Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37435058", "kind": "science-record", "revision": 4, "content_hash": "sha256:bccf6ed4891ddc5e9d677af883cc893a51b088297ea38c53e9ca2004b45e4035", "created_at": "2026-11-01T19:02:12Z", "updated_at": "2026-11-01T22:52:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients.", "doi": "10.3389/fimmu.2023.1164742", "pmid": "37435058", "authors": ["Wenli Chen", "Yuanfang Chen", "Liting Wu", "Yue Gao", "Hangju Zhu", "Ye Li", "Xinyu Ji", "Ziyi Wang", "Wen Wang", "Lei Han", "Baoli Zhu", "Hongxing Wang", "Ming Xu"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Stroke is the second leading cause of death and the third leading cause of disability worldwide, with ischemic stroke (IS) being the most prevalent. A substantial number of irreversible brain cell death occur in the short term, leading to impairment or death in IS. Limiting the loss of brain cells is the primary therapy target and a significant clinical issue for IS therapy. Our study aims to establish the gender specificity pattern from immune cell infiltration and four kinds of cell-death perspectives to improve IS diagnosis and therapy.\nMethods: Combining and standardizing two IS datasets (GSE16561 and GSE22255) from the GEO database, we used the CIBERSORT algorithm to investigate and compare the immune cell infiltration in different groups and genders. Then, ferroptosis-related differently expressed genes (FRDEGs), pyroptosis-related DEGs (PRDEGs), anoikis-related DEGs (ARDEGs), and cuproptosis-related DEGs (CRDEGs) between the IS patient group and the healthy control group were identified in men and women, respectively. Machine learning (ML) was finally used to generate the disease prediction model for cell death-related DEGs (CDRDEGs) and to screen biomarkers related to cell death involved in IS.\nResults: Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls. In total, 10 FRDEGs, 11 PRDEGs, 3 ARDEGs, and 1 CRDEG were present in male IS patients, while 6 FRDEGs, 16 PRDEGs, 4 ARDEGs, and 1 CRDEG existed in female IS patients. ML techniques indicated that the best diagnostic model for both male and female patients was the support vector machine (SVM) for CDRDEG genes. SVM's feature importance analysis demonstrated that SLC2A3, MMP9, C5AR1, ACSL1, and NLRP3 were the top five feature-important CDRDEGs in male IS patients. Meanwhile, the PDK4, SCL40A1, FAR1, CD163, and CD96 displayed their overwhelming influence on female IS patients.\nConclusion: These findings contribute to a better knowledge of immune cell infiltration and their corresponding molecular mechanisms of cell death and offer distinct clinically relevant biological targets for IS patients of different genders.", "claims": [{"id": "claim-method", "statement": "The criteria of “log2FC = 0.58” and “adjust-p = 0.05” were set, and the “limma” package in R was used to identify male and female FRDEGs, PRDEGs, ARDEGs, and CRDEGs, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37435058", "locator": "sections[5] \"Extraction and analysis of CDRDEGs\" paragraph 2", "quote": "The criteria of “log2FC = 0.58” and “adjust-p = 0.05” were set, and the “limma” package in R was used to identify male and female FRDEGs, PRDEGs, ARDEGs, and CRDEGs, respectively."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37435058", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37435058", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls.", "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:37435058"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16561"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16561"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37435058", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1164742", "sha256": "sha256:d14271c5c9d5f55cae51af1891f66763da70782ec422867f284d3dca97463266", "bytes": 44818, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1164742", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10332266", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE16561", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE16561", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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