Identification and validation of m6A RNA methylation regulators with clinical prognostic value in Papillary thyroid cancer

Abstract

Abstract Background Papillary thyroid cancer (PTC) is a type of malignant tumor with excellent prognosis, accounting for more than 80% of thyroid cancer. Recently, numerous studies illustrated the importance of N 6 -methyladenosine (m 6 A) RNA modification to tumorigenesis, but it has never been reported in PTC. Methods We downloaded data from The Cancer Genome Atlas (TCGA) and analyzed RNA expression, single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) of 19 m 6 A RNA methylation regulators in PTC. Then we used nonnegative matrix factorization (NMF) to cluster patients into two m 6 A subtypes and compared them in overall survival (OS) and disease-free survival (DFS). The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to select genes for the construction of a m 6 A-related signature. The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, K-M (Kaplan–Meier) survival analysis, univariant and multivariant analyses. Results CNVs and differential expression of m 6 A regulators were observed in PTC patients. Especially IGF2BP2 (Insulin-like growth factor 2 mRNA binding protein 2), which was most significantly overexpressed in tumor tissue. We chose 4 genes in the m 6 A-related module from WGCNA: IGF2BP2, STT3A, MTHFD1 and GSTM4, and used them to construct a m 6 A-related signature. The prognostic value of this signature was validated, and risk scores provided by the signature was the independent prognostic factor for PTC. A nomogram was also provided for clinical usage. Conclusions We performed a comprehensive evaluation of the m 6 A RNA modification landscape of PTC and explored its underlying mechanisms. Our m 6 A-related signature was of great significance in predicting the DFS of patients with PTC. And IGF2BP2 was a gene worthy for further analysis as its strong correlation with DFS and clinical phenotypes of PTC.