Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

• Published in: Frontiers in molecular biosciences Vol. 8; p. 609865
• Main Authors: Zhan, Chuanchuan, Wang, Zichu, Xu, Chao, Huang, Xiao, Su, Junzhou, Chen, Bisheng, Wang, Mingshan, Qi, Zhihong, Bai, Peiming
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 08.04.2021
• Subjects: kidney cancer; microarray; Molecular Biosciences; novel markers; prognostic model; targeting therapy; WGCNA; kidney cancer; microarray; targeting therapy; novel markers; prognostic model; WGCNA
• ISSN: 2296-889X; 2296-889X
• DOI: 10.3389/fmolb.2021.609865

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.