Identification of m6A-related lncRNAs for thyroid cancer recurrence

Abstract

Background: Although the prognosis of thyroid cancer (THCA) is generally good, many patients have a high risk of recurrence after treatment. N6-methyladenosine (m6A)-related long noncoding RNAs (lncRNAs) have been extensively studied in recent years. However, the potential of m6A-related lncRNAs to predict recurrence in THCA is unknown. Methods: RNA sequencing (RNA-seq) data and clinical information for THCA were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DELs) were identified using the R package DESeq2. A coexpression network based on m6A-related genes and lncRNAs was constructed. The CIBERSORT algorithm and gene set enrichment analysis (GSEA) were used for immune-infiltrating cell estimation and clustering functional enrichment analysis, respectively. A Kaplan-Meier plot was used for prognostic analysis based on m6A-associated lncRNA risk patterns. The expression of lncRNAs in recurrent and nonrecurrent THCA tissues was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR). Results: A network of m6A-related lncRNAs containing 8 lncRNAs was constructed with good predictive power for recurrence in THCA. A total of 3 clusters were obtained, and cluster 1 was most associated with THCA recurrence. We found significantly lower levels of CD8 T cells and follicular helper T cells, and significantly higher levels of dendritic cells (DCs), M2 macrophages, resting DCs, regulatory T cells, and mast cells in cluster 1 patients. Pathway analysis revealed significant enrichment in natural killer cell-mediated cytotoxicity, butyrate metabolism, and cell adhesion molecules in cluster 1. The m6A-related lncRNA risk model was effective in predicting progression-free survival (PFS) in patients with THCA recurrence. RT-qPCR analysis based on 40 THCA clinical samples from our center found the risk model to be a good predictor of recurrence in THCA patients. Conclusions: In summary, m6A-related lncRNAs may provide a novel predictive method for prognostic relapse in THCA patients.