Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. While evidence is mounting that links ccRCC to genetic changes, researchers have not yet fully understood the precise molecular mechanisms involved. Although drug therapies are the preferred option following metastasis, many patients develop resistance to these treatments after about two years. This underscores the necessity for multi-targeted therapies to effectively combat ccRCC. To elucidate the molecular mechanisms driving ccRCC development and progression, as well as to identify potential multi-targeted treatment options, it is crucial to pinpoint the key genes (KGs) associated with ccRCC.
Initially, we identified 133 common differentially expressed genes (cDEGs) between ccRCC and control samples by analyzing nine microarray gene-expression datasets (NCBI accession IDs: GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895). We then refined these cDEGs through survival analysis using independent data from the TCGA and GTEx databases, resulting in 54 significant survival-related differentially expressed genes (scDEGs). Utilizing protein-protein interaction (PPI) network analysis on this refined set, we identified eight top-ranked KGs linked to ccRCC: PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, and SERPINA5. Pan-cancer analysis of these KGs in the TCGA database revealed significant associations with various kidney cancer subtypes, including ccRCC.
Gene regulatory network (GRN) analysis uncovered key transcriptional and post-transcriptional regulators associated with these KGs. Additionally, enrichment analysis of the scDEGs set identified critical molecular functions, biological processes, cellular components, and pathways related to ccRCC. DNA methylation studies indicated both hypomethylation and hypermethylation patterns in the KGs, which may contribute to ccRCC development. Analysis of immune infiltrating cell types revealed that KGs showed a significant correlation with immune cells, particularly a positive association with CD4+ T cells and a negative correlation with most other immune cell types, corroborated by existing literature.
Finally, we identified ten repurposable drug candidates (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib) through molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with independent receptors further confirmed their potential efficacy against ccRCC. These findings may serve as valuable resources for wet-lab researchers and clinicians in developing effective treatment strategies for ccRCC.