Novel insight into m6A regulator-mediated methylation modification patterns and immune characteristics in intracranial aneurysm.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36034129", "kind": "science-record", "revision": 4, "content_hash": "sha256:db2daf4160977a60eb57f1265bc1470326605ee3f4a7835b9287438236f5de9a", "created_at": "2026-11-01T18:15:12Z", "updated_at": "2026-11-01T22:07:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel insight into m6A regulator-mediated methylation modification patterns and immune characteristics in intracranial aneurysm.", "doi": "10.3389/fnagi.2022.973258", "pmid": "36034129", "authors": ["Shifu Li", "Qian Zhang", "Ling Weng", "Ying Han", "Jian Li"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Background: Growing evidence demonstrated that m6A modification in cardiovascular diseases. However, how it is involved in the intracranial aneurysm (IA) is still unclear. This study aimed to identify the role of m6A modification in IA.\nMethods: Three datasets downloaded from the Gene Expression Omnibus (GEO) database were used, including GSE122897, GSE15629, and GSE3679. The landscapes of 24 m6A regulators were depicted using the STRING database, Pearson's correlation analysis, and Wilcoxon test. The targets of differentially expressed m6A (DEm6A) were predicted in the m6A2Target database and the modification m6A sites of hub targets were identified in SRAMP online tool. A diagnostic model based on DEm6A was constructed and verified in training and test databases. A consensus clustering algorithm was performed to classify IA patients into distinct m6A-related clusters. Functional analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis, and gene set enrichment analysis analyses were conducted to elucidate the underlying mechanisms. ssGSEA algorithm was performed to uncover the immune characteristics. A PCA method was adopted to quantify the m6A score.\nResults: Nine DEm6A (IGF2BP1, IGF2BP3, YTHDF2, ZNF217, RBM15, YTHDF3, YTHDC1, FTO, and LRPPRC) significantly differed between IA and controls. Biological annotations showed that immune-related pathways (such as complement activation, inflammatory response, and interleukin signaling) and apoptosis were more enriched in IAs than in controls. Immune analyses indicate that the abundance of immune cells, immune responses, and HLA gene expression were elevated in IA samples than in controls. PCA results showed that IA has a lower m6A score than controls. An immune/apoptosis-related network modified by DEm6A was constructed. The m6A sites of six hub targets (CDK1, ASPM, AURKB, BUB1B, MKI67, and TPX2) were predicted with very high confidence. A diagnostic model with four genes (LRPPRC, YTHDF3, IGF2BP1, and ZNF217) was constructed and verified. Two m6A modification subtypes were identified with unsupervised cluster analysis. Immune infiltration analysis revealed that cluster 1 had higher immune activation than cluster 2. Further study showed that cluster 1 had a larger proportion of ruptured IAs.\nConclusion: The m6A modification may shape the IAs microenvironment and participates in the formation and rupture of IAs by regulating immune infiltration.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) logistic regression was used to further screen out DEm6A using the “glmnet” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36034129", "locator": "sections[10] \"Development and validation of a m6A diagnostic model\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) logistic regression was used to further screen out DEm6A using the “glmnet” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36034129", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene m6A regulator diagnostic model showed good diagnostic ability for distinguishing intracranial aneurysms from controls across the training, testing, and external validation datasets.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The diagnostic ability of the risk score was evaluated with ROC curves, indicating that the risk scores had good diagnostic ability in discriminating aneurysms from the control samples, with an AUC of 0.847 (95% CI 0.660–1.000) in the training set, 0.889 (95% CI 0.746–1.000) in the test set, 0.957 (95% CI 0.866–1.000) in the GSE15629 dataset, and 0.743 (95% CI 0.594–0.892) in the GSE36791 dataset (Figure 7F)."}, "evidence": [{"asset": "source:pmid:36034129", "locator": "sections[23] \"Construction and validation of a m6A regulator diagnostic model\" paragraph 2", "quote": "The diagnostic ability of the risk score was evaluated with ROC curves, indicating that the risk scores had good diagnostic ability in discriminating aneurysms from the control samples, with an AUC of 0.847 (95% CI 0.660–1.000) in the training set, 0.889 (95% CI 0.746–1.000) in the test set, 0.957 (95% CI 0.866–1.000) in the GSE15629 dataset, and 0.743 (95% CI 0.594–0.892) in the GSE36791 dataset (Figure 7F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene m6A regulator diagnostic model showed good diagnostic ability for distinguishing intracranial aneurysms from controls across the training, testing, and external validation datasets.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36034129"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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