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

Abstract

Backgrounds: Lipid 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. Methods: The 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. Results: In 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. Conclusions: In 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.

Keywords

• Fatty acid metabolism
• Renal clear cell carcinoma
• Prognostic model
• ACADM
• Systems biology