A five-gene signature may predict sunitinib sensitivity and serve as prognostic biomarkers for renal cell carcinoma

被引:36
作者
Chen, Yuan-Lei [1 ]
Ge, Guang-Ju [1 ]
Qi, Chao [2 ]
Wang, Huan [1 ]
Wang, Huai-Lan [1 ]
Li, Li-Yang [3 ]
Li, Gong-Hui [1 ]
Xia, Li-Qun [1 ]
机构
[1] Zhejiang Univ, Sir Run Run Shaw Hosp, Dept Urol, Sch Med, Hangzhou 310016, Zhejiang, Peoples R China
[2] Zhejiang Univ, Sir Run Run Shaw Hosp, Dept Clin Lab, Sch Med, Hangzhou, Zhejiang, Peoples R China
[3] UCL, Dept Math & Stat Sci, London, England
基金
中国国家自然科学基金;
关键词
microarray; molecular pathways; prognosis; protein-protein interaction; renal cell carcinoma; sunitinib resistance; GENE ONTOLOGY; CANCER CELLS; TRANSFERRIN; CD44; EXPRESSION; RESISTANCE; APOPTOSIS; SURVIVIN; HEAD; MUC1;
D O I
10.1002/jcp.26441
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
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.
引用
收藏
页码:6649 / 6660
页数:12
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