A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer

• Published in: International journal of gynecological cancer Vol. 28; no. 1; pp. 51 - 58
• Main Authors: Jin, Chengjuan, Xue, Yingfeng, Li, Yingwei, Bu, Hualei, Yu, Hongfeng, Zhang, Tao, Zhang, Zhiwei, Yan, Shi, Lu, Nan, Kong, Beihua
• Format: Journal Article
• Language: English
• Published: United States by the International Gynecologic Cancer Society and the European Society of Gynaecological Oncology 01.01.2018; BMJ Publishing Group LTD
• Subjects: Clinical outcomes; Medical prognosis; Ovarian cancer
• ISSN: 1048-891X; 1525-1438
• DOI: 10.1097/IGC.0000000000001141

OBJECTIVEHigh-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. METHODSWe 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. RESULTSHigh 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. CONCLUSIONSCXCL11 combined with HMGA2 signature was a clinically applicable prognostic model that could precisely predict an HGSOC patientʼs OS and tumor recurrence. This model could serve as an important tool for risk assessment of HGSOC prognosis.