Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis - Huang, Hao; Zhu, Ling; Huang, Chao; Dong, Yi; Fan, Liangliang; Tao, Lijian; Peng, Zhangzhe; Xiang, Rong (Frontiers in Oncology, 2021)
Background: Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches. Methods: Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) KIRC database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA-Sequencing data was obtained from the GSE131685 and GSE171306 datasets. Results: In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 up-regulated genes and 1,192 down-regulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator and multivariate Cox regression analyses, ALDOB, EFHD1 and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA-Sequencing analysis revealed the expression profile of ALDOB, EFHD1 and ESRRG in different cell type of ccRCC. Conclusions: The present results demonstrated that ALDOB, EFHD1 and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.