Current Advances in N6-Methyladenosine Methylation Modification During Bladder Cancer

N6-methyladenosine (m6A) is a dynamic, reversible post-transcriptional modification, and the most common internal modification of eukaryotic messenger RNA (mRNA). Considerable evidence now shows that m6A alters gene expression, thereby regulating cell self-renewal, differentiation, invasion, and apoptotic processes. M6A methylation disorders are directly related to abnormal RNA metabolism, which may lead to tumor formation. M6A methyltransferase is the dominant catalyst during m6A modification; it removes m6A demethylase, promotes recognition by m6A binding proteins, and regulates mRNA metabolic processes. Bladder cancer (BC) is a urinary system malignant tumor, with complex etiology and high incidence rates. A well-differentiated or moderately differentiated pathological type at initial diagnosis accounts for most patients with BC. For differentiated superficial bladder urothelial carcinoma, the prognosis is normally good after surgery. However, due to poor epithelial cell differentiation, BC urothelial cell proliferation and infiltration may lead to invasive or metastatic BC, which lowers the 5-years survival rate and significantly affects clinical treatments in elderly patients. Here, we review the latest progress in m6A RNA methylation research and investigate its regulation on BC occurrence and development.

Intermittent Fasting Improves High-Fat Diet-Induced Obesity Cardiomyopathy via Alleviating Lipid Deposition and Apoptosis and Decreasing m6A Methylation in the Heart

Intermittent fasting (IF) plays an essential role in improving lipid metabolism disorders caused by metabolic cardiomyopathy. Growing evidence revealed that N6-methyladenosine (m6A) RNA methylation is related to obesity and lipid metabolic. Our study aimed to assess the beneficial effects of IF on lipid deposition, apoptosis, and m6A methylation in high-fat diet (HFD)-induced obesity cardiomyopathy. Male C57BL/6J mice were fed a normal diet (ND) or HFD ad libitum for 13 weeks, after which time a subgroup of HFD mice were subjected to IF for 24 h and fed HFD in the other day for 8 weeks. We found that IF intervention significantly improved cardiac functional and structural impairment and serum lipid metabolic disorder induced by HFD. Furthermore, IF intervention decreased the mRNA levels of the fatty acid uptake genes of FABP1, FATP1, and CD36 and the fatty acid synthesis genes of SREBF1, FAS, and ACCα and increased the mRNA levels of the fatty acid catabolism genes of ATGL, HSL, LAL, and LPL in cardiac tissueof HFD-induced obese mice. TUNEL-positive cells, Bax/Bcl-2 ratio, and Cleaved Caspase-3 protein expression in HFD-induced obese mice hearts was down-regulated by IF intervention. In addition, IF intervention decreased the m6A methylation levels and METTL3 expression and increased FTO expression in HFD-induced obesity cardiomyopathy. In conclusion, our findings demonstrate that IF attenuated cardiac lipid deposition and apoptosis, as well as improved cardiac functional and structural impairment in HFD-induced obesity cardiomyopathy, by a mechanism associated with decreased m6A RNA methylation levels.

The Role of m6A RNA Methylation-Related lncRNAs in the Prognosis and Tumor Immune Microenvironment of Papillary Thyroid Carcinoma

Emerging evidence has indicated that N6-methylandenosine (m6A) RNA methylation plays a critical role in cancer development. However, the function of m6A RNA methylation-related long noncoding RNAs (m6A-lncRNAs) in papillary thyroid carcinoma (PTC) has never been reported. This study aimed to investigate the role of m6A-lncRNAs in the prognosis and tumor microenvironment (TME) of PTC. Three subgroups (clusters 1, 2, and 3) were identified by consensus clustering of 19 prognosis-related m6A-lncRNA regulators, of which cluster 1 is preferentially related to unfavorable prognosis, lower immune scores, and distinct immune infiltrate level. A risk-score model was established based on 8 prognosis-related m6A-lncRNAs. Patients with a high-risk score showed a worse prognosis, and the ROC indicated a reliable prediction performance for patients with PTC (AUC = 0.802). As expected, the immune scores, the infiltration levels of immune cells, and ESTIMATE scores in the low-risk subgroups were notably higher (p < 0.001) when compared with those in high-risk subgroups. Furthermore, GSEA analysis revealed that tumor associated pathways, hallmarks, and biological processes were remarkably enriched in the high-risk subgroup. Further analysis indicated that the risk score and age were independent prognostic factors for PTC. An integrated nomogram was constructed that accurately predicted the survival status (AUC = 0.963). Moreover, a lncRNA–miRNA–mRNA regulated network was established based on seven prognosis-related m6A-lncRNAs. In addition, 30 clinical samples and different PTC cells were validated. This is the first study to reveal that m6A-lncRNAs plays a vital role in the prognosis and TME of PTC. To a certain degree, m6A-lncRNAs can be considered as new, promising prognostic biomarkers and treatment targets.

The m6A methyltransferase METTL3 affects autophagy and progression of nasopharyngeal carcinoma by regulating the stability of lncRNA ZFAS1

Background Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the epithelial cells of the nasopharyngeal mucosa of the head and neck. The role of long non-coding RNA and RNA methylation in NPC has received increasing attention. Therefore, this study aims to investigate the mechanism of lncRNA ZFAS1 in NPC and its relationship with RNA methylation, providing evidence for targeted therapy of NPC. Methods Microarray arrays were used to screen the differentially expressed miRNAs in normal tissues and tumor tissues. QRT-PCR was used to quantify ZFAS1, miR-100-3p, ATG10, autophagy and epithelial-mesenchymal transition related genes. The interactive relationship between ZFAS1 and miR-100-3p was verified using dual-luciferase reporter gene assay and RIP assay. CCK-8, transwell and apoptosis were used to detect the occurrence of tumor cells after different treatments. The m6A modification test is used to verify the effect of METTL3 on ZFAS1. BALB/c mice and BALB/c nude mice are used to detect the effects of different treatments on tumor growth and immune escape in vivo. Results ZFAS1 is upregulated in tumor tissues and NPC cells. N (6)-methyladenosine (m6A) is highly enriched in ZFAS1 and enhances its RNA stability. ZFAS1 is used as an oncogenic lncRNA, which can promote NPC cell proliferation, migration and tumor growth. In terms of mechanism, ZFAS1 up-regulates the expression of ATG10 by competitively adsorbing miR-100-3p and regulates the level of autophagy by inhibiting the PI3K/Akt signaling pathway to promote the proliferation and migration of NPC cells. Conclusion In short, our study verified the cancer-promoting effect of ZFAS1 in NPC and explained part of the reason for its upregulation. In addition, we confirmed that ZFAS1 can regulate the autophagy level of NPC cells through the PI3K/AKT pathway through miR-100-3p/ATG10 to affect tumor progression.

Comprehensive Analyses of the Expression, Genetic Alteration, Prognosis Significance, and Interaction Networks of m6A Regulators Across Human Cancers

Accumulating lines of evidence indicate that the deregulation of m6A is involved in various cancer types. The m6A RNA methylation is modulated by m6A methyltransferases, demethylases, and reader proteins. Although the aberrant expression of m6A RNA methylation contributes to the development and progression of multiple cancer types, the roles of m6A regulators across numerous types of cancers remain largely unknown. Here, we comprehensively investigated the expression, genetic alteration, and prognosis significance of 20 commonly studied m6A regulators across diverse cancer types using TCGA datasets via bioinformatic analyses. The results revealed that the m6A regulators exhibited widespread dysregulation, genetic alteration, and the modulation of oncogenic pathways across TCGA cancer types. In addition, most of the m6A regulators were closely relevant with significant prognosis in many cancer types. Furthermore, we also constructed the protein–protein interacting network of the 20 m6A regulators, and a more complex interacting regulatory network including m6A regulators and their corresponding interacting factors. Besides, the networks between m6A regulators and their upstream regulators such as miRNAs or transcriptional factors were further constructed in this study. Finally, the possible chemicals targeting each m6A regulator were obtained by bioinformatics analysis and the m6A regulators–potential drugs network was further constructed. Taken together, the comprehensive analyses of m6A regulators might provide novel insights into the m6A regulators’ roles across cancer types and shed light on their potential molecular mechanisms as well as help develop new therapy approaches for cancers.

Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types

Background: The role of N6-methyladenine (m6A) RNA methylation in a variety of biological processes is gradually being revealed. Methods: Here, we systematically describe the correlation between the expression pattern of m6A RNA methylation regulatory factors and clinical phenotype, immunity, drug sensitivity, stem cells and prognosis in more than 10,000 samples of 33 types of cancer. Results: The results show that there are significant differences in the expression of 20 m6A RNA methylation regulatory factors in different cancers, and there was a significant correlation with the analysis indicators. Conclusion: In this study, the m6A RNA methylation regulatory factor was found not only to potentially assist in stratifying the prognosis but also to predict or improve the sensitivity of clinical drug therapy.

Predicting genes associated with RNA methylation pathways using machine learning

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function.