BACKGROUND: Although the mortality rates of clear cell renal cell carcinoma (ccRCC) have decreased in recent years, the clinical outcome remains highly dependent on the individual patient. Therefore, identifying novel biomarkers for ccRCC patients is crucial.

MATERIAL AND METHODS: In this study, we obtained RNA sequencing data and clinical information from the TCGA database. Subsequently, we performed integrated bioinformatic analysis that includes differently expressed genes analysis, gene ontology and KEGG pathway analysis, protein-protein interaction analysis, and survival analysis. Moreover, univariate and multivariate Cox proportional hazards regression models were constructed.

RESULTS: As a result, we identified a total of 263 dysregulated genes that may participate in the metastasis of ccRCC, and established a predictive signature relying on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4, which could serve as significant progressive and prognostic biomarkers for ccRCC.

CONCLUSIONS: We identified differentially expressed genes that may be involved in the metastasis of ccRCC. Moreover, a predictive signature based on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4 could be an independent prognostic factor for ccRCC.

Keywords: Biological Markers, Carcinoma, Renal Cell, Biomarkers, Tumor, Computational Biology, Databases, Genetic, gene ontology, Genetic Predisposition to Disease, Kidney Neoplasms, Proportional Hazards Models