Construction of a ferroptosis-related eight gene signature for predicting the prognosis and immune infiltration of thyroid cancer

BackgroundThyroid cancer is the most common malignant tumor of the endocrine system. Most patients with thyroid cancer have a good prognosis, although a small proportion experience recurrence and metastasis and have a poor prognosis. Ferroptosis is a novel form of regulated cell death (RCD); previous studies have confirmed that ferroptosis was associated with thyroid cancer. The purpose of this study was to investigate the key ferroptosis-related genes in thyroid cancer and their relationship with prognosis and immune cell infiltration. MethodsIn this study, 497 thyroid cancer RNA expression datasets were downloaded from the cancer genome atlas (TCGA) cohort and a prognostic risk model for eight ferroptosis-related genes (FRGs) was constructed by Lasso-Cox regression. The prognostic value of the risk model and the correlation of prognostic features with immune scores and tumor immune cell infiltration were systematically analyzed. ResultsThe prognostic risk model for eight FRGs (DPP4, TYRO3, TIMP1, CDKN2A, SNCA, NR4A1, IL-6 and FABP4) were constructed and validated in training and testing cohorts. Kaplan-Meier curve and receiver operating characteristic (ROC) curve analysis confirmed that that the ferroptosis-related eight gene signature had good predictive value for the prognosis of thyroid cancer (THCA) patients. Multivariate regression analysis further showed that the risk score of the prognostic model could be used as an independent prognostic factor for THCA patients. Functional enrichment analysis showed that DEGs in high risk and low risk groups were involved in immune-related biological processes and that there were significant differences in immune cell infiltration between the two risk groups. ConclusionWe identified eight key genes related to ferroptosis in THCA patients. Further studies are now needed to investigate the mechanisms involved; these genes may represent clinical diagnostic and prognostic biomarkers.