bims-rimeca Biomed News
on RNA methylation in cancer
Issue of 2020‒12‒27
three papers selected by
Sk Ramiz Islam
Saha Institute of Nuclear Physics

  1. Front Genet. 2020 ;11 602485
      RNA methylation accounts for over 60% of all RNA modifications, and N6-methyladenosine (m6A) is the most common modification on mRNA and lncRNA of human beings. It has been found that m6A modification occurs in microRNA, circRNA, rRNA, and tRNA, etc. The m6A modification plays an important role in regulating gene expression, and the abnormality of its regulatory mechanism refers to many human diseases, including cancers. Pitifully, as it stands there is a serious lack of knowledge of the extent to which the expression and function of m6A RNA methylation can influence prostate cancer (PC). Herein, we systematically analyzed the expression levels of 35 m6A RNA methylation regulators mentioned in literatures among prostate adenocarcinoma patients in the Cancer Genome Atlas (TCGA), finding that most of them expressed differently between cancer tissues and normal tissues with the significance of p < 0.05. Utilizing consensus clustering, we divided PC patients into two subgroups based on the differentially expressed m6A RNA methylation regulators with significantly different clinical outcomes. To appraise the discrepancy in total transcriptome between subgroups, the functional enrichment analysis was conducted for differential signaling pathways and cellular processes. Next, we selected five critical genes by the criteria that the regulators had a significant impact on prognosis of PC patients from TCGA through the last absolute shrinkage and selection operator (LASSO) Cox regression and obtained a risk score by weighted summation for prognosis prediction. The survival analysis curve and receiver operating characteristic (ROC) curve showed that this signature could excellently predict the prognosis of PC patients. The univariate and multivariate Cox regression analyses proved the independent prognostic value of the signature. In summary, our effort revealed the significance of m6A RNA methylation regulators in prostate cancer and determined a m6A gene expression classifier that well predicted the prognosis of prostate cancer.
    Keywords:  LASSO Cox regression; Ncpsdummy6-methyladenosine; RNA methylation; biomarker; consensus clustering; methyltransferase; prognostic signature; prostate adenocarcinoma
  2. Genomics. 2020 Dec 16. pii: S0888-7543(20)32073-5. [Epub ahead of print]
      N6-methyladenosine (m6A), the most prevalent epitranscriptomic modification in eukaryotes, is enriched in 3'-untranslated regions (3'UTRs) of mRNAs. As 3'UTRs are major binding sites of RNA-binding proteins (RBPs) and microRNAs (miRNAs), m6A-dependent local RNA structure change may alter the accessibility of RBPs and miRNAs to their target sites and regulate mRNA function. Using a human transcriptome-wide computational analysis to investigate the relation between m6A, RBPs and miRNAs, we find a strong positive correlation between number of m6A sites, miRNAs and RBPs binding to mRNAs, suggesting m6A-modified mRNAs are more targeted by miRNAs and RBPs. Moreover, m6A sites are located proximally to miRNA target sites and binding sites of multiple RBPs. Further, miRNA target sites and RBP-binding sites located close to each other are also located proximally to m6A. This study indicates three-way interplay between m6A, microRNA and RBP binding, suggesting the influence of mRNA modifications on the miRNA and RBP interactomes.
    Keywords:  Post-transcriptional regulation; RNA methylation; RNA structural switch; RNA-binding protein; m(6)A; microRNA
  3. Front Mol Biosci. 2020 ;7 577460
      Background: YTH domain family (YTHDF) 2 acts as a "reader" protein for RNA methylation, which is important in tumor regulation. However, the effect of YTHDF2 in liver hepatocellular carcinoma (LIHC) has yet to be elucidated.Methods: We explored the role of YTHDF2 in LIHC based on publicly available datasets [The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO)]. A bioinformatics approach was employed to analyze YTHDF2. Logistic regression analyses were applied to analyze the correlation between YTHDF2 expression and clinical characteristics. To evaluate the effect of YTHDF2 on the prognosis of LIHC patients, we used Kaplan-Meier (K-M) curves. Gene set enrichment analysis (GSEA) was undertaken using TCGA dataset. Univariate and multivariate Cox analyses were used to ascertain the correlations between YTHDF2 expression and clinicopathologic characteristics with survival. Genes co-expressed with YTHDF2 were identified and detected using publicly available datasets [LinkedOmics, University of California, Santa Cruz (UCSC), Gene Expression Profiling Interactive Analysis (GEPIA), and GEO]. Correlations between YTHDF2 and infiltration of immune cells were investigated by Tumor Immune Estimation Resource (TIMER) and GEPIA.
    Results: mRNA and protein expression of YTHDF2 was significantly higher in LIHC tissues than in non-cancerous tissues. High YTHDF2 expression in LIHC was associated with poor prognostic clinical factors (high stage, grade, and T classification). K-M analyses indicated that high YTHDF2 expression was correlated with an unfavorable prognosis. Univariate and multivariate Cox analyses revealed that YTHDF2 was an independent factor for a poor prognosis in LIHC patients. GSEA revealed that the high-expression phenotype of YTHDF2 was consistent with the molecular pathways implicated in LIHC carcinogenesis. Analyses of receiver operating characteristic curves showed that YTHDF2 might have a diagnostic value in LIHC patients. YTHDF2 expression was associated positively with SF3A3 expression, which implied that they may cooperate in LIHC progression. YTHDF2 expression was associated with infiltration of immune cells and their marker genes. YTHDF2 had the potential to regulate polarization of tumor-associated macrophages, induce T-cell exhaustion, and activate T-regulatory cells.
    Conclusion: YTHDF2 may be a promising biomarker for the diagnosis and prognosis of LIHC and may provide new directions and strategies for LIHC treatment.
    Keywords:  N6-methyladenosine (m6A) RNA methylation; YTHDF2; liver hepatocellular carcinoma; prognosis; tumor-immune infiltration