利用蛋白质组学揭示卵巢癌组织学类型特异性生物标志物。
Unveiling histotype-specific biomarkers in ovarian carcinoma using proteomics.
作者信息
Werner Lucas, Ittner Ella, Swenson Hugo, Rönnerman Elisabeth Werner, Mateoiu Claudia, Kovács Anikó, Dahm-Kähler Pernilla, Karlsson Per, Thorsell Annika, Rekabdar Elham, Esmaeili Parisa, Levander Fredrik, Forssell-Aronsson Eva, Tullberg Axel Stenmark, Saed Ghassan, Parris Toshima Z, Helou Khalil
机构信息
Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41390 Gothenburg, Sweden.
Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, 41390 Gothenburg, Sweden.
出版信息
Mol Ther Oncol. 2025 Jul 16;33(3):201019. doi: 10.1016/j.omton.2025.201019. eCollection 2025 Sep 18.
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, yet clinical tools for diagnosis, prognosis, and treatment remain limited, and molecular profiling of histotypes is lacking. Here, we leverage proteomic data to further stratify four main EOC histotypes, borderline (BL) and benign (B) tumors, and identify candidate prognostic and diagnostic biomarkers. Using proteomic data from 300 patient samples, we identified differentially abundant proteins (DAPs) such as SNCG, S100A1, VWA2, AGR2, CTH, and SPINK1 and biomarker panels to stratify the tissues. Enrichment of biological processes profiled histotypes and involvement of DAPs. Survival analysis identified candidate biomarkers predicting overall- and disease-specific survival with histotype-specificity. Of these, GLYR1, RPL12, GDPGP1, and POLR2M were associated with favorable outcomes, while SDF4, PPP3CC, EIF2AK2, and STX6 were linked to unfavorable outcomes. Collectively, these findings provide histotype-specific attributes for known and EOC biomarkers that may serve as clinical tools for EOC diagnosis and treatment decisions.
上皮性卵巢癌（EOC）是最致命的妇科恶性肿瘤，但用于诊断、预后和治疗的临床工具仍然有限，并且缺乏组织学类型的分子谱分析。在此，我们利用蛋白质组学数据进一步对四种主要的EOC组织学类型、交界性（BL）和良性（B）肿瘤进行分层，并鉴定候选的预后和诊断生物标志物。利用来自300例患者样本的蛋白质组学数据，我们鉴定出差异丰富蛋白（DAP），如SNCG、S100A1、VWA2、AGR2、CTH和SPINK1以及生物标志物面板来对组织进行分层。生物过程的富集分析描绘了组织学类型以及DAP的参与情况。生存分析鉴定出具有组织学特异性的预测总生存和疾病特异性生存的候选生物标志物。其中，GLYR1、RPL12、GDPGP1和POLR2M与良好预后相关，而SDF4、PPP3CC、EIF2AK2和STX6与不良预后相关。总体而言，这些发现为已知的和EOC生物标志物提供了组织学特异性属性，可能作为EOC诊断和治疗决策的临床工具。
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