Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data

Renal cell carcinoma is one of the most universal urinary system cancers in the world. The most common renal cell carcinoma subtype is renal clear cell carcinoma. It is usually associated with high rates of metastasis and mortality. Therefore, finding effective therapeutic targets and prognostic molecular markers is of great significance to improve the early diagnosis rate and prognostic accuracy of renal clear cell carcinoma. In this work, we successfully identified six hub genes that are closely related to the occurrence, development and prognosis of renal clear cell carcinoma and proposed three new potential prognostic markers, namely ATP4B, AC144831.1 and Tfcp2l1 through differentially expressed genes (DEGs) analysis, GO functional enrichment and KEGG pathway analysis, WGCNA analysis, and survival analysis. In addition, we established machine learning models to predict the occurrence of tumors through the gene expression data of patients. It is expected that the results of this study can provide reference value for the treatment of renal clear cell carcinoma.