Identification of Senescence- and Inflammation-Related Genes and Immune Microenvironment Characterization in Intracranial Aneurysms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41487007", "kind": "science-record", "revision": 4, "content_hash": "sha256:834908d1a2e1bf3dc8c88a271bd0b09a6fc60d030d2012588d251dce2441c18b", "created_at": "2026-11-01T20:31:27Z", "updated_at": "2026-11-01T22:57:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Senescence- and Inflammation-Related Genes and Immune Microenvironment Characterization in Intracranial Aneurysms.", "doi": "10.2174/0118715303420035251023051847", "pmid": "41487007", "authors": ["Xiaoyan Li", "Yingying Li", "Le Zhang", "Jie Mao", "Bin Li"], "journal": "Endocrine, metabolic & immune disorders drug targets", "year": 2026, "abstract": "INTRODUCTION: Intracranial aneurysm (IA) is a cerebrovascular disease that lacks effective methods for early diagnosis and risk prediction. Considering the pivotal roles of senescence and inflammation in many diseases, this study aimed to identify related biomarkers for IA and explore their underlying mechanisms.\nMETHODS: The GSE122897 and GSE75436 datasets were obtained from the Gene Expression Omnibus (GEO) database. Senescence and inflammation scores were calculated using single-sample GSEA (ssGSEA), and weighted gene co-expression network analysis (WGCNA) was performed to identify relevant modules and hub genes. Differentially expressed genes (DEGs) were determined with the DESeq2 package, followed by conducting LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) to screen key genes. Immune infiltration was analyzed using CIBERSORT and ESTIMATE algoritms, and correlations between key genes and immune cells were assessed. Finally, transcription factor (TF)-miRNA regulatory networks were constructed using the JASPAR package and ENCORI database.\nRESULTS: IA samples exhibited significantly higher senescence and inflammation scores in comparison to the controls. A total of 858 hub genes identified by WGCNA were intersected with the DEGs for further refinement by LASSO and SVM-RFE, ultimately yielding PTCH1, ACACB, DRD1, and SLC25A21-AS1 as the candidate genes for IA. Immune infiltration analysis showed that the expression of these genes was associated with several immune cells, including NK cells. Moreover, IA samples had higher ESTIMATEScore, ImmuneScore, and StromalScore, which were all negatively correlated with the expression of the four genes. TF-miRNA network analysis revealed 67, 71, and 76 potential TF regulators for PTCH1, ACACB, and SLC25A21-AS1, respectively.\nDISCUSSION: These findings indicated that senescence and inflammation contributed to IA pathogenesis and may regulate disease progression by modulating the immune microenvironment, highlighting their dual role in IA.\nCONCLUSION: PTCH1, ACACB, and SLC25A21-AS1 were identified as potential biomarkers associated with senescence and inflammation in IA, providing novel insights into its molecular mechanisms and potential diagnostic or therapeutic targets.", "claims": [{"id": "claim-method", "statement": "Different from previous studies, this study was the first to combine WGCNA with two machine learning algorithms (Lasso and SVM-RFE) with a focus on aging and inflammation as entry points to systematically screen key genes closely associated with IA and further explore their correlation with immune cell infiltration.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41487007", "locator": "sections[20] \"DISCUSSION\" paragraph 2", "quote": "Different from previous studies, this study was the first to combine WGCNA with two machine learning algorithms (Lasso and SVM-RFE) with a focus on aging and inflammation as entry points to systematically screen key genes closely associated with IA and further explore their correlation with immune cell infiltration."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the GSE122897 repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122897) and GSE75436 repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75436).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41487007", "locator": "sections[28] \"AVAILABILITY OF DATA AND MATERIALS\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the GSE122897 repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122897) and GSE75436 repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75436)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified PTCH1, ACACB, and SLC25A21-AS1 as potential biomarkers associated with senescence and inflammation in intracranial aneurysm.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41487007", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "PTCH1, ACACB, and SLC25A21-AS1 were identified as potential biomarkers associated with senescence and inflammation in IA, providing novel insights into its molecular mechanisms and potential diagnostic or therapeutic targets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified PTCH1, ACACB, and SLC25A21-AS1 as potential biomarkers associated with senescence and inflammation in intracranial aneurysm.", "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:41487007"], "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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