m6A regulator–mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in patients with intracranial aneurysms

Abstract

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.

GREM1,LRPPRC and SLC39A4 as Potential Biomarkers of Intervertebral Disc Degeneration:A Bioinformatics Analysis based on Multiple Microarray and Single-cell Sequencing Data

“…Various studies demonstrate that high expression of LRPPRC is associated with poor prognosis in a variety of cancers, such as bladder urothelial carcinoma [39], lung cancer [40] and pancreatic cancer [41]. Maimaiti [42] et al de ned LRPPRC as a N6methyladenosine (m6A) modi cation for intracranial aneurysms. Ghavami S [43] et al reported that LRPPRC was associated with multiple neurodegenerative diseases via autophagy and apoptosis.…”

DNA Methylation-estimated Phenotypes, Telomere length, Aging and Risk of Intracranial Aneurysms: Evidence from genetic studies

“…Previously, the mechanisms of m6A methylation [49] and immunogenic cell death [50] in IAs were also investigated. In the future, further investigation should be conducted on the mechanism of its role in IAs in DNA methylation and more epigenetic regulation.…”

Elucidating key immunological biomarkers and immune microenvironment dynamics in aging‐related intracranial aneurysm through integrated multi‐omics analysis

“…Sva package was used to remove batch effect in raw data. A total of 64 IA samples and 33 normal samples were included 11 . Subsequently, a random split was performed using the caret package in a 7:3 ratio.…”

“…A total of 64 IA samples and 33 normal samples were included. 11 Subsequently, a random split was performed using the caret package in a 7:3 ratio. The training set included 45 cases of aneurysm samples and 24 cases of normal samples.…”