RT Journal Article
SR Electronic
T1 A 2-Protein Signature Predicting Clinical Outcome in High-Grade Serous Ovarian Cancer
JF International Journal of Gynecologic Cancer
JO Int J Gynecol Cancer
FD BMJ Publishing Group Ltd
SP 51
OP 58
DO 10.1097/IGC.0000000000001141
VO 28
IS 1
A1 Chengjuan Jin
A1 Yingfeng Xue
A1 Yingwei Li
A1 Hualei Bu
A1 Hongfeng Yu
A1 Tao Zhang
A1 Zhiwei Zhang
A1 Shi Yan
A1 Nan Lu
A1 Beihua Kong
YR 2018
UL http://ijgc.bmj.com/content/28/1/51.abstract
AB 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.