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YTHDF3as a prognostic predictive biomarker of thyroid cancer and its correlation with immune infiltration

PURPOSE: Thyroid cancer (TC) is one of the most common endocrine malignancies, and its morbidity continues to rise. N(6)-methyladenosine (m(6)A) RNA methylation, an epigenetic modification, is an important regulator of gene expression in TC. Therefore, it’s worth finding the characteristics and pred...

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Detalles Bibliográficos
Autores principales: Zhang, Yihan, Chen, Ying, Chen, Ruihua, Zhou, Hong, Lin, Yi, Li, Bingxin, Song, Huaidong, Zhou, Guoqiang, Dong, Mei, Xu, Huanbai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507848/
https://www.ncbi.nlm.nih.gov/pubmed/37726690
http://dx.doi.org/10.1186/s12885-023-11361-9
Descripción
Sumario:PURPOSE: Thyroid cancer (TC) is one of the most common endocrine malignancies, and its morbidity continues to rise. N(6)-methyladenosine (m(6)A) RNA methylation, an epigenetic modification, is an important regulator of gene expression in TC. Therefore, it’s worth finding the characteristics and predictive value of the m(6)A RNA methylation regulators in thyroid cancer (TC). METHOD: RNA-seq data of TC was downloaded from the Cancer Genome Atlas (TCGA) database to screen out the differential expressed regulators. The absolute contraction selection operator (Lasso) Cox regression was used to construct the risk model of m(6)A methylation regulators. The predictive value of the risk scoring model was evaluated by Kaplan Meier (K-M) analysis and receiver operating characteristic (ROC) curves. The underlying mechanism of m(6)A methylation regulators in TC was predicted by gene set enrichment analysis (GSEA). Further validation was performed by using immunohistochemistry (IHC) and q-PCR. The correlation between risk-related gene and immune infiltration was evaluated by Tumour Immune Estimation Resource (TIMER). RESULTS: IGF2BP2, YTHDF1 and YTHDF3 were screened out as strong independent prognostic factors of TC. Then a risk score model was established to further screen the predictors. Finally, according to the results of overall survival (OS) and clinical characteristics of TC, YTHDF3 was screened out as a potential predictor. Meanwhile, IHC and qPCR confirmed that YTHDF3 was expressed differential in TC. The expression of YTHDF3 was positively associated with the infiltration level of CD4(+) T cells and macrophages. It was strongly correlated with a variety of immune markers in TC. CONCLUSION: We confirmed that YTHDF3 can be used as a potential prognostic biomarker of TC. It not only plays a decisive role in the initiation and development of TC, but also provides a new perspective for understanding the modification of m(6)A RNA in TC.