Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37386280", "kind": "science-record", "revision": 4, "content_hash": "sha256:72f633767c7dff0555ab6ca02b40ab4a37b69cd0a137d521a651d0851aecd1f4", "created_at": "2026-11-01T19:02:08Z", "updated_at": "2026-11-01T22:52:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke.", "doi": "10.1038/s41598-023-37753-2", "pmid": "37386280", "authors": ["Kai Yang", "Zhaoqi Zhang", "Xiaoju Liu", "Tong Wang", "Zhicheng Jia", "Xin Li", "Wei Liu"], "journal": "Scientific reports", "year": 2023, "abstract": "Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between ischemic stroke and control samples in the GSE16561 were identified by the package of limma in R language (adj. p. values < 0.05 and |log2FC| ≥ 0.5)18,19.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37386280", "locator": "sections[4] \"Differential expression analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) between ischemic stroke and control samples in the GSE16561 were identified by the package of limma in R language (adj. p. values < 0.05 and |log2FC| ≥ 0.5)18,19."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37386280", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified SLC2A3, MYH9, CREB1, and SAP30 as hypoxia-related diagnostic biomarkers for ischemic stroke and validated their diagnostic performance in an external cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37386280", "locator": "sections[14] \"Construction of the diagnostic risk model\" paragraph 2", "quote": "Then, multivariate logistic regression analysis was used to determine the independent candidate diagnostic biomarkers. Four genes (SLC2A3, MYH9, CREB, and SAP30) were obtained (Fig. 4D)."}, {"asset": "source:pmid:37386280", "locator": "sections[15] \"External validation of diagnostic model performance\" paragraph 2", "quote": "And the GSE58294 dataset was used to validate the diagnostic accuracy of the four HRGs biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified SLC2A3, MYH9, CREB1, and SAP30 as hypoxia-related diagnostic biomarkers for ischemic stroke and validated their diagnostic performance in an external cohort.", "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:37386280"], "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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