A Fatty Acid Metabolism Signature Associated With Clinical Therapy in Clear Cell Renal Cell Carcinoma - Wei, Zhihao; Cheng, Gong; Ye, Yuzhong; Le, Changjie; Miao, Qi; Chen, Jiawei; Yang, Hongmei; Zhang, Xiaoping (Frontiers in Genetics, 2022)
Renal cell carcinoma is one of the most common tumors in the urinary system, among which clear cell renal cell carcinoma is the most common subtype with poor prognosis. As one of the tumors closely related to lipid metabolism, the role of fatty acid metabolism in ccRCC was investigated to predict the prognosis and guide treatment strategies. RNA-seq and clinical information of patients with ccRCC and expression microarray of human renal cell carcinoma cell lines were obtained from TCGA and GEO database. Fatty acid metabolism-related risk signature was established by univariate Cox regression and LASSO analysis to predict patients’ prognosis and response to different treatment modalities. Using the fatty acid metabolism risk signature, the risk score for each sample in the TCGA cohort was calculated and divided into high-risk and low-risk groups, with the cutoff point being the median. Patients with higher risk scores had a poorer prognosis than those with lower risk scores. The response of each sample to immunotherapy was predicted from the “TIDE” algorithm, while the sensitivity of each sample to sunitinib was obtained using the “pRRophetic” R package. Patients with lower risk scores had higher expression of PD-L1 and better efficacy for sunitinib compared with those in the high-risk group, and were less likely to develop drug resistance, while patients with high risk scores had a strong response to anti CTLA4 antibody therapy. Nomogram was constructed by independent prognostic factors to predict the 1-, 3-, 5- year survival. According to the calibration curves, the nomogram had an excellent ability to predict survival for patients with ccRCC. Therefore, the fatty acid metabolism risk signature we established can not only predict the survival of patients with ccRCC, but also predict the patients’ response to targeted therapy as well as immunotherapy to provide optimal treatment strategies for patients.