Identification of Key Genes Involved in the Progression of Renal Fibrosis and Associated with Kidney Renal Clear Cell Carcinoma

Purpose: Renal fibrosis (RF) is the necessary way for Chronic kidney disease (CKD) to develop to End Stage Renal Disease (ESRD). Patients with chronic kidney disease suffer from high morbidity and premature death due to various complications and even cancer. Therefore, this study aims to identify key genes in the pathogenesis of RF and Kidney Renal Clear Cell Carcinoma (KIRC).Method: We analyzed the gene expression characteristics of two databases (GSE6344 and GSE22459) and used geo2R tools to obtain the differentially expressed genes (DEG). Then, use Database for Annotation, Visualization and Integrated Discovery (DAVID) for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) path analysis. Subsequently, we used the STRING database and built the protein-protein-interactions (PPI) network, the cytoHubba plug-ins of Cytoscape were used to select the hub. Then, we used The Cancer Genome Atlas(TCGA) database to verify hub genes and further screen out core genes. Then, TargetScanHuman, miRTarbase and miRWalk databases were used to reverse-predict targeted miRNA regulated by core genes and screen out core miRNA. mRNA and miRNA mutual aid network were established. At the same time, Gene Expression Profiling Interactive Analysis（GEPIA）database was used for survival analysis of screened core genes to find genes related to prognosis. Tumor Immune Estimation Resource（TIMER）database was used to evaluate the correlation between the expression of core genes and immune cell penetration. Then use the Gene Set Enrichment Analysis (GSEA) tool to analyze the LYZ gene, and finally use the Human Protein Atlas (HPA) online database to verify the expression level of the identified central gene.Result: We filtered 2755 DEGs from the GSE6344 database, including 1292 upregulated DEGs and 1463 downregulated DEGs; 2552 DEGs were filtered from the GSE22459 database, including 2022 downregulated DEGs and 530 upregulated DEGs. We did functional enrichment analysis of down-regulated and up-regulated differential genes, Functional enrichment analysis of up-regulated genes shows that DEGs involves many functions and expression pathways. such as immune response, plasma membrane, membrane, integral component of plasma membrane, signal transduction, extracellular region and extracellular space. It is demonstrated in the PPI network constructed by 67 nodes (proteins) and 546 PPI edges (interactions); Functional enrichment analysis of down-regulated genes also shows that DEGs involves many functions and expression pathways. such as integral component of plasma membrane, plasma membrane, extracellular space and extracellular region. It is demonstrated in the PPI network constructed by 141 nodes (proteins) and 624 PPI edges (interactions). Then a gene LYZ was selected step by step in three rounds of validation through TCGA data set, GTEx data set, Timer database and HPA database. LYZ expression was significantly correlated with the immune infiltration levels of CD4+ T cells, CD8+ T cells, Macrophage, Myeloid dendritic, Neutrophil and B cell. The upstream hub miRNA that regulate this gene were identified: has-miR-4649-3p and has-miR-873-3p. Based on these findings, it is proposed that LYZ may be a potential novel diagnostic and prognostic biomarker of KIRC at the mRNA and protein levels, and has-miR-4649-3p and has-miR-873-3p at the molecular level, and can help us better manage the progression of renal fibrosis.Conclusion: Our findings suggest that immune response, inflammation and other pathways play an extremely important role in RF and KIRC. LYZ, has-miR-4649-3p and has-miR-873-3p may become potential prognostic biomarkers of KIRC and contribute to the prevention and treatment of renal fibrosis, which also shows us a new therapeutic idea that provides the possibility to treat renal fibrosis from the perspective of immunity.