###### Abstract  
Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer. It advances quickly and often metastasizes, making the prognosis for patients challenging. This study used weighted gene co-expression network analysis (WGCNA) to study gene expression data of different stages of ccRCC obtained in the GEO database. The analysis identified three significant highly preserved gene modules across the datasets: GSE53757, GSE22541, GSE66272, and GSE57371. Functional annotation and pathway enrichment analysis using DAVID revealed inflammatory pathways (e.g., NF-kB, Hippo, and HIF-1 pathways) that may drive ccRCC development and progression. The study also introduced the involvement of viral infections associated with the disease in the metabolic reprogramming of ccRCC. A drug repurposing analysis was also conducted to identify potential drug candidates for ccRCC using the upregulated and downregulated hub genes. The top candidates are ziprasidone (dopamine and serotonin receptor antagonist) and femiaac (cyclooxygenase inhibitor). Other drug candidates were also obtained, such as phosphodiesterase/DNA methyltransferase/ATM kinase inhibitors, acetylcholine antagonists, and NAD precursors. Overall, the study's findings suggest that identifying several genes and signaling pathways related to ccRCC may uncover new targets, biomarkers, and even drugs that can be repurposed, which can help develop new and effective treatments for the disease.  
Keywords:renal cancer; clear cell renal cell carcinoma; drug repurposing; inflammatory pathways +
Footnote †: journal:  
significant leukocyte infiltrates, including CD8+ and CD4+ T cells, are linked with higher tumor grade and poor prognosis in ccRCC [14; 18].  
The early stages of ccRCC are usually managed with surgery or conventional treatments. Still, as the disease progresses to a later stage, the treatment becomes more complex and requires multiple therapies with a combination of tyrosine kinase/immune checkpoint/mTOR inhibitors [19; 20]. Along with that, drug resistance and severe side effects are also considered problems in treating ccRCC [3; 20; 21; 22]. Currently, researchers are exploring how combining traditional treatments with new approaches might address these problems. However, it has been found that simply adding existing therapies together only resolves some of the issues [20]. That is why new treatments are needed. Studies have shown also that treatment responses can vary due to many factors (e.g., genetic differences, inflammation). This emphasizes the need for a deeper understanding of ccRCC [3].  
To address these challenges, bioinformatics techniques such as weighted gene co-expression network analysis (WGCNA) can be used. WGCNA analyzes the relationship of genes in line with their expression patterns across different diseases or stages/grades of a particular disease. It helps identify important gene groups that can be analyzed to determine key genes that are significant or related to the disease's development, progression, and even pathways associated with the disease [23]. By identifying hub/key genes, researchers can also use this to identify existing drugs that can be repurposed based on their gene signatures and patterns. These genes may also uncover significant pathways that can reveal therapeutic opportunities. Recent studies employing WGCNA have explored diseases [24; 25] including cancer. This suggests that bioinformatics analysis may lead to discovering new biomarkers and important pathways involved in the disease [24; 25; 26].  
This study used four gene expression datasets (GSE53757, GSE22541, GSE66272, and GSE73731) to analyze different stages of ccRCC. Bioinformatics tools such as WGCNA, DAVID, STRING, and Cytoscape were used to identify gene modules. These gene modules are used to determine functional annotations, pathways, and key genes associated with ccRCC. The identified hub genes were also used to facilitate the repurposing of existing drugs through drug repurposing analysis.