J Cancer 2019; 10(2):397-407. doi:10.7150/jca.28127 This issue

Research Paper

High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer

Haiyue Zhao1, Qian Sun2, Lisong Li3, Jinhua Zhou3, Cong Zhang2, Ting Hu2, Xuemei Zhou4, Long Zhang2, Baiyu Wang5, Bo Li6, Tao Zhu2✉, Hong Li1✉

1. Center of Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215002, China
2. Cancer Biology Research Center (Key laboratory of the ministry of education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
3. Department of Orthopedic Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.
4. Department of Obstetrics and Gynecology, Xiaogan First Hospital, Xiaogan 432000, China.
5. Department of Obstetrics and Gynecology, Yangxin County People's Hospital, Huangshi, 435200, China.
6. Department of Obstetrics and Gynecology, Suizhou Central Hospital, Suizhou 441300, China.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Zhao H, Sun Q, Li L, Zhou J, Zhang C, Hu T, Zhou X, Zhang L, Wang B, Li B, Zhu T, Li H. High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer. J Cancer 2019; 10(2):397-407. doi:10.7150/jca.28127. Available from https://www.jcancer.org/v10p0397.htm

File import instruction


Primary platinum-based chemoresistance occurs in approximately one-third of patients with serous ovarian cancer (SOC); however, traditional clinical indicators are poor predictors of chemoresistance. So we aimed to identify novel genes as predictors of primary platinum-based chemoresistance. Gene expression microarray analyses were performed to identify the genes related to primary platinum resistance in SOC on two discovery datasets (GSE51373, GSE63885) and one validation dataset (TCGA). Univariate and multivariate analyses with logistic regression were performed to evaluate the predictive values of the genes for platinum resistance. Machine learning algorithms (linear kernel support vector machine and artificial neural network) were applied to build prediction models. Univariate and multivariate analyses with Cox proportional hazards regression and log-rank tests were used to assess the effects of these gene signatures for platinum resistance on prognosis in two independent datasets (GSE9891, GSE32062). AGGF1 and MFAP4 were found highly expressed in patients with platinum-resistant SOC and independently predicted platinum resistance. Platinum resistance prediction models based on these targets had robust predictive power (highest AUC: 0.8056, 95% CI: 0.6338-0.9773; lowest AUC: 0.7245, 95% CI: 0.6052-0.8438). An AGGF1- and MFAP4-centered protein interaction network was built, and hypothetical regulatory pathways were identified. Enrichment analysis indicated that aberrations of extracellular matrix may play important roles in platinum resistance in SOC. High AGGF1 and MFAP4 expression levels were also related to shorter recurrence-free and overall survival in patients with SOC after adjustment for other clinical variables. Therefore, AGGF1 and MFAP4 are potential predictive biomarkers for response to platinum-based chemotherapy and survival outcomes in SOC.

Keywords: AGGF1, MFAP4, serous ovarian cancer, platinum resistance