Multi-omics analysis of m6A modification-related patterns based on m6A regulators and tumor microenvironment infiltration in lung adenocarcinoma

Identification of marker genes associated with N6-methyladenosine and autophagy in ulcerative colitis

BACKGROUND

Both N6-methyladenosine (m6A) methylation and autophagy are considered relevant to the pathogenesis of ulcerative colitis (UC). However, a systematic exploration of the role of the com-bination of m6A methylation and autophagy in UC remains to be performed.

AIM

To elucidate the autophagy-related genes of m6A with a diagnostic value for UC.

METHODS

The correlation between m6A-related genes and autophagy-related genes (ARGs) was analyzed. Finally, gene set enrichment analysis (GSEA) was performed on the characteristic genes. Additionally, the expression levels of four characteristic genes were verified in dextran sulfate sodium (DSS)-induced colitis in mice.

RESULTS

GSEA indicated that BAG3, P4HB and TP53INP2 were involved in the inflammatory response and TNF-α signalling via nuclear factor kappa-B. Furthermore, polymerase chain reaction results showed significantly higher mRNA levels of BAG3 and P4HB and lower mRNA levels of FMR1 and TP53INP2 in the DSS group compared to the control group.

CONCLUSION

This study identified four m6A-ARGs that predict the occurrence of UC, thus providing a scientific reference for further studies on the pathogenesis of UC.

Development of a Novel Prognostic Model of Glioblastoma Based on m6A-Associated Immune Genes and Identification of a New Biomarker

Background

Accumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified.

Methods

We obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX–least absolute shrinkage and selection operator (LASSO)–multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells.

Results

Our glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients’ survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells.

Conclusion

We developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.

m6A Regulator-Mediated Methylation Modification Patterns and Tumor Microenvironment Cell-Infiltration Characterization in Head and Neck Cancer

Background: Recently, RNA modifications have emerged as essential epigenetic regulators of gene expression. However, the mechanism of how RNA N⁶-methyladenosine (m6A) modification interacts with tumor microenvironment (TME) infiltration remains obscure.

Methods: A total of 876 head and neck cancer samples considering 21 m6A regulators were included and analyzed to determine the m6A modification patterns. These modification patterns were then correlated with TME immune cell-infiltrating characteristics. A scoring system, the m6Ascore, was constructed using principal component analysis algorithms to quantify m6A modification of tumors.

Results: Three m6A modification patterns were identified, with TME infiltrating characteristics highly consistent with tumors with three distinct immune phenotypes, including immune-inflamed, immune-exclude, and immune-desert phenotypes. It was demonstrated that the identification of the m6A modification patterns via m6Ascore could predict tumor progression, subtypes, TME stromal activity, variation of relevant genes, and patient prognosis. Low m6Ascore, identified to be an inflamed phenotype, is found to be associated with low stroma activity and tumor mutation burden, high survival probability, increased tumor neoantigen burden, and enhanced response to anti-PD-1/L1 immunotherapy. The therapeutic advantages and clinical benefits of patients with low m6Ascore were further verified in two immunotherapy cohorts.

Conclusion: This study identified the significant role that the m6A modification played in the formation of TME characteristics. A more comprehensive understanding of the m6A modification patterns and their correlation with TME infiltration will contribute to the discovery of immunotherapy strategies with better efficacy.