## 5 Conclusions  
In the study, three significant highly preserved modules across different ccRCC stages obtained from GSE53757, GSE22541, GSE66272, and GSE73731 were identified. With WGCNA, the top hub genes were identified for each module and were used to determine functional annotation, pathways, and potential drug candidates that can be repurposed. The analysis provides insights about ccRCC through signaling pathways associated with inflammation. The top drug candidates identified for repurposing are ziprasidone (dopamine and serotonin receptor antagonist) and fentiazac (cyclooxygen-ase inhibitor). Other candidates are phosphodieserase inhibitor, acetylcholine receptor antagonist, DNA methyltransferase inhibitor, an ATM kinase inhibitor, a MAP kinase inhibitor, and NAD precursors. These findings may inhibit ccRCC through the identified inflammatory pathways. While our study is limited by its in silico approach, the theoretical data provided can be assist in developing potential drug candidates or markers for ccRCC. The hub genes identified may also offer an understanding regarding the mech-anism of ccRCC. Moreover, further analysis is recommended such as using RNA-seq data to analyze ccRCC and compare it with DNA microarray. Additionally, experiments and wet lab research are recommended to confirm and apply these findings. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14198768/s1](https://www.mdpi.com/article/10.3390/app14198768/s1)]([https://www.mdpi.com/article/10.3390/app14198768/s1](https://www.mdpi.com/article/10.3390/app14198768/s1)), Figure S1: Boxplot of ccRCC microarray datasets: (a) Stage 1, (b) Stage 2, (c) Stage 3, and (d) Stage 4 datasets; Figure S2. Sample clustering dendrogram for ccRCC: (a) Stage 1, (b) Stage 2, (c) Stage 3, and (d) Stage 4 datasets; Figure S3. TOM-based dissimilarity clustering of ccRCC (a) S1, (b) S2, (c) S3, and (d) S4 datasets. **Author Contributions:** Conceptualization, K.S.S., M.A.O. and L.L.T. ; methodology, K.S.S. ; software, K.S.S. ; validation, M.A.O. ; formal analysis, K.S.S. and M.A.O. ; investigation, K.S.S. and M.A.O. ; resources, K.S.S. and L.L.T. ; data curation, K.S.S. and M.A.O. ; writing--original draft preparation, K.S.S. ; writing--review and editing, K.S.S. and M.A.O. ; visualization, K.S.S. and M.A.O. ; supervision, L.L.T. and P.-W.T. ; project administration, L.L.T. and P.-W.T. ; funding acquisition, L.L.T. and P.-W.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. Data Availability Statement: The gene microarray datasets used for the study are openly available in the NCBI Gene Expression Omnibus (GEO) database under the accession IDs GSE53757, GSE22541, GSE66272, and GSE73731 datasets at [[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)), accessed on 12 June 2024. The authors would like to extend their gratitude to Mapua University for their support. Along with that, we also would like to acknowledge several online platforms that helped us with our manuscript. Some figures were created with BioRender.com (accessed on 1 August 2024) and Canva.com (accessed on 1 August 2024). Along with that, grammatical checking was conducted using Grammarly. The authors declare no conflicts of interest.