Development and validation of a three-immune-related gene signature prognostic risk model in papillary thyroid carcinoma

PURPOSE Increasing evidence indicates that there is a correlation between papillary thyroid carcinoma (PTC) prognosis and the immune signature. Our goal was to construct a new prognostic tool based on immune genes to achieve more accurate prognosis predictions and earlier diagnoses of PTC. METHODS The 493 PTCs samples and 58 tumor-adjacent normal tissues were obtained from The Cancer Genome Atlas database (TCGA). Immune genes were obtained from the ImmPort database. First, this cohort was randomly divided into training cohort and testing cohort. Second, the differentially expressed (DE) immune genes from the training set were used to construct the prognostic model. Then, the testing and entire data cohorts were used to validate the model, and the data were analyzed to determine the correlation of the clinical prognostic model with immune cell infiltration and expression profiles of human leukocyte antigen (HLA) genes. Finally, an analysis of the gene ontology (GO) annotation was performed. RESULTS A total of 189 upregulated and 128 downregulated DE immune genes were identified. We developed and validated a three-immune gene model for PTC that includes Hsp70, NOX5, and FGF23. This model was demonstrated to be an independent prognostic variable. In addition, the overall immune activity of the high-risk group was higher than that of the low-risk group. CONCLUSIONS We developed and validated a three-immune gene model for PTC that includes HSPA1A, NOX5, and FGF23. This model can be used as a validated tool to predict outcomes in PTC.