Integration of bioinformatics analysis to identify possible hub genes and important pathways associated with clear cell renal cell carcinoma

Abstract

Introduction:

One of the most fatal urological malignancies is clear cell renal cell carcinoma (ccRCC), yet little is known about its pathophysiology or prognosis. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for ccRCC.

Material and Methods:

Using three publically accessible ccRCC gene expression profiles acquired from the Gene Expression Omnibus database, differentially expressed genes (DEG) were discovered and function enrichment analyses were carried out. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the DAVID tool and a protein-protein interaction (PPI) network was constructed and visualized by Cytoscape. Then we identified 10 hub genes using the cytohubba plugin of Cytoscape based on degree score. The mRNA and protein expression of hub genes was analyzed by GEPIA and Human Protein Atlas (HPA) database. Then, prognosis analysis of hub genes was conducted using GEPIA 3.0 which consists of data from The Cancer Genome Atlas (TCGA).

Results:

We discovered 293 DEG which is highly enriched in several biological processes connected to immune-regulation and pathways linked to tumors, including HIF-1, PI3K-AKT, and metabolic pathways. In particular, C1QA, C1QB, FCER1G, and TYROBP were related to advanced clinical stage, high pathological grade, and poor survival in patients with ccRCC.

Conclusions:

Further molecular biological studies are required to confirm the role of the putative biomarkers in human ccRCC. Our work highlighted the hub genes and pathways involved in the progression of ccRCC.

Keywords

Bioinformatics analysis hub genes in silico RCC cancer differentially expressed genes (DEG)

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