RESUMEN
Thyroid cancer (THCA) is the most common endocrine malignancy worldwide and has been rising at the fastest rate in recent years. Long-stranded non-coding RNAs (lncRNAs) and N6-methyladenosine (m6A) have been associated with immunotherapy efficacy and cancer prognosis. However, how m6A-associated lncRNAs (mrlncRNAs) affect the prognosis of patients with thyroid cancer is unclear. Therefore, this study utilized The Cancer Genome Atlas (TCGA) database to provide thyroid cancer-related transcriptomic data and related clinical data. The R program was used to identify m6A-related lncRNAs, and a risk model consisting of two lncRNAs (LINC02471 and DOCK9-DT) was obtained using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Kaplan-Meier survival analysis and transient subject operating characteristics (ROC) were used for analysis. The results showed a substantial association between immune cell infiltration and risk scores. Independent analyses confirmed that the expression of LINC02471 and DOCK9-DT was significantly higher in thyroid cancer tissues than in normal tissues, suggesting that they may be useful biomarkers for thyroid cancer.