Construction and Validation of an Immune-Related lncRNA Prognosis Model for Thyroid Cancer

Background: Immune-related long noncoding RNAs (lncRNAs) play an important role in the development of cancer. This study aimed to identify immune-related lncRNAs in thyroid cancer (THCA) and to develop a prognostic model for THCA Method: We downloaded immune-related gene sets from the Gene Set Enrichment Analysis (GSEA) website and obtained THCA gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were then obtained by performing a correlation analysis on the expression of lncRNAs and immune-related genes. Prognostic model for THCA immune-related lncRNAs was developed though univariate Cox regression and multiple Cox regression analyses. We confirmed the results in clinical samples using quantitative real-time PCR. Results: A totally of 26 immune-related lncRNAs in THCA were obtained. Then we constructed a prognosis model composed of seven lncRNAs (LINC01614, AC017074.1, LINC01184, LINC00667, ACVR2B-AS1, AC090673.1 and LINC00900). Our model can be used as an independent prognostic factor. Principal component analysis displayed that the lncRNAs in the model can distinguish between high and low-risk groups. Clinical correlation analysis showed that the expression levels of AC090673.1 (P<0.05), LINC01184 (P<0.001), and LINC01614 (P<0.001) were related to disease stage, and LINC00900 (P<0.001) and LINC01614 (P<0.001) were related to T stage. We validated this model in cancer and paracancerous tissues from 24 THCA patients. Conclusion: We identified and experimentally validated seven immune-related lncRNAs that can serve as potential biomarkers for THCA prognosis.