KCNN4 is a potential prognostic marker and critical factor affecting the immune status of the tumor microenvironment in kidney renal clear cell carcinoma

BACKGROUND: The tumor microenvironment (TME) has emerged as a crucial factor in cancer development and progression. Recent findings have indicated that tumor-infiltrating immune cells (TICs) in the TME may predict cancer prognosis and response to treatment. Herein, we sought to identify critical modulators of the kidney renal clear cell carcinoma (KIRC) TME. METHODS: KIRC datasets from The Cancer Genome Atlas (TCGA) were analyzed using the ESTIMATE algorithm to determine the ImmuneScore and StromalScore. By profiling the differentially expressed genes (DEGs) in the ImmuneScore and StromalScore, we finally identified the immune- and stromal-related DEGs of the cases, through which we then performed intersection analysis to determine the immune-related genes (IRGs). Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify critical IRGs and construct a prognostic model. The CIBERSORT algorithm was used to calculate the relative content of 22 immune cell types. Finally, the datasets from the Gene Expression Omnibus (GEO) database were analyzed to validate results from the above analyses. Experimental validation was used on KIRC tissues by quantitative polymerase chain reaction (qPCR) and western blot. RESULTS: We found that the ImmuneScore was negatively correlated with patients’ prognosis. Intersection analysis of the ImmuneScore and StromalScore identified 118 IRGs that were enriched in immune-related functions. Following IRGs screening by Cox and LASSO regression analyses, six genes were identified and used to construct a KIRC prognostic model. Intersection analysis of these six genes and protein-protein interaction (PPI) were performed and obtained the most critical gene: Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4). Further analysis showed that KCNN4 expression was higher in tumor samples relative to normal controls, and was negatively correlated with prognosis. CIBERSORT analysis revealed significant correlation between KCNN4 expression and multiple types of TICs, demonstrating that KCNN4 may affect KIRC prognosis by influencing the TME immune status. Ultimately, the GEO datasets and validation experiments confirmed that KCNN4 was highly expressed in tumor tissues compared to the corresponding normal tissues. CONCLUSIONS: Our study demonstrated that KCNN4 might be a potential prognostic marker in KIRC, offering a novel therapeutic avenue.