Value of Fatty Acid Metabolism-Related Genes as Individual Risk Prediction Model for Clear Cell Renal Cell Carcinoma

Background: The dysregulation of fatty acid metabolism (FAM) is pivotal for the occurrence and development of cancer. However, the prognostic value of genes associated with FAM in clear cell renal cell carcinoma (ccRCC) characterized by the presence of numerous lipid droplets remains elusive. To construct a multiple FAM gene prognosis model of ccRCC using the TCGA database. The model was combined with clinical features, and a nomograph was established and weighted. In addition, the infiltration of immune cells in tumor microenvironment (TME) was also studied. Seven genes associated with FAM (HACD1, HPGD, CPT1B, SCD5, GAD2, IL4I1, CPT2) were chosen to establish a prognosis model, and ccRCC patients were separated into high and low risk groups. ROC analysis and principal component analysis (PCA) showed that the model exhibited the best performance. The prediction capability of the nomogram was verified by the ROC curve, calibration diagram and decision curve analysis (DCA). Tumor microenvironment and immune cell infiltration analysis were associated with risk scores in ccRCC patients. There were remarkable diversities in the expression of immuno-checkpoints and RNA methylation regulatory genes in both groups. The prognosis model using genes related to FAM exhibits a satisfactory prediction power, which can ameliorate personalized therapies for ccRCC patients.