The functions and prognostic values of m6A RNA methylation regulators in thyroid carcinoma.

ABSTRACT:

Background

N6-Methyladenosine (m6A) is the most common RNA modification and regulates RNA splicing, translation, translocation, and stability. Aberrant expression of m6A has been reported in various types of human cancers. m6A RNA modification is dynamically and reversibly mediated by different regulators, including methyltransferase, demethylases, and m6A binding proteins. However, the role of m6A RNA methylation regulators in thyroid cancer remains unknown. The aim of this study is to investigate the effect of the 13 main m6A RNA modification regulators in thyroid carcinoma.

Methods

We obtained clinical data and RNA sequencing data of 13 m6A RNA methylation regulators from The Cancer Genome Atlas (TCGA) THCA database. We performed consensus clustering to identify the clinical relevance of m6A RNA methylation regulators in thyroid carcinoma. Then we used LASSO Cox regression analysis to generate a prognostic signature based on m6A RNA modification regulator expression. Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and Gene Set Enrichment Analyses were performed to explore differential cellular processes and signaling pathways between the two groups based on risk signature.

Results

We found that most of the m6A RNA modification regulators are down-regulated in 450 patients with thyroid carcinoma. We derived a three m6A RNA modification regulator genes-based risk signature (FTO, RBM15 and KIAA1429), that is an independent prognostic biomarker in patients with thyroid carcinoma. Moreover, we found that this risk signature could better predict outcome in male than female. Functional research in vitro demonstrated that the m6A RNA methylation regulators involved in the model acted significant role in the proliferation and migration of thyroid cancer cells.

Conclusions

Our study revealed the influence of m6A RNA methylation regulators on thyroid carcinoma through biological experiments and three-gene prognostic model.

SUBMITTER: Yu ZH 

PROVIDER: S-EPMC8287668 | biostudies-literature |

REPOSITORIES: biostudies-literature