Exploration of Potential Integrated Models of N6-Methyladenosine Immunity in Systemic Lupus Erythematosus by Bioinformatic Analyses

Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis

Background

Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions.

Methods

The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE.

Results

Twelve m6A regulatory genes was identified, demonstrating statistical significance (p < 0.05) and utilized for constructing a nomogram model using the RF algorithm. EPSTI1, USP18, HP, and MMP9, as the hub genes, were identified. MMP9 uniquely correlates with m6A modification and was causally linked to an increased risk of IS, as indicated by our inverse variance weighting analysis showing an odds ratio of 1.0134 (95% CI=1.0004-1.0266, p = 0.0440).

Conclusion

Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.

RNA N6-methyladenosine methylation and skin diseases

Skin diseases are global health issues caused by multiple pathogenic factors, in which epigenetics plays an invaluable role. Post-transcriptional RNA modifications are important epigenetic mechanism that regulate gene expression at the genome-wide level. N6-methyladenosine (m6A) is the most prevalent modification that occurs in the messenger RNAs (mRNA) of most eukaryotes, which is installed by methyltransferases called "writers", removed by demethylases called "erasers", and recognised by RNA-binding proteins called "readers". To date, m6A is emerging to play essential part in both physiological processes and pathological progression, including skin diseases. However, a systematic summary of m6A in skin disease has not yet been reported. This review starts by illustrating each m6A-related modifier specifically and their roles in RNA processing, and then focus on the existing research advances of m6A in immune homeostasis and skin diseases.

M6A methylation modification in autoimmune diseases, a promising treatment strategy based on epigenetics

BACKGROUND

N6-methyladenosine (m6A) methylation modification is involved in the regulation of various biological processes, including inflammation, antitumor, and antiviral immunity. However, the role of m6A modification in the pathogenesis of autoimmune diseases has been rarely reported.

METHODS

Based on a description of m6A modification and the corresponding research methods, this review systematically summarizes current insights into the mechanism of m6A methylation modification in autoimmune diseases, especially its contribution to rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE).

RESULTS

By regulating different biological processes, m6A methylation is involved in the pathogenesis of autoimmune diseases and provides a promising biomarker for the diagnosis and treatment of such diseases. Notably, m6A methylation modification is involved in regulating a variety of immune cells and mitochondrial energy metabolism. In addition, m6A methylation modification plays a role in the pathological processes of RA, and m6A methylation-related genes can be used as potential targets in RA therapy.

CONCLUSIONS

M6A methylation modification plays an important role in autoimmune pathological processes such as RA and SLE and represents a promising new target for clinical diagnosis and treatment, providing new ideas for the treatment of autoimmune diseases by targeting m6A modification-related pathways.

The role of N6-methyladenosine (m6A) in kidney diseases

Chemical modifications are a specific and efficient way to regulate the function of biological macromolecules. Among them, RNA molecules exhibit a variety of modifications that play important regulatory roles in various biological processes. More than 170 modifications have been identified in RNA molecules, among which the most common internal modifications include N6-methyladenine (m6A), n1-methyladenosine (m1A), 5-methylcytosine (m5C), and 7-methylguanine nucleotide (m7G). The most widely affected RNA modification is m6A, whose writers, readers, and erasers all have regulatory effects on RNA localization, splicing, translation, and degradation. These functions, in turn, affect RNA functionality and disease development. RNA modifications, especially m6A, play a unique role in renal cell carcinoma disease. In this manuscript, we will focus on the biological roles of m6A in renal diseases such as acute kidney injury, chronic kidney disease, lupus nephritis, diabetic kidney disease, and renal cancer.

Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis

RNA N6-methladenosine (m6A) regulators are required for a variety of biological processes, including immune responses, and increasing evidence indicates that their dysregulation is closely associated with many diseases. However, the potential roles of m6A regulators in sepsis remain unknown. We comprehensively analyzed the transcriptional variations in and interactions of 26 m6A regulators in sepsis based on the Gene Expression Omnibus (GEO) database. A random forest (RF) model and nomogram were established to predict the occurrence and risk of sepsis in patients. Then, two different m6A subtypes were defined by consensus clustering analysis, and we explored the correlation between the subtypes and immune cells. We found that 17 of the 26 m6A regulators were significantly differentially expressed between patients with and without sepsis, and strong correlations among these 17 m6A regulators were revealed. Compared with the support vector machine (SVM) model, the RF model had better predictive ability, and therefore was used to construct a reliable nomogram containing 10 candidate m6A regulators to predict the risk of sepsis in patients. In addition, a consensus clustering algorithm was used to identify two different subtypes of m6A, which helped us distinguish different levels of immune cell infiltration and inflammation in patients with sepsis. Comprehensive analysis of m6A regulators in sepsis revealed their potential roles in sepsis occurrence, immune cell infiltration and inflammation in patients with sepsis. This study may contribute to the development of follow-up treatment strategies for sepsis.

Identification and validation of immune cells and hub genes alterations in recurrent implantation failure: A GEO data mining study

Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

The Role of N6-Methyladenosine in Inflammatory Diseases

N⁶-Methyladenosine (m⁶A) is the most abundant epigenetic RNA modification in eukaryotes, regulating RNA metabolism (export, stability, translation, and decay) in cells through changes in the activity of writers, erasers, and readers and ultimately affecting human life or disease processes. Inflammation is a response to infection and injury in various diseases and has therefore attracted significant attention. Currently, extensive evidence indicates that m⁶A plays an essential role in inflammation. In this review, we focus on the mechanisms of m⁶A in inflammatory autoimmune diseases, metabolic disorder, cardio-cerebrovascular diseases, cancer, and pathogen-induced inflammation, as well as its possible role as targets for clinical diagnosis and treatment.

Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients

Background: As a recurrent inflammatory bone disease, the treatment of osteomyelitis is always a tricky problem in orthopaedics. N6-methyladenosine (m6A) regulators play significant roles in immune and inflammatory responses. Nevertheless, the function of m6A modification in osteomyelitis remains unclear. Methods: Based on the key m6A regulators selected by the GSE16129 dataset, a nomogram model was established to predict the incidence of osteomyelitis by using the random forest (RF) method. Through unsupervised clustering, osteomyelitis patients were divided into two m6A subtypes, and the immune infiltration of these subtypes was further evaluated. Validating the accuracy of the diagnostic model for osteomyelitis and the consistency of clustering based on the GSE30119 dataset. Results: 3 writers of Methyltransferase-like 3 (METTL3), RNA-binding motif protein 15B (RBM15B) and Casitas B-lineage proto-oncogene like 1 (CBLL1) and three readers of YT521-B homology domain-containing protein 1 (YTHDC1), YT521-B homology domain-containing family 3 (YTHDF2) and Leucine-rich PPR motif-containing protein (LRPPRC) were identified by difference analysis, and their Mean Decrease Gini (MDG) scores were all greater than 10. Based on these 6 significant m6A regulators, a nomogram model was developed to predict the incidence of osteomyelitis, and the fitting curve indicated a high degree of fit in both the test and validation groups. Two m6A subtypes (cluster A and cluster B) were identified by the unsupervised clustering method, and there were significant differences in m6A scores and the abundance of immune infiltration between the two m6A subtypes. Among them, two m6A regulators (METTL3 and LRPPRC) were closely related to immune infiltration in patients with osteomyelitis. Conclusion: m6A regulators play key roles in the molecular subtypes and immune response of osteomyelitis, which may provide assistance for personalized immunotherapy in patients with osteomyelitis.