Development of a Novel KCNN4-Related ceRNA Network and Prognostic Model for Renal Clear Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) accounts for more than 80% of renal cell carcinomas. Yet, it has not been fully understood about the derivation and progression of the tumor, as well as the long-term benefits from multimodality therapy. Therefore, reliable and applicable molecular markers are urgently needed for the prediction of diagnosis and prognosis of ccRCC patients.

Methods: Genetic and clinical information of 533 ccRCC patients from The Cancer Genome Atlas database was collected for comprehensive bioinformatic analyses. UALCAN was used to detect gene expression in paired tumor samples. Two data sets from Gene Expression Omnibus database were analyzed to identify differentially expressed genes (DEGs), and Gene Set Enrichment Analysis was applied for the functional enrichment of DEGs. Tumor Immune Single Cell Hub and Tumor IMmune Estimation Resource databases were separately used for analyses of single-immune cell and immune cell infiltration. Encyclopedia of RNA Interactomes database was explored to predict targeted microRNAs (miRNAs) and corresponding long non-coding RNAs (lncRNAs). Cox regression analysis was performed for the construction of risk signature and prognosis model. Finally, quantitative real-time polymerase chain reaction and western blot were conducted for KCNN4 expression detection in cell lines and clinical samples. Small interfering RNA was employed to knock down KCNN4, and corresponding functional experiments were conducted on ccRCC cells as well.

Results: KCNN4 showed elevated expression in tumors and prominent clinical correlation in ccRCC. In total, 41 KCNN4-related genes were enriched, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed they were intimately related to immune-related signaling pathways. Spearman's analysis revealed the significantly positive correlation of KCNN4 with immune cell infiltration. By integrating hub miRNA-let-7e-5p and four critical lncRNA, a competitive endogenous RNA network-based risk signature was constructed. The prognosis model derived from it showed considerable predictive value for survival of ccRCC patients. Finally, in vitro experiments confirmed the remarkable tumor-promoting role of KCNN4 in ccRCC cells.

Conclusion: KCNN4 significantly affected the immune status of tumor microenvironment and immunotherapy elements, through which it promoted tumor progression in ccRCC, and it could be a potential biomarker for prognosis and immunotherapy effects of ccRCC patients.