Department of Pharmacology, School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450001, China.
Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
J Proteome Res. 2024 Jun 7;23(6):2241-2252. doi: 10.1021/acs.jproteome.4c00199. Epub 2024 May 24.
Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).
膀胱癌(BCa)是泌尿系统最常见的恶性肿瘤。本研究旨在建立一种全面的尿蛋白质组学特征,用于膀胱癌的非侵入性诊断和复发监测。收集了 279 例病例(63 例原发性 BCa、87 例非肿瘤对照(NT)、73 例复发 BCa(BCR)和 56 例非复发 BCa(BCNR)),以筛选尿蛋白生物标志物。在两个发现集中,通过 DDA 和序贯窗口采集所有理论质谱(SWATH-MS)分析分别鉴定和定量了 4761 种和 3668 种蛋白质。通过多重反应监测(MRM)在两个独立的组合集中验证了上调蛋白。使用多支持向量机-递归特征消除(mSVM-RFE)算法,在包括 BCa 和 NT 的两个模型中,包含 13 种蛋白质的模型表现出良好的性能,AUC 为 0.821(95%CI:0.675-0.967),90.9%的敏感性(95%CI:72.7-100%)和 73.3%的特异性(95%CI:53.3-93.3%)在诊断测试集中。同时,一个 11 标志物分类器可以显著区分 BCR 与 BCNR,在测试队列中,75.0%的敏感性(95%CI:50.0-100%),81.8%的特异性(95%CI:54.5-100%)和 AUC 为 0.784(95%CI:0.609-0.959)。值得注意的是,在 24 个标志物中有 6 个蛋白质(SPR、AK1、CD2AP、ADGRF1、GMPS 和 C8A)是新报道的。本研究揭示了膀胱癌新的尿液蛋白质生物标志物,为膀胱癌的发病机制提供了新的理论见解(数据标识符 PXD044896)。