Identification of an m6A RNA Methylation Regulator Risk Score Model for Prediction of Clinical Prognosis in Astrocytoma

• Published in: Computational and mathematical methods in medicine Vol. 2022; pp. 1 - 13
• Main Authors: Guo, Fangzhou, Deng, Teng, Shi, Liu, Wu, Pinghua, Yan, Jun, Ling, Guoyuan, Chen, Hainan, Huang, Qianrong, Mu, Junbo, Mo, Ligen
• Format: Journal Article
• Language: English
• Published: United States Hindawi 10.01.2022
• Subjects: Adenosine - analogs & derivatives; Adenosine - genetics; Adenosine - metabolism; Astrocytoma - genetics; Astrocytoma - metabolism; Central Nervous System Neoplasms - genetics; Central Nervous System Neoplasms - metabolism; Computational Biology; Databases, Genetic; Female; Gene Expression Regulation, Neoplastic; Humans; Male; Methylation; Methyltransferases - genetics; Methyltransferases - metabolism; Models, Genetic; Prognosis; Proportional Hazards Models; Risk Factors; RNA Processing, Post-Transcriptional - genetics; RNA, Messenger - genetics; RNA, Messenger - metabolism
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2022/7168929

Astrocytoma (AS) is the most ubiquitous primary malignancy of the central nervous system (CNS). The vital involvement of the N6-methyladenosine (m6A) RNA modification in the growth of multiple human tumors is known. This study entailed probing m6A regulators with AS prognosis to construct a risk prediction model (RS) for potential clinical use. A total of 579 AS patients’ (of the Chinese Glioma Genome Atlas,CGGA) data and the expression of 12 published m6A-related genes were included in this study. Cox and selection operator (LASSO) regression analyses for independent prognostic factors and multifactor Cox analysis established an R.S. model to predict the AS patient prognosis. This was subject to verification employing 331 samples from the TCGA data set followed by gene ontology and pathway enrichment study with gene set enrichment analysis (GSEA). The R.S. constructed with three m6A genes inclusive of WTAP, RBM15, and YTHDF2 emerged as independent prognostic factors in AS patients with vital involvement in the advancement and development of the malignancy. In a nutshell, this work reported an m6A-related gene risk model to predict the prognosis of AS patients to pave the way for discerning diagnostic and prognostic biomarkers. Further corroboration employing relevant wet-lab assays of this model is warranted.