Identification of a DNA N6-Adenine Methyltransferase Complex and Its Impact on Chromatin Organization.

DNA N6-adenine methylation (6mA) has recently been described in diverse eukaryotes, spanning unicellular organisms to metazoa. Here, we report a DNA 6mA methyltransferase complex in ciliates, termed MTA1c. It consists of two MT-A70 proteins and two homeobox-like DNA-binding proteins and specifically methylates dsDNA. Disruption of the catalytic subunit, MTA1, in the ciliate Oxytricha leads to genome-wide loss of 6mA and abolishment of the consensus ApT dimethylated motif. Mutants fail to complete the sexual cycle, which normally coincides with peak MTA1 expression. We investigate the impact of 6mA on nucleosome occupancy in vitro by reconstructing complete, full-length Oxytricha chromosomes harboring 6mA in native or ectopic positions. We show that 6mA directly disfavors nucleosomes in vitro in a local, quantitative manner, independent of DNA sequence. Furthermore, the chromatin remodeler ACF can overcome this effect. Our study identifies a diverged DNA N6-adenine methyltransferase and defines the role of 6mA in chromatin organization.

Engineering Epigenetic Regulation Using Synthetic Read-Write Modules.

Chemical modifications to DNA and histone proteins are involved in epigenetic programs underlying cellular differentiation and development. Regulatory networks involving molecular writers and readers of chromatin marks are thought to control these programs. Guided by this common principle, we established an orthogonal epigenetic regulatory system in mammalian cells using N6-methyladenine (m6A), a DNA modification not commonly found in metazoan epigenomes. Our system utilizes synthetic factors that write and read m6A and consequently recruit transcriptional regulators to control reporter loci. Inspired by models of chromatin spreading and epigenetic inheritance, we used our system and mathematical models to construct regulatory circuits that induce m6A-dependent transcriptional states, promote their spatial propagation, and maintain epigenetic memory of the states. These minimal circuits were able to program epigenetic functions de novo, conceptually validating "read-write" architectures. This work provides a toolkit for investigating models of epigenetic regulation and encoding additional layers of epigenetic information in cells.

N6-methyladenine DNA Modification in Glioblastoma.

Genetic drivers of cancer can be dysregulated through epigenetic modifications of DNA. Although the critical role of DNA 5-methylcytosine (5mC) in the regulation of transcription is recognized, the functions of other non-canonical DNA modifications remain obscure. Here, we report the identification of novel N6-methyladenine (N6-mA) DNA modifications in human tissues and implicate this epigenetic mark in human disease, specifically the highly malignant brain cancer glioblastoma. Glioblastoma markedly upregulated N6-mA levels, which co-localized with heterochromatic histone modifications, predominantly H3K9me3. N6-mA levels were dynamically regulated by the DNA demethylase ALKBH1, depletion of which led to transcriptional silencing of oncogenic pathways through decreasing chromatin accessibility. Targeting the N6-mA regulator ALKBH1 in patient-derived human glioblastoma models inhibited tumor cell proliferation and extended the survival of tumor-bearing mice, supporting this novel DNA modification as a potential therapeutic target for glioblastoma. Collectively, our results uncover a novel epigenetic node in cancer through the DNA modification N6-mA.

Aberrant DNA N6 -methyladenine incorporation via adenylate kinase 1 is suppressed by ADAL deaminase-dependent 2'-deoxynucleotide pool sanitation.

Intracellular decay of N6 -methyladenine (m6A)-containing RNA potentially induces aberrant N6 -methyl-2'-adenine (6mdA) misincorporation into DNA. Biophysically, misincorporated 6mdA may destabilize the DNA duplex in a manner similar to bona fide methylated 6mdA DNA, thereby affecting DNA replication and transcription. Utilizing heavy stable isotope labeling and ultrasensitive UHPLC-MS/MS assay, we demonstrate that intracellular m6A-RNA decay does not generate free 6mdA species, nor lead to any misincorporated DNA 6mdA in most mammalian cell lines tested, unveiling the existence of a sanitation mechanism that prevents 6mdA misincorporation. Depletion of deaminase ADAL increases the levels of free 6mdA species, concomitant with the presence of DNA-misincorporated 6mdA resulting from intracellular RNA m6A decay, suggesting that ADAL catabolizes 6mdAMP in vivo. Furthermore, we show that the overexpression of adenylate kinase 1 (AK1) promotes 6mdA misincorporation, while AK1 knockdown diminishes 6mdA incorporation, in ADAL-deficient cells. We conclude that ADAL together with other factors (such as MTH1) contributes to 2'-deoxynucleotide pool sanitation in most cells but compromised sanitation (e.g., in NIH3T3 cells) and increased AK1 expression may facilitate aberrant 6mdA incorporation. This sanitation mechanism may provide a framework for the maintenance of the epigenetic 6mdA landscape.

A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/.

Bioinformatics analysis of the prognosis and biological significance of N6 methyladenine regulators in oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a common type of cancer. We performed the present study to explore the function and specific regulatory mechanism of m6A in OSCC and to find a new diagnosis and treatment strategy for OSCC. METHODS: Using bioinformatics, we examined the associations between 20 genes associated with methylation and the epidemiological data about OSCC tumor samples. RESULTS: We created two subgroup curves based on the gene expression levels related to m6A methylation. In total, 14 genes were found to be differentially expressed. Significant differences in terms of survival rates, Grade and gender were found among subgroups with different m6A expression levels. Nine genes had areas under the curves greater than 0.7. Therefore, these genes may be utilized for the clinical diagnosis and prognosis of OSCC. Because of their high individual predictive value, HNRNPC and IGF2BP2 were chosen as the two potential predictors. The two regulatory elements were used to create the prognostic signals for OSCC. The developed prognostic signals made it possible to discern between the samples with good and poor prognoses without potential confounding factors. Four genes (HNRNPC, METTL14, YTHDF2 and ALKBH5) combined well with compounds, which had an anti-cancer effect. CONCLUSIONS: Our findings suggested that OSCC-related genes with m6A methylation could be beneficial treatment targets or prognostic indicators.

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block.

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

UVB promotes melanogenesis by regulating METTL3.

Ultraviolet (UV) radiation is the primary exogenous inducer of skin pigmentation, although the mechanism has not been fully elucidated. N6-methyladenosine (m6 A) modification is one of the key epigenetic form of gene regulation that affects multiple biological processes. The aim of this study was to explore the role and underlying mechanisms of m6 A modification in UVB-induced melanogenesis. Low-dose UVB increased global m6 A modification in melanocytes (MCs) and MNT1 melanoma cell line. The GEPIA database predicted that methyltransferase METTL3 is positively correlated with the melanogenic transcription factor MITF in the sun-exposed skin tissues. After METTL3 respectively overexpressed and knocked down in the MNT1, the melanin content and melanogenesis-related genes were significantly upregulated after overexpression of METTL3, especially with UVB irradiation, and downregulated after METTL3 knockdown. METTL3 levels were also higher in melanocytic nevi with high melanin content. METTL3 overexpression and knockdown also altered the protein level of YAP1. SRAMP analysis predicted four high-potential m6 A modification sites on YAP1 mRNA, of which three were confirmed by methylated RNA immunoprecipitation. Inhibition of YAP1 expression can partially reverse melanogenesis induced by overexpression of METTL3. In conclusion, UVB irradiation promotes global m6 A modification in MCs and upregulates METTL3, which increases the expression level of YAP1 through m6 A modification, thereby activating the co-transcription factor TEAD1 and promoting melanogenesis.

N6-Methyladenine Modification of Hepatitis Delta Virus Regulates Its Virion Assembly by Recruiting YTHDF1.

Hepatitis delta virus (HDV) is a defective satellite virus that uses hepatitis B virus (HBV) envelope proteins to form its virions and infect hepatocytes via the HBV receptors. Concomitant HDV/HBV infection continues to be a major health problem, with at least 25 million people chronically infected worldwide. N6-methyladenine (m6A) modification of cellular and viral RNAs is the most prevalent internal modification that occurs cotranscriptionally, and this modification regulates various biological processes. We have previously described a wider range of functional roles of m6A methylation of HBV RNAs, including its imminent regulatory role in the encapsidation of pregenomic RNA. In this study, we present evidence that m6A methylation also plays an important role in the HDV life cycle. Using the methylated RNA immunoprecipitation (MeRIP) assay, we identified that the intracellular HDV genome and antigenome are m6A methylated in HDV- and HBV-coinfected primary human hepatocytes and HepG2 cell expressing sodium taurocholate cotransporting polypeptide (NTCP), while the extracellular HDV genome is not m6A methylated. We observed that HDV genome and delta antigen levels are significantly decreased in the absence of METTL3/14, while the extracellular HDV genome levels are increased by depletion of METTL3/14. Importantly, YTHDF1, an m6A reader protein, interacts with the m6A-methylated HDV genome and inhibits the interaction between the HDV genome and antigens. Thus, m6A of the HDV genome negatively regulates virion production by inhibiting the interaction of the HDV genome with delta antigens through the recruitment of YTHDF1. This is the first study that provides insight into the functional roles of m6A in the HDV life cycle. IMPORTANCE The functional roles of N6-methyladenine (m6A) modifications in the HBV life cycle have been recently highlighted. Here, we investigated the functional role of m6A modification in the HDV life cycle. HDV is a subviral agent of HBV, as it uses HBV envelope proteins to form its virions. We found that m6A methylation also occurs in the intracellular HDV genome and antigenome but not in the extracellular HDV genome. The m6A modification of the HDV genome recruits m6A reader protein (YTHDF1) onto the viral genome. The association of YTHDF1 with the HDV genome abrogates the interaction of delta antigens with the HDV genome and inhibits virion assembly. This study describes the unique effects of m6A on regulation of the HDV life cycle.

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naïve Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/.

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

MOTIVATION: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. RESULTS: In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.

Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.

As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6 mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6 mA modifications is necessary for a deep understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models were developed with small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we present a novel model based on transformer architecture and deep learning to identify DNA 6 mA sites from the cross-species genome. The model is constructed on a benchmark dataset and explored a feature derived from pre-trained transformer word embedding approaches. Subsequently, a convolutional neural network was employed to learn the generated features and generate the prediction outcomes. As a result, our predictor achieved excellent performance during independent test with the accuracy and Matthews correlation coefficient (MCC) of 79.3% and 0.58, respectively. Overall, its performance achieved better accuracy than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, our model is expected to assist biologists in accurately identifying 6mAs and formulate the novel testable biological hypothesis. We also release source codes and datasets freely at https://github.com/khanhlee/bert-dna for front-end users.

DNA N6-methyladenine involvement and regulation of hepatocellular carcinoma development.

DNA N6-methyladenine (6 mA) is a new type of DNA methylation identified in various eukaryotic cells. However, its alteration and genomic distribution features in hepatocellular carcinoma (HCC) remain elusive. In this study, we found that N6AMT1 overexpression increased HCC cell viability, suppressed apoptosis, and enhanced migration and invasion, whereas ALKBH1 overexpression induced the opposite effects. Further, 23,779 gain-of-6 mA regions and 11,240 loss-of-6 mA regions were differentially identified in HCC tissues. The differential gain and loss of 6 mA regions were considerably enriched in intergenic regions. Moreover, 7% of the differential 6 mA modifications were associated with tumors, with 60 associated with oncogenes and 57 with tumor suppressor genes (TSGs), and 17 were common to oncogenes and TSGs. The candidate genes affected by 6 mA were filtered by gene ontology (GO) and RNA-seq. Using quantitative polymerase chain reaction (qPCR), BCL2 and PARTICL were found to be correlated with DNA 6 mA in certain HCC processes.

CRISPR-Cas9-Mediated Mutation of Methyltransferase METTL4 Results in Embryonic Defects in Silkworm Bombyx mori.

DNA N6-methyladenine (6mA) has recently been found to play regulatory roles in gene expression that links to various biological processes in eukaryotic species. The functional identification of 6mA methyltransferase will be important for understanding the underlying molecular mechanism of epigenetic 6mA methylation. It has been reported that the methyltransferase METTL4 can catalyze the methylation of 6mA; however, the function of METTL4 remains largely unknown. In this study, we aim to investigate the role of the Bombyx mori homolog METTL4 (BmMETTL4) in silkworm, a lepidopteran model insect. By using CRISPR-Cas9 system, we somatically mutated BmMETTL4 in silkworm individuates and found that disruption of BmMETTL4 caused the developmental defect of late silkworm embryo and subsequent lethality. We performed RNA-Seq and identified that there were 3192 differentially expressed genes in BmMETTL4 mutant including 1743 up-regulated and 1449 down-regulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that genes involved in molecular structure, chitin binding, and serine hydrolase activity were significantly affected by BmMETTL4 mutation. We further found that the expression of cuticular protein genes and collagens were clearly decreased while collagenases were highly increased, which had great contributions to the abnormal embryo and decreased hatchability of silkworm. Taken together, these results demonstrated a critical role of 6mA methyltransferase BmMETTL4 in regulating embryonic development of silkworm.

N6-Methyladenine Progressively Accumulates in Mitochondrial DNA during Aging.

N6-methyladenine (6mA) in the DNA is a conserved epigenetic mark with various cellular, physiological and developmental functions. Although the presence of 6mA was discovered a few years ago in the nuclear genome of distantly related animal taxa and just recently in mammalian mitochondrial DNA (mtDNA), accumulating evidence at present seriously questions the presence of N6-adenine methylation in these genetic systems, attributing it to methodological errors. In this paper, we present a reliable, PCR-based method to determine accurately the relative 6mA levels in the mtDNA of Caenorhabditis elegans, Drosophila melanogaster and dogs, and show that these levels gradually increase with age. Furthermore, daf-2(-)-mutant worms, which are defective for insulin/IGF-1 (insulin-like growth factor) signaling and live twice as long as the wild type, display a half rate at which 6mA progressively accumulates in the mtDNA as compared to normal values. Together, these results suggest a fundamental role for mtDNA N6-adenine methylation in aging and reveal an efficient diagnostic technique to determine age using DNA.

Context-dependent DNA polymerization effects can masquerade as DNA modification signals.

BACKGROUND: Single molecule measurements of DNA polymerization kinetics provide a sensitive means to detect both secondary structures in DNA and deviations from primary chemical structure as a result of modified bases. In one approach to such analysis, deviations can be inferred by monitoring the behavior of DNA polymerase using single-molecule, real-time sequencing with zero-mode waveguide. This approach uses a Single Molecule Real Time (SMRT)-sequencing measurement of time between fluorescence pulse signals from consecutive nucleosides incorporated during DNA replication, called the interpulse duration (IPD). RESULTS: In this paper we present an analysis of loci with high IPDs in two genomes, a bacterial genome (E. coli) and a eukaryotic genome (C. elegans). To distinguish the potential effects of DNA modification on DNA polymerization speed, we paired an analysis of native genomic DNA with whole-genome amplified (WGA) material in which DNA modifications were effectively removed. Adenine modification sites for E. coli are known and we observed the expected IPD shifts at these sites in the native but not WGA samples. For C. elegans, such differences were not observed. Instead, we found a number of novel sequence contexts where IPDs were raised relative to the average IPDs for each of the four nucleotides, but for which the raised IPD was present in both native and WGA samples. CONCLUSION: The latter results argue strongly against DNA modification as the underlying driver for high IPD segments for C. elegans, and provide a framework for separating effects of DNA modification from context-dependent DNA polymerase kinetic patterns inherent in underlying DNA sequence for a complex eukaryotic genome.

Destabilization of DNA and RNA G-quadruplex structures formed by GGA repeat due to N6-methyladenine modification.

N6-methyladenine (m6A) is the most abundant RNA modification in eukaryotic RNA. Further, m6A has been identified in the genomic DNA of both eukaryotes and prokaryotes. The G-quadruplex (G4) structure is a non-canonical nucleic acid structure formed by the stacking of G:G:G:G tetrads. In this study, we evaluated the effect of m6A modifications on G4 structures formed by GGA repeat oligonucleotides, d(GGA)8, d(GGA)4, and r(GGA)4. The d(GGA)8 forms an intramolecular tetrad:heptad:heptad:tetrad G4 structure, while d(GGA)4 forms a dimerized intermolecular tetrad:heptad:heptad:tetrad G4 structure. r(GGA)4 forms a dimerized intermolecular tetrad:hexad:hexad:tetrad G4 structure. Circular dichroism melting analysis demonstrated that (1) m6A modifications destabilized the G4 structure formed by d(GGA)8, (2) m6A modification at A3 disrupted the G4 structure formed by d(GGA)4, and (3) m6A modification at A3 destabilized the G4 structure formed by r(GGA)4. m6A modifications may be involved in controlling G4 structure formation to regulate biological functions.

IGF2BP1-regulated expression of ERRα is involved in metabolic reprogramming of chemotherapy resistant osteosarcoma cells.

Doxorubicin (Dox) is the standard treatment approach for osteosarcoma (OS), while acquired drug resistance seriously attenuates its treatment efficiency. The present study aimed to investigate the potential roles of metabolic reprogramming and the related regulatory mechanism in Dox-resistant OS cells. The results showed that the ATP levels, lactate generation, glucose consumption and oxygen consumption rate were significantly increased in Dox-resistant OS cells compared with parental cells. Furthermore, the results revealed that the increased expression of estrogen-related receptor alpha (ERRα) was involved in metabolic reprogramming in chemotherapy resistant OS cells, since targeted inhibition of ERRα restored the shifting of metabolic profiles. Mechanistic analysis indicated that the mRNA stability, rather than ERRα transcription was markedly increased in chemoresistant OS cells. Therefore, it was hypothesized that the 3'-untranslated region of ERRα mRNA was methylated by N6-methyladenine, which could further recruit insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) to suppress mRNA decay and increase mRNA stability. IGF2BP1 knockdown downregulated ERRα and reversed the metabolic alteration of resistant OS cells. Additionally, the oncogenic effect of the IGF2BP1/ERRα axis on Dox-resistant OS cells was verified by in vitro and in vivo experiments. Clinical analysis also revealed that the expression levels of IGF2BP1 and ERRα were associated with the clinical progression of OS. Collectively, the current study suggested that the IGF2BP1/ERRα axis could regulate metabolic reprogramming to contribute to the chemoresistance of OS cells.

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.