Key genes involved in renal fibrosis and renal clear cell carcinoma

Objective  To employ bioinformatics for exploring the key genes affecting the pathogenesis of renal fibrosis（RF） and renal clear cell carcinoma.

Methods  The gene chip datasets of RF and KIRC were downloaded from GEO database and differentially expressed genes（DEGs） retrieved through a GEO2R online tool. Then DAVID database was employed for GO and KEGG enrichment analyses. Then the STRING database was utilized for constructing a PPI network, Cytoscape's cytoHubba plug-in was used for selecting the central genes. Then TCGA, GEPIA and TIMER databases were utilized for verifying the central genes and further screening out the core genes. Meanwhile, TargetScanHuman, miRTarbase and miRWalk databases were utilized for reverse predict the targeted miRNA regulated by the hub genes. Core miRNA was screened out and a mutual network of mRNA/miRNA established. LYZ gene was analyzed by GSEA tool and the expression level of identified hub genes verified by HPA database.

Results  A total of 347 differential genes were screened out. Functional enrichment analysis was performed on down/up-regulated differential genes. Through TCGA dataset, GTEx dataset, TIMER database and HPA database, LYZ gene was gradually screened in three rounds of verification. The upstream hub regulatory miRNA was identified as has-miR-4649-3p and has-miR-873-3p.

Conclusion  Based upon these findings, LYZ, has-miR-4649-3p and has-miR-873-3p may be potential prognostic biomarkers of KIRC and contribute to the prevention and treatment of RF. As a novel therapeutic concept, it offers a promising immunotherapy for RF.