Hyejung Lee, Jincheng Shen, Muhammad Zaki Fadlullah, Anna Neibling, Claire Hanson, Enos Ampaw, Tengda Lin, Matt Larsen, Jennifer Lloyd, Benjamin L Maughan, Umang Swami, Sumati Gupta, Jonathan Tward, Skyler B Johnson, Brock O'Neil, Bogdana Schmidt, Christopher B Dechet, Benjamin Haaland, Liang Wang, Aik-Choon Tan, Manish Kohli
{"title":"循环前列腺癌蛋白质组景观和转移性去势抵抗前列腺癌的预后生物标志物。","authors":"Hyejung Lee, Jincheng Shen, Muhammad Zaki Fadlullah, Anna Neibling, Claire Hanson, Enos Ampaw, Tengda Lin, Matt Larsen, Jennifer Lloyd, Benjamin L Maughan, Umang Swami, Sumati Gupta, Jonathan Tward, Skyler B Johnson, Brock O'Neil, Bogdana Schmidt, Christopher B Dechet, Benjamin Haaland, Liang Wang, Aik-Choon Tan, Manish Kohli","doi":"10.1186/s12014-025-09536-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Plasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).</p><p><strong>Methods: </strong>Plasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.</p><p><strong>Results: </strong>Of 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).</p><p><strong>Conclusions: </strong>Plasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"22 1","pages":"13"},"PeriodicalIF":2.8000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008844/pdf/","citationCount":"0","resultStr":"{\"title\":\"Circulatory prostate cancer proteome landscapes and prognostic biomarkers in metastatic castrate resistant prostate cancer.\",\"authors\":\"Hyejung Lee, Jincheng Shen, Muhammad Zaki Fadlullah, Anna Neibling, Claire Hanson, Enos Ampaw, Tengda Lin, Matt Larsen, Jennifer Lloyd, Benjamin L Maughan, Umang Swami, Sumati Gupta, Jonathan Tward, Skyler B Johnson, Brock O'Neil, Bogdana Schmidt, Christopher B Dechet, Benjamin Haaland, Liang Wang, Aik-Choon Tan, Manish Kohli\",\"doi\":\"10.1186/s12014-025-09536-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Plasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).</p><p><strong>Methods: </strong>Plasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.</p><p><strong>Results: </strong>Of 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).</p><p><strong>Conclusions: </strong>Plasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.</p>\",\"PeriodicalId\":10468,\"journal\":{\"name\":\"Clinical proteomics\",\"volume\":\"22 1\",\"pages\":\"13\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008844/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical proteomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12014-025-09536-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical proteomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12014-025-09536-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
摘要
背景:使用Olink®Explore Proximity Extension Assay对前列腺癌(PC)患者进行基于血浆的高plex蛋白质组学分析,以鉴定不同PC状态相关的血浆蛋白,并探索潜在的预后生物标志物。进行性PC状态包括局部、器官局限型PC (local PC)、转移性激素敏感型PC (mHSPC)和转移性去势抵抗型PC (mCRPC)。方法:84例PC患者血浆标本进行均匀处理(局部PC 10例;mHSPC患者29例;45例mCRPC患者)。进行了一项蛋白质组全关联研究,以确定进展性癌症状态中差异过表达的蛋白质。具体来说,采用顺序筛选方法,评估一种疾病状态下过表达的蛋白质在进展性疾病状态下的过表达。该方法采用线性回归、方差分析和t检验。然后使用Cox比例风险模型,利用mCRPC中的差异表达蛋白(DEPs)构建mCRPC患者总生存期(OS)的预后模型。在一个独立的mCRPC患者样本中,使用受试者工作特征曲线下的时间依赖面积(tAUC)来评估该模型的预测性能。然后将预后模型的tAUC与不含DEPs的模型的tAUC进行比较,以评估循环蛋白在预测生存中的附加价值。结果:在736个肿瘤相关蛋白中,26个在局部PC、mHSPC和mCRPC状态下存在差异表达。其中,与局部状态相比,20个在转移状态中过表达,与mHSPC状态相比,在mCRPC状态中过表达。在这20个蛋白中,核糖核苷二磷酸还原酶亚基M2 (RRM2)被确定为mCRPC中OS的预后生物标志物,每增加一个标准化表达单位,其风险比为2.30(95%置信区间(CI) 1.17-4.51)。包含先前确定的临床预后因素的模型的tAUC为0.62 (95% CI 0.29-0.91),而包含RRM2与临床预后因素的模型的tAUC为0.87 (95% CI 0.51-0.98)。结论:血浆蛋白质组分析可以识别与mCRPC状态存活相关的新型循环dep。RRM2过表达与不良的mCRPC生存有关,将其与传统预后因素结合可增强预后模型的预测性能。
Circulatory prostate cancer proteome landscapes and prognostic biomarkers in metastatic castrate resistant prostate cancer.
Background: Plasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).
Methods: Plasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.
Results: Of 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).
Conclusions: Plasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.
期刊介绍:
Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts.