腺癌生物学和预测研究提供了全面的资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Molecular & Cellular Proteomics 生物-生化研究方法

CiteScore

11.50

自引率

4.30%

发文量

131

审稿时长

84 days

期刊介绍： The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes