Increased expression of YTHDF1 and HNRNPA2B1 as potent biomarkers for melanoma: a systematic analysis

Abstract

Background: The incidence and mortality of melanoma is increasing around the world. To deeply explain the mechanism insight into it, we conducted a systematic analysis to examine the levels of regulatory genes of the common RNA epigenetic modification-N6-methyladenosine (m 6 A) in patients with melanoma compared by the healthy. Methods: We analyzed the expression of m 6 A Eraser, Writer, and Reader genes based on publicly available datasets on Oncomine and validated the results with a gene expression omnibus dataset. Hub genes were identified with Cytohubba and the frequency of copy number alterations was analyzed with the cBioPortal tool. Results: The results revealed the up-regulation of YTHDF1 and HNRNPA2B1 in melanoma. Combining the two genes improved the efficacy in diagnosing melanoma by about 10% compared to each gene alone. Hub genes identified with four analysis methods were compared and the overlapping genes were selected. These genes were enriched in several gene ontology terms. Genes related to p53-signaling consisted of CDK2, CDK1, RRM2, CCNB1, and CHEK1. All five genes were positively correlated with either YTHDF1 or HNRNPA2B1, suggesting that both genes may affect m 6 A modification by the five genes, further up-regulating their expression and facilitate their roles in inhibiting p53 to suppress tumorigenesis. We also observed major mutations in YTHDF1 and HNRNPA2B1 that led to their amplification in melanoma. Significant differences were observed in the clinical characteristics of patients with altered and unaltered m 6 A regulatory genes such as tumor stage and treatment response. Conclusions: We, for the first time, identified a combination of m 6 A regulatory genes to diagnose melanoma. We also analyzed m 6 A-related genes more comprehensively based on systematic complete data. We found that YTHDF1 and HNRNPA2B1 were altered in melanoma and might influence the development of the disease through signaling pathways such as p53.

Identification of a m6A RNA methylation regulators-based signature for predicting the prognosis of esophageal adenocarcinoma

“…Compared with the expression level in normal tissues, the expression of three genes, HNRNPA2B1, HNRNPC, and YTHDF1, were signi cantly up-regulated in EAC tissues with p<0.001. There was a similar phenomenon in a study on melanoma, where YTHDF1 and HNRNPA2B1 affected modi cation by genes related to p53-signaling, further up-regulating their expression and facilitated their roles in inhibiting p53 to suppress tumorigenesis [39]. The m6A demethylase FTO represented a relatively higher expression in normal tissues than in EAC tissues in this study (Fig.…”

N6-methyladenosine (m6A) RNA modification in cancer stem cells

“…49 Combined with the integrated analysis from GEO database, the significant elevation of HNRNPA2B1 and YTHDF1 was identified in melanoma. 49 ferred a worse OS and event-free survival. 54 Moreover, the authors detected that AML patients with mutated TP53 displayed the genetic alterations of more than one m6A regulator.…”

“…In brief, RNA methylation in CTCs may be used for noninvasive cancer diagnosis 48 . Recently, Li et al detected the expression level of m6A regulators in more than 500 melanoma samples using Oncomine 49 . Combined with the integrated analysis from GEO database, the significant elevation of HNRNPA2B1 and YTHDF1 was identified in melanoma 49 .…”

“…Recently, Li et al detected the expression level of m6A regulators in more than 500 melanoma samples using Oncomine 49 . Combined with the integrated analysis from GEO database, the significant elevation of HNRNPA2B1 and YTHDF1 was identified in melanoma 49 . Then, linear regression analysis testified that combined selection of m6A regulators markedly enhanced the diagnostic efficacy from 0.760 to 0.857, offering optimized method to distinguish melanoma patients based on m6A regulators 49 .…”

“…Combined with the integrated analysis from GEO database, the significant elevation of HNRNPA2B1 and YTHDF1 was identified in melanoma 49 . Then, linear regression analysis testified that combined selection of m6A regulators markedly enhanced the diagnostic efficacy from 0.760 to 0.857, offering optimized method to distinguish melanoma patients based on m6A regulators 49 . Shortly after, Zhuang et al built a 11‐m6A regulator diagnostic score model in lung cancer (KIAA1429, HNRNPC, RBM15, METTL3, ZC3H13, WTAP, YTHDF1, YTHDC1, FTO, YTHDF2, ALKBH5), with the area under the receiver operating characteristic curve (ROC) of 0.996 in the training group 49 .…”