KCNN4 and S100A14 act as predictors of recurrence in optimally debulked patients with serous ovarian cancer
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Haiyue Zhao1,*, Ensong Guo1,*, Ting Hu1, Qian Sun1, Jianli Wu1, Xingguang Lin1, Danfeng Luo1, Chaoyang Sun1, Changyu Wang1, Bo Zhou1, Na Li1, Meng Xia1, Hao Lu1, Li Meng1, Xiaoyan Xu1, Junbo Hu1, Ding Ma1, Gang Chen1, Tao Zhu1
1Cancer Biology Research Center (Key Laboratory of the Ministry Of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
* These authors have contributed equally to this work
Tao Zhu, email: firstname.lastname@example.org
Gang Chen, email: email@example.com
Keywords: serous ovarian cancer, recurrence, prognosis, KCNN4, S100A14
Received: November 26, 2015 Accepted: May 8, 2016 Published: May 30, 2016
Approximately 50-75% of patients with serous ovarian carcinoma (SOC) experience recurrence within 18 months after first-line treatment. Current clinical indicators are inadequate for predicting the risk of recurrence. In this study, we used 7 publicly available microarray datasets to identify gene signatures related to recurrence in optimally debulked SOC patients, and validated their expressions in an independent clinic cohort of 127 patients using immunohistochemistry (IHC). We identified a two-gene signature including KCNN4 and S100A14 which was related to recurrence in optimally debulked SOC patients. Their mRNA expression levels were positively correlated and regulated by DNA copy number alterations (CNA) (KCNN4: p=1.918e-05) and DNA promotermethylation (KCNN4: p=0.0179; S100A14: p=2.787e-13). Recurrence prediction models built in the TCGA dataset based on KCNN4 and S100A14 individually and in combination showed good prediction performance in the other 6 datasets (AUC:0.5442-0.9524). The independent cohort supported the expression difference between SOC recurrences. Also, a KCNN4 and S100A14-centered protein interaction subnetwork was built from the STRING database, and the shortest regulation path between them, called the KCNN4-UBA52-KLF4-S100A14 axis, was identified. This discovery might facilitate individualized treatment of SOC.
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