m6A Regulators Research Articles

Identification of clinical prognostic features of esophageal cancer based on m6A regulators.

BackgroundEsophageal cancer (ESCA) is a common malignancy with high morbidity and mortality. n6-methyladenosine (m6A) regulators have been widely recognized as one of the major causes of cancer development and progression. However, for ESCA, the role of regulators is unclear. The aim of this study was to investigate the role of m6A RNA methylation regulators in the immune regulation and prognosis of ESCA.MethodsRNA-seq data were downloaded using the Cancer Genome Atlas (TCGA) database, and the expression differences of m6A RNA methylation regulators in ESCA were analyzed. Further m6A methylation regulator markers were constructed, and prognostic and predictive values were assessed using survival analysis and nomograms. Patients were divided into low-risk and high-risk groups. The signature was evaluated in terms of survival, single nucleotide polymorphism (SNP), copy number variation (CNV), tumor mutation burden (TMB), and functional enrichment analysis (TMB). The m6A expression of key genes in clinical specimens was validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsIn ESCA tissues, most of the 23 regulators were significantly differentially expressed. LASSO regression analysis included 7 m6A-related factors (FMR1, RBMX, IGFBP1, IGFBP2, ALKBH5, RBM15B, METTL14). In addition, this study also identified that the risk model is associated with biological functions, including base metabolism, DNA repair, and mismatch repair. In this study, a nomogram was created to predict the prognosis of ESCA patients. Bioinformatics analysis of human ESCA and normal tissues was performed using qRT-PCR. Finally. Seven genetic features were found to be associated with m6A in ESCA patients. The results of this study suggest that three different clusters of m6A modifications are involved in the immune microenvironment of ESCA, providing important clues for clinical diagnosis and treatment.

Significance of m6A regulatory factor in gene expression and immune function of osteoarthritis.

One of the most prevalent posttranscriptional modifications of eukaryotic mRNA is the RNA N6-methyladenosine (m6A) regulator, which plays a significant role in various illnesses. The involvement of m6A regulators in osteoarthritis (OA) is not fully known. By comparing nonosteoarthritic and osteoarthritic patients, 26 important m6A regulators were identified from the gene expression omnibus GSE48556 dataset. Seven candidate m6A regulators (IGFBP3, WTAP, IGFBP1, HNRNPC, RBM15B, YTHDC1, and METTL3) were screened using a random forest model to assess the likelihood of OA. A column line graph model founded on seven m6A modulator candidates was created. According to decision curve analysis, patients might profit from the column line graph model. Based on chosen relevant m6A modifiers, a consensus clustering approach was utilized to categorize OA into two m6A categories (group A and group B). To measure the m6A pattern, a principal component analysis technique was created to generate the m6A score for every sample. Cluster A patients exhibited more excellent m6A scores than cluster B patients. Furthermore, we discovered that patients with lower and higher m6A scores had varied immunological responses using the m6A type. At last, m6A regulators contribute significantly to the progression of OA. Our research on m6A patterns might help to guide further OA immunotherapeutic techniques.

Identification of molecular patterns and diagnostic biomarkers in juvenile idiopathic arthritis based on the gene expression of m6A regulators.

The role of N6-methyladenosine modification in immunity is increasingly being appreciated. However, the landscape of m6A regulators in juvenile idiopathic arthritis (JIA) is poorly understood. Thus, this study explored the impact of m6A modification and related lncRNAs in JIA immune microenvironment. Fourteen m6A regulators and eight lncRNAs were identified as potential diagnostic biomarkers for JIA. Two diagnostic models for JIA were also constructed. The putative molecular regulatory mechanism of FTO-mediated m6A modification in JIA was hypothesized. Three distinct m6A patterns mediated by 26 m6A regulators and three diverse lncRNA clusters mediated by 405 lncRNAs were thoroughly investigated. They exhibited dramatically diverse immune microenvironments and expression of HLA genes. The identification of two separate subtypes of enthesitis-related arthritis implies that our work may aid in the establishment of a more precise categorization system for JIA. m6A modification-related genes were obtained, and their underlying biological functions were explored. The m6Ascore system developed for individual JIA patients may be utilized to evaluate the immunological state or molecular pattern, thereby offering therapy recommendations. In short, through the investigation of the m6A regulators in JIA, the current work may contribute to our knowledge of the pathophysiology of JIA.

Molecular subtype identification and predictive power of N6-methyladenosine regulator in unexplained recurrent pregnancy loss.

The etiology of recurrent pregnancy loss (RPL) is complicated and effective clinical preventive measures are lacking. Identifying biomarkers for RPL has been challenging, and to date, little is known about the role of N6-methyladenosine (m6A) regulators in RPL. Expression data for m6A regulators in 29 patients with RPL and 29 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. To establish a diagnostic model for unexplained RPL, differential gene expression analysis was conducting for 36 m6A regulators using least absolute shrinkage and selection operator (LASSO) regression. Unsupervised cluster analysis was conducted on hub genes, and probable mechanisms were explored using gene set enrichment analysis (GSEA) and gene ontology (GO) analysis. Correlations between m6A-related differentially expressed genes and immune infiltration were analyzed using single-sample GSEA. A total of 18 m6A regulators showed significant differences in expression in RPL: 10 were upregulated and eight were downregulated. Fifteen m6A regulators were integrated and used to construct a diagnostic model for RPL that had good predictive efficiency and robustness in differentiating RPL from control samples, with an overall area under the curve (AUC) value of 0.994. Crosstalk was identified between 10 hub genes, miRNAs, and transcription factors (TFs). For example, YTHDF2 was targeted by mir-1-3p and interacted with embryonic development-related TFs such as FOXA1 and GATA2. YTHDF2 was also positively correlated with METTL14 (r = 0.5983, p < 0.001). Two RPL subtypes (Cluster-1 and Cluster-2) with distinct hub gene signatures were identified. GSEA and GO analysis revealed that the differentially expressed genes were mainly associated with immune processes and cell cycle signaling pathway (normalized enrichment score, NES = -1.626, p < 0.001). Immune infiltration was significantly higher in Cluster-1 than in Cluster-2 (p < 0.01). In conclusion, we demonstrated that m6A modification plays a critical role in RPL. We also developed and validated a diagnostic model for RPL prediction based on m6A regulators. Finally, we identified two distinct RPL subtypes with different biological processes and immune statuses.

Modification of m6A mediates tissue immune microenvironment in calcific aortic valve disease.

BackgroundSeveral human diseases are associated with aberrant expression of regulators involved in N6-methyladenosine (m6A) RNA modification. However, their role in aortic valve calcification (AVC) is largely unknown. The aim of this study was to determine the general expression pattern and potential function of m6A regulators in AVC by bioinformatics methods.MethodsWe obtained AVC datasets from the Gene Expression Omnibus (GEO). The identification of m6A-related differentially expressed genes (DEGs) and the Consensus Clustering method was performed to type AVC individuals based DEGs. Then, we quantified the effect of typing by principal component analysis (PCA). Next, we performed the weighted gene co-expression network analysis (WGCNA) and identified the main modules as well as functional analysis. Additionally, the key genes were screened by protein-protein interaction network (PPIN) analysis and identifying important genes of important modules. We again typed AVC individuals by the same method using key genes. Finally, we evaluated the link between key genes and immune infiltration.ResultsWe discovered that METTL14, ZC3H13, FTO, FMR1, HNRNPA2B1, HNRNPC, LRPPRC, YTHDC1, YTHDC2, and YTHDF1 expression levels decreased considerably in AVC tissues. Based on 10 genes, we typed 240 AVC samples as clusters A and B. We assessed the immune cell content in 240 samples using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and found that B cell memory, CD8 T cells, T follicular helper cells, monocytes, M0 macrophages, resting dendritic cells (DCs), and interleukin-10 (IL-10) were concentrated in the cluster A group. Additionally, based on the important WGCNA modules, we identified 7 key genes. Next, 240 samples were retyped based on 7 key genes; we found that T cells CD8, T cells CD4 memory activated, T cells follicular helper, and macrophages M1 were significantly increased in gene cluster-1. Finally, we performed functional enrichment of gene cluster-typed samples, showing potential functional differences between different types.ConclusionsOur study provides a review of the m6A regulators’ expression pattern and functional importance in human AVC. The data from this study might serve as a significant resource for future mechanistic and therapeutic investigations into the role of critical m6A regulators in AVC.

Gene signature of m6A-related targets to predict prognosis and immunotherapy response in ovarian cancer.

The aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer. The gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer. CBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy. We comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients' survival time and visualize the prediction results.

New roles of N6-methyladenosine methylation system regulating the occurrence of non-alcoholic fatty liver disease with N6-methyladenosine-modified MYC.

Non-alcoholic fatty liver disease (NAFLD) has become a major chronic disease in contemporary society, affected by N6-methyladenosine (m6A) RNA methylation, one of the most common RNA modifications. Compared with healthy control, m6A RNA methyltransferase 3 (METTL3) and METTL14 increased, while Wilms tumor 1-associated protein (WTAP) and RNA-binding motif protein 15 (RBM15) decreased significantly in NAFLD, and the m6A demethylases fat mass and obesity-associated protein (FTO) elevated. Meanwhile, the m6A binding proteins, YT521-B homology (YTH) domain-containing 1 (YTHDC1), YTHDC2, insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), heterogeneous nuclear ribonucleoprotein C (HNRNPC), and HNRNPA2B1 were decreased, while eukaryotic translation initiation factor 3 subunit H (EIF3H) was increased significantly. All these changes of m6A regulators had significant differences between healthy control and NAFLD, but no differences between the NAFL and NASH group. The expression level of RBM15, HNRNPC, and HNRNPA2B1 were related to body fat index. RBM15, YTHDC2, HNRNPC, HNRNPA2B1, and EIF3H were related to steatosis. Also, KIAA1429 and YTH domain family 1 (YTHDF1) were related to lobular inflammation. Taken together, m6A regulators were involved in the occurrence of NAFLD. More importantly, abnormal MYC was determined as a key link to m6A regulation of NAFLD. The higher MYC mRNA level was accompanied by higher HDL cholesterol and unsaturated fatty acid proportions, as well as lower fat mass, glucose, and transaminase. Taken together, dysregulation of m6A methylation caused steatosis and fibrosis, affecting the occurrence of NAFLD, and MYC might be its potential target.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis.

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

Comprehensive characterization of tumor microenvironment and m6A RNA methylation regulators and its effects on PD-L1 and immune infiltrates in cervical cancer.

Understanding the role of N6-adenosine methylation (m6A) in the tumor microenvironment (TME) is important since it can contribute to tumor development. However, the research investigating the association between m6A and TME and cervical cancer is still in its early stages. The aim of this study was to discover the possible relationship between m6A RNA methylation regulators, TME, PD-L1 expression levels, and immune infiltration in cervical cancer. We gathered RNA-seq transcriptome data and clinical information from cervical cancer patients using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. To begin, researchers assessed the differences in m6A regulatory factor expression levels between cervical cancer and normal tissues. Clustering analysis was adapted to assess PD-L1 expression, immunological score, immune cell infiltration, TME, and probable pathways in cervical cancer samples. The majority of m6A regulators were found to be considerably overexpressed in cervical cancer tissues. Using consensus clustering of 21 m6A regulators, we identified two subtypes (clusters 1/2) of cervical cancer, and we found that WHO stage and grade were associated with the subtypes. PD-L1 expression increased dramatically in cervical cancer tissues and was significantly linked to ALKBH5, FTO, METTL3, RBM15B, YTHDF1, YTHDF3, and ZC3H13 expression levels. Plasma cells and regulatory T cells (Tregs) were considerably elevated in cluster 2. Cluster 1 is involved in numerous signature pathways, including basal transcription factors, cell cycle, RNA degradation, and the spliceosome. The prognostic signature-based riskscore (METTL16, YTHDF1, and ZC3H13) was found to be an independent prognostic indicator of cervical cancer. The tumor immune microenvironment (TIME) was linked to m6A methylation regulators, and changes in their copy number will affect the quantity of tumor-infiltrating immune cells dynamically. Overall, our research discovered a powerful predictive signature based on m6A RNA methylation regulators. This signature correctly predicted the prognosis of cervical cancer patients. The m6A methylation regulator could be a critical mediator of PD-L1 expression and immune cell infiltration, and it could have a significant impact on the TIME of cervical cancer.

Crosstalk between m6A regulators and mRNA during cancer progression.

m6A modification, the most abundant and widespread RNA modification, is present and involved in the occurrence and development of various cancers. To date, most studies have mainly focused on the roles of a single m6A regulator (writer/eraser/reader) in various cancers, but cumulative evidence shows that aberrant m6A regulators and m6A levels exert dual effects (promoting and/or inhibiting roles) in cancer progression. Recently, studies have investigated the direct interactions between different m6A regulators (writer/eraser and reader) and mRNAs in a variety of cancers. In this review, we summarize the functions of m6A regulators and their roles in various types of cancers. We further propose the possible crosstalk mechanisms (Writer-m6A-Reader-mRNA axis and Eraser-m6A-Reader-mRNA axis) between different m6A regulators and mRNAs during cancer progression. We also discuss the clinical potential of m6A regulator‑targeting strategies.

M6A regulator-mediated RNA methylation modification patterns are involved in immune microenvironment regulation of coronary heart disease.

BackgroundAlthough the roles of m6A modification in the immune responses to human diseases have been increasingly revealed, their roles in immune microenvironment regulation in coronary heart disease (CHD) are poorly understood.MethodsThe GSE20680 and GSE20681 datasets related to CHD were acquired from the Gene Expression Omnibus (GEO) database. A total of 30 m6A regulators were used to perform LASSO regression to identify the significant genes involved in CHD. Unsupervised clustering analysis was conducted using the m6A regulators to distinguish the m6A RNA methylation patterns in patients with CHD. The differentially expressed genes (DEGs) and biological characteristics, including GO and KEGG enrichment results, were assessed for the different m6A patterns to analyse the impacts of m6A regulators on CHD. Hub genes were identified, and subsequent microRNAs-mRNAs (miRNAs–mRNAs) and mRNAs-transcriptional factors (mRNA-TFs) interaction networks were constructed by the protein and protein interaction (PPI) network method using Cytoscape software. The infiltrating proportion of immune cells was assessed by ssGSEA and the CIBERSORT algorithm. Quantitative real-time PCR (qRT-PCR) was performed to detect the expression of the significant m6A regulators and hub genes.ResultsFour of 30 m6A regulators (HNRNPC, YTHDC2, YTHDF3, and ZC3H13) were identified to be significant in the development of CHD. Two m6A RNA methylation clusters were distinguished by unsupervised clustering analysis based on the expression of the 30 m6A regulators. A total of 491 genes were identified as DEGs between the two clusters. A PPI network including 308 mRNAs corresponding to proteins was constructed, and 30 genes were identified as hub genes that were enriched in the bioprocesses of peptide cross-linking, keratinocyte differentiation. Twenty-seven hub genes were found to be related to miRNAs, and seven hub genes were found to be related to TFs. Moreover, among the 30 hub genes, eight genes were found to be upregulated in CHD, and three were found to be downregulated in CHD compared to the normal people. The high m6A modification pattern was associated with a higher infiltrated abundance of immune cells.ConclusionOur findings demonstrated that m6A modification plays crucial roles in the diversity and complexity of the immune microenvironment in CHD.

Identification of hub biomarkers and exploring the roles of immunity, M6A, ferroptosis, or cuproptosis in rats with diabetic erectile dysfunction.

Currently, patients with diabetic erectile dysfunction (DMED) were not satisfied with the effects of first-line phosphodiesterase type 5 inhibitors (PDE5Is). Hence, this paper was designed to mine hub biomarkers in DMED and explore its potential mechanisms. Gene expression matrix of DMED was downloaded from the gene expression omnibus (GEO; GSE2457) dataset. The top 20 genes were selected based on the connectivity degrees in protein-protein interaction (PPI) network. Functional enrichment analysis was utilized to reveal DMED-related signaling pathways. We also explored the roles of immunity, m6A, ferroptosis, or cuproptosis in DMED and constructed Sprague Dawley (SD) rats DMED model to verify gene expressions by quantitative real-time polymerase chain reaction (qRT-PCR). Based on the threshold, a total of 122 differently expressed genes (DEGs) were identified in DMED, including 39 up-regulated and 83 down-regulated genes. Functional enrichment analysis implied that these DEGs were significantly enriched in peroxisome proliferator-activated receptors, ferroptosis, hypoxia-inducible factor 1 signaling pathways, and so on. SD rats DMED model was also successfully established by us and validated by intracavernous pressure/mean arterial pressure, Masson's trichrome staining, and immunohistochemical analysis. We further verified the expression of these top 20 genes from the PPI network by qRT-PCR in the SD rats DMED model and finally identified Sparc, Lox, Srebf1, and Mmp3 as hub biomarkers (all p<0.05). As for immunity and cuproptosis, our analysis indicated that DMED had nothing to do with them (all p>0.05). Actually, DMED was markedly associated with m6A regulators and ferroptosis. We identified Sparc, Lox, Srebf1, and Mmp3 as potential hub biomarkers in the SD rats DMED model for future drug development and found its significant associations with m6A regulators and ferroptosis, but not with immunity or cuproptosis.

Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators in the Diagnosis and Subtype Classification of Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is still a huge danger to human health. Sensitive markers are necessary for the prediction of the risk of AMI and would be beneficial for managing the incidence rate. N6-methyladenosine (m6A) RNA methylation regulators have been confirmed to be involved in the development of various diseases. However, their function in AMI has not been fully elucidated. The purpose of this study was to determine the expression of m6A RNA methylation regulators in AMI as well as their possible functions and prognostic values. The GEO database was used to get the gene expression profiles of patients with and without AMI, and bioinformatics assays of genes with differently expressed expression were performed. We establish two separate m6A subtypes, and relationships between subtypes and immunity were studied. In this study, we identified IGF2BP1, FTO, RBM15, METTL3, YTHDC2, FMR1, and HNRNPA2B1 as the seven major m6A regulators. A nomogram model was developed and confirmed. The consensus clustering algorithm was conducted to categorize AMI patients into two m6A subtypes from the identified m6A regulators. Patients who have activated T-cell activities were found to be in clusterA; they may have a better prognosis as a result. Importantly, we found that patients with high METTL3 expressions had an increased level of Activated.CD4.T.cell and Type.2.T.helper.cell, while having a decreased level of CD56bright.natural.killer.cell, Macrophage, Monocyte, Natural.killer.cell, and Type.17.T.helper.cell. Overall, a diagnostic model of AMI was established based on the genes of IGF2BP1, FTO, RBM15, METTL3, YTHDC2, FMR1, and HNRNPA2B1. Our investigation of m6A subtypes may prove useful in the developments of therapy approaches for AMI.

Dysregulation and implications of N6-methyladenosine modification in renal cell carcinoma.

Increasing evidence indicates that N6-methyladenosine (m6A) methylation modification serves important functions in biological metabolism. Dysregulation of m6A regulators is related to the progression of different malignancies, including renal cell carcinoma (RCC). Recent studies have reported preliminary findings on the influence of m6A regulator dysregulation on RCC tumorigenesis and development. However, no comprehensive review that integrates and analyzes the roles of m6A modification in RCC has been published to date. In this review, we focus on the dysregulation of m6A regulators as it relates to RCC tumorigenesis and development, as well as possible applications of m6A modification in RCC diagnosis and therapeutics.

M6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer.

Simple SummaryRecurrence and metastatic progression always lead to dismal outcomes in prostate cancer (PCa). There is no reliable biomarker for the prediction of recurrence and metastasis other than the Prostate Cancer Antigen (PCA). N6-methyladenosine (m6A) is the most common post-transcriptional mRNA modification and is regulated by m6A regulators dynamically. Since m6A modification is associated with cancer development and outgrowth, we performed a consensus clustering on PCa with regard to the gene expression of all m6A regulators. We identified three subtypes of Pca with distinct m6A expression patterns and enriched biological pathways. We also established an m6A score for metastasis prediction based on our clustering, which is potentially a predictive biomarker for Pca metastasis.Prostate cancer (PCa) is one of the most common cancers in men. Usually, most PCas at initial diagnosis are localized and hormone-dependent, and grow slowly. Patients with localized PCas have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive PCa is still dismal. N6-methyladenosine (m6A) is the most common post-transcriptional mRNA modification and is dynamically regulated by m6A regulators. A few studies have shown that the abnormal expression of m6A regulators is significantly associated with cancer progression and immune cell infiltration, but the roles of these regulators in PCa remain unclear. Here, we examined the expression profiles and methylation levels of 21 m6A regulators across the Cancer Genome Atlas (TCGA), 495 PCas by consensus clustering, and correlated the expression of m6A regulators with PCa progression and immune cell infiltration. Consensus clustering was applied for subtyping Pca samples into clusters based on the expression profiles of m6A regulators. Each subtype’s signature genes were obtained by a pairwise differential expression analysis. Featured pathways of m6A subtypes were predicted by Gene Ontology. The m6A score was developed to predict m6A activation. The association of the m6A score with patients’ survival, metastasis and immune cell infiltration was also investigated. We identified three distinct clusters in PCa based on the expression profiles of 21 m6A regulators by consensus clustering. The differential expression and pathway analyses on the three clusters uncovered the m6A regulators involved in metabolic processes and immune responses in PCa. Moreover, we developed an m6A score to evaluate the m6A regulator activation for PCa. The m6A score is significantly associated with Gleason scores and metastasis in PCa. The predictive capacity of the m6A score on PCa metastasis was also validated in another independent cohort with an area under the curve of 89.5%. Hence, our study revealed the critical role of m6A regulators in PCa progression and the m6A score is a promising predictive biomarker for PCa metastasis.

Predictive value of drug efficacy by M6A modification patterns in rheumatoid arthritis patients.

BackgroundRheumatoid arthritis is a highly heterogeneous autoimmune disease characterized by unpredictable disease flares and significant differences in therapeutic response to available treatments. One possible reason for poor efficacy is that it cannot be treated accurately due to no optimal stratification for RA patients.ObjectiveThis study aims to construct an RA classification model by m6A characters and further predict response to medication.MethodsTwenty m6A regulators were used to construct a random forest diagnosis model, and RNA-seq analysis was employed for external validation. The RNA modification patterns mediated by 20 m6A regulators were systematically evaluated in 1191 RA samples and explored different molecular clusters associated with other immune microenvironment characteristics and biological pathways. Then, we established an m6A score model to quantify the m6A modification patterns. The model was applied to patients at baseline to test the association between m6Ascore and infliximab responsiveness.ResultsThe m6A diagnosis model showed good discriminatory ability in distinguishing RA. Patients with RA were classified into three clusters with distinct molecular and cellular signatures. Cluster A displayed strongly activated inflammatory cells and pathways. Specific innate lymphocytes occupied cluster B. Cluster C was mainly enriched in prominent adaptive lymphocytes and NK-mediated cytotoxicity signatures with the highest m6A score. Patients with a low m6Ascore exhibited significantly infliximab therapeutic benefits compared with those with a high m6Ascore (p< 0.05).ConclusionOur study is the first to provide a comprehensive analysis of m6A modifications in RA, which provides an innovative patient stratification framework and potentially enables improved therapeutic decisions.

M6A regulator-mediated methylation modification highlights immune infiltration patterns for predicting risk in hepatocellular carcinoma.

Increasing studies have demonstrated the biological function of RNA N6-methyladenosine (m6A) modifications in tumorigenesis. However, the potential role of m6A modifications in the tumor immune microenvironment (TIME) of hepatocellular carcinoma (HCC) remains unclear. Herein, 23 m6A regulators were fetched and introduced into consensus clustering to identify distinct m6A modification patterns and develop m6A-based molecular signatures. Then, a principal component analysis algorithm was employed to construct an m6A-based scoring system to further quantify m6A modification patterns in individual tumors. Immunophenoscore (IPS) was used to estimate the immunotherapeutic response of patients. Three different m6A modification patterns with distinct prognoses and biological signatures were identified among 611 HCC samples. The TIME characteristics of these three patterns were consistent with three known immune profiles: immune-oasis, immune-excluded, and immune-inflamed phenotypes. Identifying m6A modification patterns within individual tumors based on the m6Ascore, developed under the m6A-related signature genes, contributed to elaborating biological processes, clinical outcomes, immune cell infiltration, immunotherapeutic effects, and genetic variations. The low-m6Ascore subtype, characterized by immunosuppression, suggested an immune-suppressed phenotype and a low probability of benefiting from immunotherapy. Finally, the potential function of PRDM4 in HCC was explored. This study comprehensively elucidated the indispensable role of m6A modification patterns in the complexity of TIME. The quantitative identification of m6A modification patterns in individual tumors will contribute to optimizing precision immunotherapy.

Identification of m6A- and ferroptosis-related lncRNA signature for predicting immune efficacy in hepatocellular carcinoma.

BackgroundN6-methyladenosine (m6A) methylation and ferroptosis assist long noncoding RNAs (lncRNAs) in promoting immune escape in hepatocellular carcinoma (HCC). However, the predictive value of m6A- and ferroptosis-related lncRNAs (mfrlncRNAs) in terms of immune efficacy remains unknown.MethodA total of 365 HCC patients with complete data from The Cancer Genome Atlas (TCGA) database were used as the training cohort, and half of them were randomly selected as the validation cohort. A total of 161 HCC patients from the International Cancer Genome Consortium (ICGC) database were used as external validation (ICGC cohort).ResultsWe first identified a group of specific lncRNAs associated with both m6A regulators and ferroptosis-related genes and then constructed prognosis-related mfrlncRNA pairs. Based on this, the mfrlncRNA signature was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Notably, the risk score of patients was proven to be an independent prognostic factor and was better than the TNM stage and tumor grade. Moreover, patients with high-risk scores had lower survival rates, higher infiltration of immunosuppressive cells (macrophages and Tregs), lower infiltration of cytotoxic immune cells (natural killer cells), poorer immune efficacy (both immunophenoscore and score of tumor immune dysfunction and exclusion), higher IC50, and enrichment of the induced Treg pathway, which confirmed that the mfrlncRNA signature contributed to survival prediction and risk stratification of patients with HCC.ConclusionsThe mfrlncRNA signature, which has great prognostic value, provides new clues for identifying “cold” and “hot” tumors and might have crucial implications for individualized therapy to improve the survival rate of patients with HCC.

Novel insight into m6A regulator-mediated methylation modification patterns and immune characteristics in intracranial aneurysm.

BackgroundGrowing evidence demonstrated that m6A modification in cardiovascular diseases. However, how it is involved in the intracranial aneurysm (IA) is still unclear. This study aimed to identify the role of m6A modification in IA.MethodsThree datasets downloaded from the Gene Expression Omnibus (GEO) database were used, including GSE122897, GSE15629, and GSE3679. The landscapes of 24 m6A regulators were depicted using the STRING database, Pearson’s correlation analysis, and Wilcoxon test. The targets of differentially expressed m6A (DEm6A) were predicted in the m6A2Target database and the modification m6A sites of hub targets were identified in SRAMP online tool. A diagnostic model based on DEm6A was constructed and verified in training and test databases. A consensus clustering algorithm was performed to classify IA patients into distinct m6A-related clusters. Functional analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis, and gene set enrichment analysis analyses were conducted to elucidate the underlying mechanisms. ssGSEA algorithm was performed to uncover the immune characteristics. A PCA method was adopted to quantify the m6A score.ResultsNine DEm6A (IGF2BP1, IGF2BP3, YTHDF2, ZNF217, RBM15, YTHDF3, YTHDC1, FTO, and LRPPRC) significantly differed between IA and controls. Biological annotations showed that immune-related pathways (such as complement activation, inflammatory response, and interleukin signaling) and apoptosis were more enriched in IAs than in controls. Immune analyses indicate that the abundance of immune cells, immune responses, and HLA gene expression were elevated in IA samples than in controls. PCA results showed that IA has a lower m6A score than controls. An immune/apoptosis-related network modified by DEm6A was constructed. The m6A sites of six hub targets (CDK1, ASPM, AURKB, BUB1B, MKI67, and TPX2) were predicted with very high confidence. A diagnostic model with four genes (LRPPRC, YTHDF3, IGF2BP1, and ZNF217) was constructed and verified. Two m6A modification subtypes were identified with unsupervised cluster analysis. Immune infiltration analysis revealed that cluster 1 had higher immune activation than cluster 2. Further study showed that cluster 1 had a larger proportion of ruptured IAs.ConclusionThe m6A modification may shape the IAs microenvironment and participates in the formation and rupture of IAs by regulating immune infiltration.

Comprehensive analysis of the m6A-related molecular patterns and diagnostic biomarkers in osteoporosis.

BackgroundN6-methyladenosine (m6A) modification is a critical epigenetic modification in eukaryotes and involves several biological processes and occurrences of diseases. However, the roles and regulatory mechanisms of m6A regulators in osteoporosis (OP) remain unclear. Thus, the purpose of this study is to explore the roles and mechanisms of m6A regulators in OP.MethodsThe mRNA and microRNA (miRNA) expression profiles were respectively obtained from GSE56815, GSE7158, and GSE93883 datasets in Gene Expression Omnibus (GEO). The differential expression of 21 m6A regulators between high-bone mineral density (BMD) and low-BMD women was identified. Then, a consensus clustering of low-BMD women was performed based on differentially expressed (DE)-m6A regulators. The m6A-related differentially expressed genes (DEGs), the differentially expressed miRNAs (DE-miRNAs), and biological functions were investigated. Moreover, a weighted gene co-expression network analysis (WGCNA) was constructed to identify the OP-related hub modules, hub genes, and the functional pathways. Then, an m6A regulator–target–pathway network and the competing endogenous RNA (ceRNA) network in key modules were constructed. A least absolute shrinkage and selection operation (LASSO) Cox regression model and a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model were constructed to identify the candidate genes for OP prediction. The receiver operator characteristic (ROC) curves were used to validate the performances of predictive models and candidate genes.ResultsA total of 10,520 DEGs, 13 DE-m6A regulators, and 506 DE-miRNAs between high-BMD and low-BMD women were identified. Two m6A-related subclusters with 13 DE-m6A regulators were classified for OP. There were 5,260 m6A-related DEGs identified between two m6A-related subclusters, the PI3K-Akt, MAPK, and immune-related pathways, and bone metabolism was mainly enriched in cluster 2. Cell cycle-related pathways, RNA methylation, and cell death-related pathways were significantly involved in cluster 1. Five modules were identified as key modules based on WGCNA, and an m6A regulator–target gene–pathway network and the ceRNA network were constructed in module brown. Moreover, three m6A regulators (FTO, YTHDF2, and CBLL1) were selected as the candidate genes for OP.ConclusionM6A regulators play an important role in the occurrences and diagnosis of OP.