Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.
Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.
Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.
Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.
Keywords: Clear cell renal cell carcinoma; Drug target; Systems biology; Transcriptomics; Weighted gene co-expression network.
© 2023 The Author(s). Published by S. Karger AG, Basel.