Identification of post-translational modification-related biomarkers in ischemic stroke using bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41214111", "kind": "science-record", "revision": 3, "content_hash": "sha256:ca93629b7776eb4c6688e7fabf94bdaf05d722663408f741ad28c816ab4fbe04", "created_at": "2026-11-01T20:31:14Z", "updated_at": "2026-11-01T22:14:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of post-translational modification-related biomarkers in ischemic stroke using bioinformatics and machine learning.", "doi": "10.1038/s41598-025-22997-x", "pmid": "41214111", "authors": ["Xiaonan Bian", "Haiyang Fu", "Wenqiang Sun", "Nan Wang", "Haimei Liu", "Weidong Han"], "journal": "Scientific reports", "year": 2025, "abstract": "Ischemic stroke (IS) remains a major clinical challenge due to the difficulty of early diagnosis and incomplete understanding of its pathological mechanisms. Post-translational modifications (PTMs) regulate key cellular processes in IS, but their roles as diagnostic biomarkers and therapeutic targets have not been fully elucidated. This study aimed to identify PTM-related genes (PTMRGs) associated with IS and evaluate their diagnostic and therapeutic potential using comprehensive bioinformatics and machine learning methods. Gene expression data from two GEO cohorts (GSE16561, training group n = 63; GSE58294, testing group n = 92) were analyzed. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and intersection with known PTMRGs were performed to screen candidate genes. Protein-protein interaction (PPI) networks and machine learning algorithms (Boruta, SVM-RFE, LASSO) were used to prioritize biomarkers. The expression of key genes was validated in clinical samples by RT-qPCR. An artificial neural network (ANN) was constructed to evaluate diagnostic performance. In addition, immune infiltration, gene set enrichment analysis (GSEA), gene-gene interaction (GGI), and molecular docking analyses were conducted to explore biological functions and therapeutic candidates. A total of 1465 upregulated genes and 1782 downregulated genes were identified. WGCNA revealed modules significantly associated with IS, yielding 75 key PTMRGs identified after intersection with DEGs and PTMRGs. Six genes (ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15) were identified as diagnostic markers with AUC > 0.7. RT-qPCR in 10 IS patients and 10 controls confirmed differential expression, consistent with bioinformatics results. The ANN model showed high diagnostic accuracy (AUC = 0.983 in training, 0.95 in testing). Functional enrichment linked these genes to ubiquitin-mediated proteolysis, DNA repair, and Myc signaling. Immune analysis showed associations with CD8 + T cells and neutrophils. Molecular docking suggested that ETYA may interact with UBE2I and KAT2A. In conclusion, the six diagnostic genes and the ANN model provide powerful diagnostic tools for IS, and ETYA may have a potential role in the treatment of IS.", "claims": [{"id": "claim-method", "statement": "Based on the grouping design of 39 stroke patients and 24 healthy controls, a linear model combined with empirical Bayes correction was used for differential expression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41214111", "locator": "sections[4] \"Screening of genes with significantly differential expression\" paragraph 2", "quote": "Based on the grouping design of 39 stroke patients and 24 healthy controls, a linear model combined with empirical Bayes correction was used for differential expression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository under the accession numbers GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561) and GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41214111", "locator": "sections[41] \"notes\" paragraph 1", "quote": "The datasets analysed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository under the accession numbers GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561) and GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Six genes (ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15) were identified as diagnostic markers with AUC > 0.7.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41214111", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Six genes (ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15) were identified as diagnostic markers with AUC > 0.7."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Six genes (ATG7, KAT2A, RNF20, UBA1, UBE2I, and USP15) were identified as diagnostic markers with AUC > 0.7.", "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:41214111"], "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:41214111", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-22997-x", "sha256": "sha256:f0f7e962bcb3ee4042ca9649891d621542147da04158f88cbbe55c9301819724", "bytes": 44110, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-22997-x", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12603072", "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 analysed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository under the accession numbers GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561) and GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analysed during the current study are publicly available in the Gene Expression Omnibus (GEO) repository under the accession numbers GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16561) and GSE58294 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58294). 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An artificial neural network (ANN) was cons", "tified as diagnostic markers with AUC > 0.7. RT-qPCR in 10 IS patients and 10 controls confirmed", "enes was validated in clinical samples by RT-qPCR. An artificial neural network (ANN) was cons", "ied as diagnostic markers with AUC > 0.7. RT-qPCR in 10 IS patients and 10 controls confirmed"], "order_basis": "paper_text_order:1105", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["SVM"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}