Abstract
Background
There is a surprising paucity of studies investigating the potential mechanism of SKA3 in the progression and prognosis of kidney renal papillary cell carcinoma (KIRP).
Methods
We used TCGA and other databases to analyze the expression, clinical value, and potential mechanisms of SKA3 in KIRP patients. We also explored therapeutic agents for KIRP through GSCALite.
Results
SKA3 mRNA expression was significantly upregulated and the area under the curve was 0.792 (95% CI 0.727–0.856). Increased SKA3 expression was related to shorter overall survival, disease-specific survival and progression-free survival. Hub genes in protein–protein interactions were CDK1, CDC20, CCNB1, CCNA2, BUB1, AURKB, BUB1B, PLK1, CCNB2, and MAD2L1, which were differentially expressed and also associated with KIRP prognosis. Gene-set enrichment analysis indicated that E2F targets, epithelial–mesenchymal transition, glycolysis, the WNT signaling pathway, and other pathways were highly enriched upon SKA3 upregulation. Gene-set variation analysis of SKA3 and its ten hub genes showed that the significant correlation of cancer-related pathways included the cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and Ras/MAPK. In addition, we found that MEK inhibitors, ie, trametinib, selumetinib, PD0325901, and RDEA119, may be feasible targeting agents for KIRP patients.
Conclusion
SKA3 might contribute to poor prognosis of KIRP through cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and RAS/MAPK. SKA3 potentially serves as a prognostic biomarker and target for KIRP.
Ethics
Data are from the literature and public databases. As such, approval from the West China Hospital of Sichuan University Biomedical Research Ethics Committee was not required. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Acknowledgments
The results shown here are in whole or part based upon data generated by TCGA (https://www.cancer.gov/tcga). Dechao Feng, Facai Zhang, and Ling Liu contributed equally to this work, and should be considered co–first authors. Zhenghua Liu and Lu Yang are co–corresponding authors.
Disclosure
The authors have no conflicts of interest related to this work to declare.