Fatty acid metabolism-related genes as a novel module biomarker for kidney renal clear cell carcinoma: Bioinformatics modeling with experimental verification

Abstract

BackgroundsLipid metabolism reprogramming is a hallmark of cancer, however, the associations between fatty acid metabolism (FAM) and kidney renal clear cell carcinoma (KIRC) prognosis are still less investigated. MethodsThe gene expression and clinical data of KIRC were obtained from TCGA. Using Cox regression and LASSO regression, a novel prognostic risk score model based on FAM-related genes was constructed, and a nomogram for prediction of overall survival rate of patients with KIRC was proposed. The correlation between risk score and the immune cell infiltration, immune-related function and tumor mutation burden (TMB) were explored. Finally, a hub gene was extracted from the model, and RT-qPCR, Western blot, Immunohistochemical, EdU, Scratch assay and Transwell experiments were conducted to validate and decipher the biomarker role of the hub gene in KIRC theranostics. ResultsIn this study, a novel risk score model and a nomogram were constructed based on 20 FAM-related genes to predict the prognosis of KIRC patients with AUC>0.7 at 1-, 3-, and 5-years. Patients in different subgroups showed different phenotypes in immune cell infiltration, immune-related function, TMB, and sensitivity to immunotherapy. In particular, the hub gene in the model, i.e., ACADM, was significantly down-expressed in human KIRC samples, and the knockdown of OCLN promoted proliferation, migration and invasion of KIRC cells in vitro. ConclusionsIn this study, a novel risk score model and a module biomarker based on FAM-related genes were screened for KIRC prognosis. More clinical carcinogenic validations will be performed for future translational applications of the findings.