A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Jin, Chengjuan; Xue, Yingfeng; Li, Yingwei; Bu, Hualei; Yu, Hongfeng; Zhang, Tao; Zhang, Zhiwei; Yan, Shi; Lu, Nan; Kong, Beihua
• Is Part Of: International journal of gynecological cancer. Volume 28:Issue 1(2018)
• Publication Date: 2018-01
• Subjects: CXCL11 -- HMGA2 -- MUC16 -- HGSOC -- Clinical outcome; Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1097/IGC.0000000000001141
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Objective: High-grade serous ovarian cancer (HGSOC) accounts for approximately 70% deaths in ovarian cancer. The overall survival (OS) of HGSOC is poor and still remains a clinical challenge. High-grade serous ovarian cancer can be divided into 4 molecular subtypes. The prognosis of different molecular subtypes is still unclear. We aimed to investigate the prognostic values of immunohistochemistry-based different molecular subtypes in patients with HGSOC. Methods: We analyzed the protein expression of representative biomarkers (CXCL11, HMGA2, and MUC16) of 3 different molecular subtypes in 110 formalin-fixed, paraffin-embedded HGSOC by tissue microarrays. Results: High CXCL11 expression predicted worse OS, not disease-free survival (DFS; P = 0.028 for OS, P = 0.191 for DFS). High HMGA2 expression predicted worse OS and DFS ( P = 0.037 for OS, P = 0.021 for DFS). MUC16 expression was not associated with OS or DFS ( P = 0.919 for OS, P = 0.517 for DFS). Multivariate regression analysis showed that CXCL11 combined with HMGA2 signature was an independent predictor for OS and DFS in patients with HGSOC. Conclusions: CXCL11 combined with HMGA2 signature was a clinically applicable prognostic model that could precisely predict an HGSOC patient's OS and tumor recurrence. This model could serve as an important tool for risk assessment of HGSOC prognosis. Abstract : Supplemental digital content is available in the text.