A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma - Du, Qiujing; Zhou, Ruhao; Wang, Heng; Li, Qian; Yan, Qi; Dang, Wenjiao; Guo, Jianjin (Frontiers in Genetics, 2023)
Metabolic reprogramming is one of cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested on the basis of datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis applied. We used Kaplan–Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index(C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and chemotherapeutic drug sensitivity. We also made pan-cancer analyses of model genes to predict mutational value in different cancers. In sum, our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. Our research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.