Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is a renal cortical tumor. KIRC is the most common subtype of kidney cancer, accounting for 70%–80% of kidney cancer. Early identification of the risk of KIRC patients can facilitate more accurate clinical treatment, but there is a lack of effective prognostic markers. We aimed to identify new prognostic biomarkers for KIRC on the basis of the cancer stem cell (CSC) theory. METHODS: RNA-sequencing (RNA-seq) data and related clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was used to identify significant modules and hub genes, and predictive hub genes were used to construct prognostic characteristics. RESULTS: The messenger RNA expression-based stemness index (mRNAsi) in tumor tissues of patients in the TCGA database is higher than that of the corresponding normal tissues. In addition, some clinical features and results are highly correlated with mRNAsi. WGCNA found that the green module is the most prominent module associated with mRNAsi; the genes in the green module are mainly concentration in Notch binding, endothelial cell development, Notch signaling pathway, and Rap 1 signaling pathway. A protein-protein interaction (PPI) network showed that the top 10 central genes were significantly associated with the transcriptional level. Moreover, the 10 hub genes were up-regulated in KIRC. Regarding survival analysis, the nomogram of the prognostic markers of the seven pivotal genes showed a higher predictive value. The classical receiver operating characteristic (ROC) curve analysis showed that risk score biomarkers had the highest accuracy and specificity with an area under the curve (AUC) value of 0.701. CONCLUSIONS: mRNAsi-related genes may be good prognostic biomarkers for KIRC.