Identification of Endoplasmic Reticulum Stress-related Genes for Predicting Prognosis, Immunotherapy Response, and Drug Sensitivity in Thyroid Cancer

ER stress has emerged as a promising target for cancer therapy. RNA sequencing data of patients with THCA were obtained from the TCGA database to identify differentially expressed genes associated with ER stress. Signature genes were selected through univariate Cox regression, LASSO, and multivariate Cox regression analyses. The predictive performance of the model was assessed using Kaplan-Meier survival analysis and ROC curves. GSEA was conducted to explore pathway enrichment between high-risk and low-risk groups. The immune landscape of risk groups was characterized using ssGSEA, ESTIMATE and CIBERSORT algorithms. Quantitative real-time PCR was employed to investigate the mRNA expression of the signature genes. Finally, immunotherapy response and potential drug sensitivity were evaluated. The prognostic model based on the signature genes ANK2, APOE, ERP27, FPR2, and NOS1, demonstrated robust predictive performance. GSEA results revealed distinct pathway enrichment patterns in the high-risk and low-risk groups. Furthermore, ssGSEA revealed that low-risk patients exhibited enhanced immune-related functions and increased immune cell infiltration. The RT-qPCR results revealed that in thyroid cancer cells, APOE and ERP27 expression levels were elevated, and ANK1, NOS1, and FPR2 expression levels were decreased. Immunotherapy, as well as Palbociclib and Perifosine, were predicted to be more effective for low-risk patients. Conversely, high-risk patients were more likely to benefit from Axitinib, Imatinib, Nilotinib, and Temsirolimus. This study identified 5 signature genes as potential biomarkers and therapeutic targets for THCA. These findings provide novel insights into the prognosis and targeted therapy of THCA, offering a foundation for furture clinical applications.