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The significance of m6A RNA methylation modification in prognosis and tumor microenvironment immune infiltration of cervical cancer.

ABSTRACT: Recent studies have highlighted that N6-methyladenosine (m6A) plays a significant role in tumorigenicity and progression. However, the mechanism of m6A modifications in the tumor microenvironment (TME) immune cell infiltration in cervical cancer (CC) remains unclear. Clinical and RNA sequencing data of 25 m6A RNA methylation regulators were acquired from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. LASSO Cox regression analysis was used to generate a prognostic risk signature. m6A modification patterns were identified based on the expression of 25 m6A regulators, and their correlation with TME immune cell-infiltrating characterization was analyzed. Principal component analysis was used to construct an m6A-scoring signature (m6A score) to evaluate the m6A modification patterns of individual CC samples and guide the selection of more effective immunotherapeutic strategies. Genetic and expression alterations of 25 m6A regulators were highly heterogeneous between CC and normal tissues. METTL14 and IGF2BP1 were selected to conduct the prognostic risk signature. Three m6A modification patterns were identified in 659 CC samples, which were associated with distinct clinical outcomes and biological pathways. The TME immune cell-infiltrating characterization of the three m6A modification patterns was highly consistent with 3 tumor immune phenotypes, including immune-excluded, immune-inflamed, and immune-desert phenotypes. Due to the heterogeneity of m6A modification patterns, an m6A scoring signature was established to evaluate the m6A modification patterns of individual CC samples. Univariate and multivariate Cox regression analyses revealed that the m6A score is a robust and independent prognostic biomarker for assessing the prognosis of CC patients. A low m6A score, characterized by higher somatic mutation and higher expression of proliferation-related and DNA repair-related genes, indicated poor overall survival. Activation of immune infiltration was exhibited by the high m6A score, which was likely to have a good response and clinical benefits to antiPD-1/L1 immunotherapy. This study highlights the prognostic value of 25 m6A regulators in CC. The m6A modification is related to immune regulation and the formation of TME heterogeneity and complexity. An m6A scoring signature to clarify the individual m6A modification pattern could enhance our understanding of TME immune cell-infiltrating characterization and guide immunotherapy strategies.

SUBMITTER: Guo Y

PROVIDER: S-EPMC9239609 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:ObjectiveN6-methyladenosine (m6A) has been implicated in the progression of several diseases, and the role of epigenetic regulation in immunity is emerging, particularly for RNA m6A modification. However, it is unclear how m6A-related genes affect the immune microenvironment of ligamentum flavum hyperplasia (LFH). Therefore, we aimed to investigate the effect of m6A modification on the LFH immune microenvironment.MethodsThe GSE113212 dataset was downloaded from the Gene Expression Omnibus (GEO) database. We systematically analyzed m6A regulators in eight patient samples and the corresponding clinical information of the samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and protein-protein interactions (PPIs) were used to explore the correlation of m6A clusters with the immune microenvironment in LFH. A least absolute shrinkage and selection operator (Lasso) regression was then used to further explore the m6A prognostic signature in LFH. The relative abundance of immune cell types was quantified using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. We explored the relationship between hub genes and small molecule drug sensitivity by clustering hub gene-based samples. In addition, Real-Time quantitative PCR (RT-qPCR) as well as western blotting (WB) were used to validate the gene expression of the differentially expressed genes.ResultsA total of 1259 differentially expressed genes were identified, of which 471 were upregulated and 788 were downregulated. A total of three genes showed significant differences (METTL16, PCIF1, and FTO). According to the enrichment analysis, immune factors may play a key role in LFH. ssGSEA was used to cluster the immune infiltration score, construct the hub gene diagnosis model, and screen a total of 6 LFH immune-related prediction model genes. The predictive diagnostic model of LFH was further constructed, revealing that METTL16, PCIF1, FTO and ALKBH5 had superior diagnostic efficiency. RT-qPCR results showed that 6 genes (METTL16, PCIF1, POSTN, TNNC1, MMP1 and ACTA1; P < 0.05) exhibited expression consistent with the results of the bioinformatics analysis of the mRNA microarray. Up-regulated METTL16, PCIF1, and ALKBH5 levels in LFH were validated by western blotting.ConclusionDiversity and complexity of LFH's immune microenvironment are influenced by M6A modification, and our study provides strong evidence for predicting the diagnosis and prognosis of LFH.

Project description:BackgroundLung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky.MethodsThe gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model.ResultConsensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094).ConclusionOur study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

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The significance of m6A RNA methylation modification in prognosis and tumor microenvironment immune infiltration of cervical cancer.

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