The impact of PANoptosis-related genes on immune profiles and subtype classification in ischemic stroke.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41136446", "kind": "science-record", "revision": 4, "content_hash": "sha256:0232aca9f82262ba91933bb8ea16f711bb396fea0348861d1d1da43d40c0ea54", "created_at": "2026-11-01T20:31:11Z", "updated_at": "2026-11-01T22:57:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The impact of PANoptosis-related genes on immune profiles and subtype classification in ischemic stroke.", "doi": "10.1038/s41598-025-20997-5", "pmid": "41136446", "authors": ["Xinrui Cai", "Yu Ren", "Haiyun Wu", "Yongjun Tan", "Li Zhou", "Yilin Wang", "Qinghuan Yang", "Jiani Wang", "Hao Tang", "Jun Wen", "Yong Zhao", "Ling Wang", "Xinlei Xu", "Jiagui Huang", "Qin Yang"], "journal": "Scientific reports", "year": 2025, "abstract": "Ischemic stroke (IS) is an acute neurological disorder causing brain dysfunction, with high mortality and disability. PANoptosis is a synchronized sort of regulated cell demise that combines the characteristics of pyroptosis, apoptosis, and necroptosis. However, its role in ischemic stroke remains unclear. We downloaded the ischemic stroke-related microarray dataset GSE58294 from the Gene Expression Omnibus (GEO) database and identified Differentially expressed PANoptosis-related genes (DE-PRGs). We utilized three algorithms to identify diagnostic DE-PRGs, constructed a nomogram for diagnostic modeling, and evaluated their diagnostic performance with receiver operating characteristic (ROC) analysis. Additionally, we develop interaction networks linking genes with miRNAs, transcription factors, and drugs. We also analyzed gene expression in different cell subgroups using the GSE174574 single-cell dataset. We applied consensus clustering to classify stroke samples into subtypes and compared immune microenvironment differences. Ultimately, we verified the gene expression patterns of candidate markers through the MCAO model. We pinpointed seven diagnostic DE-PRGs, with CASP1, PIK3R5, AVEN, and PSMC3 showing significant protein expression differences. Single-cell transcriptome analysis revealed the link between stroke and immune cells. Furthermore, consensus clustering revealed two clusters with unique immune infiltration patterns and functional characteristics. Our study may provide new theoretical insights for the early diagnosis and targeted therapy of IS and offer support for the clinical application of PANoptosis in IS.", "claims": [{"id": "claim-method", "statement": "Potentially diagnostic markers for Ischemic stroke were identified through three distinct machine-learning approaches: Random forest (RF), Least absolute shrinkage and selection operator (LASSO) regression, and the Support vector machine-recursive feature elimination (SVM-RFE).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41136446", "locator": "sections[12] \"Screening and validation of PANoptosis diagnostic markers\" paragraph 2", "quote": "Potentially diagnostic markers for Ischemic stroke were identified through three distinct machine-learning approaches: Random forest (RF), Least absolute shrinkage and selection operator (LASSO) regression, and the Support vector machine-recursive feature elimination (SVM-RFE)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the [Gene Expression Omnibus database] repository, [https://www.ncbi.nlm.nih.gov/geo/]. The GEO accessions are [GSE58294], [GSE16561], [GSE174574].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41136446", "locator": "sections[31] \"notes\" paragraph 1", "quote": "The datasets supporting the conclusions of this article are available in the [Gene Expression Omnibus database] repository, [https://www.ncbi.nlm.nih.gov/geo/]. The GEO accessions are [GSE58294], [GSE16561], [GSE174574]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We pinpointed seven diagnostic DE-PRGs, with CASP1, PIK3R5, AVEN, and PSMC3 showing significant protein expression differences.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41136446", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We pinpointed seven diagnostic DE-PRGs, with CASP1, PIK3R5, AVEN, and PSMC3 showing significant protein expression differences."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We pinpointed seven diagnostic DE-PRGs, with CASP1, PIK3R5, AVEN, and PSMC3 showing significant protein expression differences.", "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:41136446"], "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": "blocked", "blocked_on": "method:machine_learning_random_forest", "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": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41136446", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-20997-5", "sha256": "sha256:40452134adc815fe7a43ff918c17ef86341cc32bf19f41b34da16f82037919da", "bytes": 46892, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-20997-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12552703", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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 supporting the conclusions of this article are available in the [Gene Expression Omnibus database] repository, [https://www.ncbi.nlm.nih.gov/geo/]. 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