A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma. (1st July 2022) - Wang, Jian; Guo, Ziming; Sun, Fei; Xu, Tian; Wang, Jianlin; Yu, Jingping
Abstract : Objective . This study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA). Methods . The TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed. Results . Based on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307. Conclusion . We constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA.