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Identification of a Six-lncRNA Prognosis Model for Predicting Progression-Free Survival in Patients with Thyroid Cancer


 Background: Thyroid cancer is the most common malignant tumor of the endocrine system. Long non-coding RNAs (lncRNAs) have been demonstrated as novel biomarkers for cancer prognosis. Methods: In this study, we performed differential expression analysis of lncRNA expression profiles in GEO datasets. LASSO regression analysis was conducted to identify lncRNA-based prognosis model that can predict progression-free survival in thyroid cancer patients from The Cancer Genome Atlas (TCGA). Further functional analysis revealed the potential biological functions of the lncRNAs.Results: A risk score model based on six lncRNA biomarkers were established after LASSO Cox regression analysis. The prognostic value of the 6-lncRNA prognosis model was successfully validated and ROC curves was analysed. Patients were classified into high- and low-risk groups using the 6-lncRNA signature-based risk score. Patients in the low-risk group had significantly better progression-free survival than high-risk group. The result of multivariate analysis showed that the six-lncRNA signature was independent from clinical features such as age, gender and stage. GO and KEGG enrichment analysis and estimation of immune infiltration suggested that the lncRNAs might closely associated with tumorigenesis. Conclusion: Our study has constructed a novel six-lncRNA prognosis model to improve progression-free survival prediction in patients with thyroid cancer.