Dataset Information

M6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis.

ABSTRACT: Osteoarthritis (OA) is a common disease in orthopedics. RNA N6-methyladenosine (m6A) exerts an essential effect in a variety of biological processes in the eukaryotes. In this study, we determined the effect of m6A regulators in the OA along with performing the subtype classification. Differential analysis of OA and normal samples in the database of Gene Expression Omnibus identified 9 significantly differentially expressed m6A regulators. These regulators were monitored by a random forest algorithm so as to evaluate the risk of developing OA disease. On the basis of these 9 moderators, a nomogram was established. The results of decision curve analysis suggested that the patients could benefit from a nomogram model. The OA sample was classified as 2 m6A models through a consensus clustering algorithm in accordance with these 9 regulators. These 2 m6A patterns were then assessed with principal component analysis. We also determined the m6A scores for the 2 m6A patterns and their correlation with immune infiltration. The results indicated that type A had a higher m6A score than type B. Thus, we suggest that the m6A pattern may provide a new approach for diagnose and provide novel ideas for molecular targeted therapy of OA.

SUBMITTER: Hu S

PROVIDER: S-EPMC9805027 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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m6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis.

Hu Shidong S Shen Chen C Yao Xudong X Zou Yulong Y Wang Ting T Sun Xianding X Nie Mao M

BMC medical genomics 20221230 1

Osteoarthritis (OA) is a common disease in orthopedics. RNA N6-methyladenosine (m6A) exerts an essential effect in a variety of biological processes in the eukaryotes. In this study, we determined the effect of m6A regulators in the OA along with performing the subtype classification. Differential analysis of OA and normal samples in the database of Gene Expression Omnibus identified 9 significantly differentially expressed m6A regulators. These regulators were monitored by a random forest algor ...[more]

PMID: 36585683

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Project description:BackgroundThe role of epigenetic modulation in immunity is receiving increased recognition-particularly in the context of RNA N6-methyladenosine (m6A) modifications. Nevertheless, it is still uncertain whether m6A methylation plays a role in the onset and progression of intracranial aneurysms (IAs). This study aimed to establish the function of m6A RNA methylation in IA, as well as its correlation with the immunological microenvironment.MethodsOur study included a total of 97 samples (64 IA, 33 normal) in the training set and 60 samples (44 IA, 16 normal) in the validation set to systematically assess the pattern of RNA modifications mediated by 22 m6A regulators. The effects of m6A modifications on immune microenvironment features, i.e., immune response gene sets, human leukocyte antigen (HLA) genes, and infiltrating immune cells were explored. We employed Lasso, machine learning, and logistic regression for the purpose of identifying an m6A regulator gene signature of IA with external data validation. For the unsupervised clustering analysis of m6A modification patterns in IA, consensus clustering methods were employed. Enrichment analysis was used to assess immune response activity along with other functional pathways. The identification of m6A methylation markers was identified based on a protein-protein interaction network and weighted gene co-expression network analysis.ResultsWe identified an m6A regulator signature of IGFBP2, IGFBP1, IGF2BP2, YTHDF3, ALKBH5, RBM15B, LRPPRC, and ELAVL1, which could easily distinguish individuals with IA from healthy individuals. Unsupervised clustering revealed three m6A modification patterns. Gene enrichment analysis illustrated that the tight junction, p53 pathway, and NOTCH signaling pathway varied significantly in m6A modifier patterns. In addition, the three m6A modification patterns showed significant differences in m6A regulator expression, immune microenvironment, and bio-functional pathways. Furthermore, macrophages, activated T cells, and other immune cells were strongly correlated with m6A regulators. Eight m6A indicators were discovered-each with a statistically significant correlation with IA-suggesting their potential as prognostic biological markers.ConclusionOur study demonstrates that m6A RNA methylation and the immunological microenvironment are both intricately correlated with the onset and progression of IA. The novel insight into patterns of m6A modification offers a foundation for the development of innovative treatment approaches for IA.

Project description:Esophageal cancer is a highly malignant disease with poor prognosis. Despite recent advances in the study of esophageal cancer, there has been only limited improvement in the treatment and prognosis. N6-methyladenosine (m6A), a type of RNA modification, has been extensively investigated and is involved in many biological behaviors, including tumorigenesis and progression. Thus, more research on m6A modification may increase our understanding of esophageal cancer pathogenesis and provide potential targets. In our study, we integrated the public data of esophageal cancer from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) databases. Unsupervised clustering analysis was used to classify patients into different groups. Gene set variation analysis (GSVA) was performed in a nonparametric and unsupervised mode. We evaluated immune cell infiltration by single sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) among m6A clusters were identified using Empirical Bayesian approach. Both multivariate and univariate Cox regression models were used for prognostic analysis. We provided an overview of gene variation and expression of 23 m6A regulators in esophageal cancer, as well as their effects on survival. Based on the overall expression level of m6A regulators, patients were classified into three m6A clusters (A-C) with different immune cell infiltration abundance, gene expression signatures and prognosis. Among m6A clusters, we identified 206 DEGs, according to which patients were classified into 4 gene clusters (A-D). Quantitative m6A score was calculated for each patient based on those DEGs with significant impact on survival. The infiltration of all types of immune cells except type 2 T helper (Th2) cells were negatively correlated with m6A score. M6Acluster C exhibited the lowest m6A score, the most abundant immune cell infiltration, and the worst prognosis, suggesting an immune excluded phenotype. Consistently, gene cluster D with the lowest m6A score showed the worst prognosis. In short, patients with esophageal cancer showed different m6A modification patterns. Quantitative scoring indicated that patients with the lowest m6A score exhibited the most abundant immune cell infiltration and the poorest prognosis. This m6A scoring system is promising to assess m6A modification pattern, characterize immune infiltration and guide personalized treatment and prognostic prediction.

Project description:Introduction: Tremendous evidence indicates that N6-methyladenosine (m6A) epigenetic modification and m6A-related enzymes constitute a complex network, which jointly regulates prevailing pathological processes and various signaling pathways in humankind. Currently, the role of the m6A-mediated molecular regulatory network in hepatocellular carcinoma (HCC) remains elusive. Methods: We recruited expression and pathological files of 368 HCC patients from The Cancer Genome Atlas cohort. Four public datasets serve as external authentication sets for nearest template prediction (NTP) validation. The correlation between 35 regulators and their prognostic value was compared. Gene set variation analysis (GSVA) was used to explore the latent mechanism. Four independent algorithms (ssGSEA, xCell, MCP-counter, and TIMER) were used to calculate the ratio of tumor cells and non-tumor cells to evaluate the tumor immune microenvironment. The m6Ascore model was established by principal component analysis (PCA). Prediction of immunotherapy and potential drugs was performed using TIDE and SubMap. Results: A total of 35 m6A regulators were widely associated, most of which were risk factors for HCC patients. The m6A phenotypic-cluster revealed differences in regulator transcriptional level, gene mutation frequency, functional pathways, and immune cell infiltration abundance under distinct m6A patterns. As expected, the m6A gene cluster confirmed the aforementioned results. The m6Ascore model further found that patients in the high-m6Ascore group were associated with lower tumor purity, higher enrichment of immune and stromal cells, upregulation of metabolic pathways, lower expression of m6A regulators, and favorable outcomes. Low-m6Ascore patients were associated with adverse outcomes. Notably, low-m6Ascore patients might be more sensitive to anti-PD-L1 therapy. Conclusion: This study found that a classification model based on the m6A manner could predict HCC prognosis and response to immunotherapy for HCC patients, which might improve prognosis and contribute to clinical individualized decision-making.

Project description:BackgroundN6-methyladenosine (m6A) is the most abundant internal mRNA modification in eukaryotic cells. There is accumulating evidence that m6A methylation can play a significant role in the early diagnosis and treatment of cancers. However, the mechanism underlying the involvement of m6A in cervical cancer remains unclear.MethodsHere, we examined the m6A modification patterns of immune cells in the tumour microenvironments (TMEs) of 306 patients with cervical cancer from The Cancer Genome Atlas dataset and analysed the relations between them according to 32 m6A regulators. Immune infiltration in the TME of cervical cancer was analysed using the CIBERSORT algorithm and single-sample gene set enrichment analysis. The m6Ascore was structured though principal component analysis.ResultsTwo different m6A modification patterns were detected in 306 patients with cervical cancer, designated as m6Acluster A and B. The immune cell infiltration characteristics and biological behaviour differed between the two patterns, with m6Acluster A showing a higher level of immune infiltration. The samples were also divided into two genomic subtypes according to 114 m6A regulatory genes shown to be closely correlated with prognosis on univariate Cox regression analysis. Survival analysis showed that gene cluster B was related to better survival than gene cluster A. Most of the m6A regulators showed higher expression in gene cluster B than in gene cluster A. Single-sample gene set enrichment analysis indicated a higher level of immune cell infiltration in gene cluster A. The m6Ascore signature was examined to determine the m6A modification patterns in cervical cancer. Patients with a high m6Ascore showed better survival, while the low m6Ascore group had a higher mutation frequency and better response to treatment.ConclusionsThis study showed that m6A modification patterns play important roles in cervical cancer. Analysis of m6A modification patterns will yield an improved understanding of the TME in cervical cancer, and facilitate the development of better immunotherapy strategies.

Dataset Information

M6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis.

Publications

m6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis.

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