Identification of the Signature Associated With m(6)A RNA Methylation Regulators and m(6)A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma

BACKGROUND: N6-methyladenosine (m(6)A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m(6)A RNA methylation regulators and m(6)A-related genes to understand the prognosis of early lung adeno...

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Detalles Bibliográficos
Autores principales: Guo, Bingzhou, Zhang, Hongliang, Wang, Jinliang, Wu, Rilige, Zhang, Junyan, Zhang, Qiqin, Xu, Lu, Shen, Ming, Zhang, Zhibo, Gu, Fangyan, Zeng, Weiliang, Jia, Xiaodong, Yin, Chengliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226131/
https://www.ncbi.nlm.nih.gov/pubmed/34178026
http://dx.doi.org/10.3389/fgene.2021.656114
Descripción
Sumario:BACKGROUND: N6-methyladenosine (m(6)A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m(6)A RNA methylation regulators and m(6)A-related genes to understand the prognosis of early lung adenocarcinoma. METHODS: The relevant datasets were utilized to analyze 21 m(6)A RNA methylation regulators and 5,486 m(6)A-related genes in m(6)Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan–Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed. RESULTS: Respectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m(6)A methylation. CONCLUSION: The findings of this study suggested that associated with m(6)A RNA methylation regulators and m(6)A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.

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