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.
Papillary thyroid cancer (PTC) accounts for the majority of malignant thyroid tumors. Recently, several microRNA (miRNA) expression profiling studies have used bioinformatics to suggest miRNA signatures as potential prognostic biomarkers in various malignancies. However, a prognostic miRNA biomarker has not yet been established for PTC. The aim of the present study was to identify miRNAs with prognostic value for the overall survival (OS) of patients with PTC by analyzing high‐throughput miRNA data and their associated clinical characteristics downloaded from The Cancer Genome Atlas database. From our dataset, 150 differentially expressed miRNAs were identified between tumor and nontumor samples; of these miRNAs, 118 were upregulated and 32 were downregulated. Among the 150 differentially expressed miRNAs, a four miRNA signature was identified that reliably predicts OS in patients with PTC. This miRNA signature was able to classify patients into a high‐risk group and a low‐risk group with a significant difference in OS (P < .01). The prognostic value of the signature was validated in a testing set ( P < .01). The four miRNA signature was an independent prognostic predictor according to the multivariate analysis and demonstrated good performance in predicting 5‐year disease survival with an area under the receiver operating characteristic curve area under the curve (AUC) score of 0.886. Thus, this signature may serve as a novel biomarker for predicting the survival of patients with PTC.
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