Potassium channel-related genes are a novel prognostic signature for the tumor microenvironment of renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 80% of renal cell carcinomas (RCCs), and its morbidity and prognosis are unfavorable. Surgical resection is the first-line treatment for ccRCC, but the oncogenesis of ccRCC is very complex. With the development of high-throughput sequencing technology, it is necessary to analyze the transcriptome to determine more effective treatment methods. The tumor microenvironment (TME) is composed of tumor cells, various immune-infiltrating cells, fibroblasts, many cytokines, and catalysts. It is a complex system with a dynamic balance that plays an essential role in tumor growth, invasion, and metastasis. Previous studies have confirmed that potassium channels can affect the immune system, especially T lymphocytes that require potassium channel activation. However, the effect of potassium channels on the TME of ccRCC remains to be studied. Therefore, this study aims to construct a prognostic signature for ccRCC patients based on potassium ion channel-related genes (PCRGs), assess patient risk scores, and divide patients into high- and low-risk groups based on the cutoff value. In addition, we investigated whether there were differences in immune cell infiltration, immune activator expression, somatic mutations, and chemotherapeutic responses between the high- and low-risk groups. Our results demonstrate that the PCRG signature can accurately assess patient prognosis and the tumor microenvironment and predict chemotherapeutic responses. In summary, the PCRG signature could serve as an auxiliary tool for the precision treatment of ccRCC.

Automated summary

This study aims to construct a prognostic signature for ccRCC patients based on potassium ion channel-related genes (PCRGs), assess patient risk scores, and divide patients into high- and low-risk groups based on the cutoff value. The study results demonstrate that the PCRG signature can accurately assess patient prognosis, predict the tumor microenvironment, and predict chemotherapeutic responses.