Sunitinib resistance is, nowadays, the major challenge for advanced renal cell carcinoma patients. Illuminating the potential mechanisms and exploring effective strategies to overcome sunitinib resistance are highly desired. We constructed a reliable gene signature which may function as biomarkers for prediction of sunitinib sensitivity and clinical prognosis. The gene expression profiles were obtained from The Cancer Genome Atlas database. By performing GEO2R analysis, numerous differentially expressed genes (DEGs) were found to be associated with sunitinib resistance. To acquire more precise DEGs, we integrated three different microarray datasets. Functional analysis revealed that these DEGs were mainly involved in Rap1 signaling pathway, p53 signaling pathway and Ras signaling pathway. Then, top five hub genes, BIRC5, CD44, MUC1, TF, CCL5, were identified from protein-protein interaction (PPI) network. Sub-network analysis carried out by MCODE plugin revealed that key DEGs were related with PI3K-Akt signaling pathway, Rap1 signaling pathway and VEGF signaling pathway. Next, we established sunitinib-resistant OS-RC-2 and 786-O cell lines and validated the expression of five hub genes in cell lines. To further evaluate the potentials of five-gene signature for predicting clinical prognosis, we analyzed RCC patients with gene expressions and overall survival information from two independent patient datasets. The Kaplan-Meier estimated the OS of RCC patients in the low- and high-risk groups according to gene expression signature. Multivariate Cox regression analysis indicated that the prognostic power of five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature which can predict sunitinib sensitivity and OS for advanced RCC patients, providing novel insights into understanding of sunitinib-resistant mechanisms and identification of RCC patients with poor prognosis. This article is protected by copyright. All rights reserved.
Journal of cellular physiology. 2018 Jan 12 [Epub ahead of print]
Yuan-Lei Chen, Guang-Ju Ge, Chao Qi, Huan Wang, Huai-Lan Wang, Li-Yang Li, Gong-Hui Li, Li-Qun Xia
Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China., Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China., Department of Mathematics and Statistics Science, University College of London, London, WC1E 6BT, England.