Identifying pyroptosis-related genes as novel therapeutic targets in diabetic foot ulceration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40750912", "kind": "science-record", "revision": 4, "content_hash": "sha256:975ba30fdd6e6f9f3e942f2744f5ca3a6e18fd9439841b87044bafef490c653c", "created_at": "2026-11-01T20:30:50Z", "updated_at": "2026-11-01T22:56:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying pyroptosis-related genes as novel therapeutic targets in diabetic foot ulceration.", "doi": "10.1186/s13098-025-01880-9", "pmid": "40750912", "authors": ["Kang Liu", "Lei Lei", "Xin-Lei Yang", "Xin-He Zhang"], "journal": "Diabetology & metabolic syndrome", "year": 2025, "abstract": "Diabetic foot ulcer (DFU) is considered one of the most challenging chronic complications for individuals with diabetes, and its global incidence continues to rise, imposing a substantial burden on society. During the treatment of DFU, the locally intense inflammatory response induced by pyroptosis may adversely affect wound healing, making this phenomenon a focus of research. This study employed bioinformatics methods to systematically analyze the role of pyroptosis-related genes (PRGs) in the healing process of diabetic foot ulcers. Based on public datasets GSE147890 and GSE80178, we performed differential expression analysis, random forest, and LASSO regression to screen for key genes, and constructed and evaluated a multivariate logistic regression diagnostic model. We analyzed the GEO datasets GSE147890 and GSE80178, identifying 1336 and 2727 differentially expressed genes (DEGs), respectively. The intersection analysis with PRGs revealed 9 pyroptosis-related differentially expressed genes (PRDEGs). Functional enrichment analysis associated these genes with pathways such as I-kappaB kinase/nuclear factor-kappaB (IKK/NF-κB) signaling and mitophagy. Six key PRDEGs (FSTL1, PINK1, HDAC3, ULK1, CPTP, and NOD2) were selected, and a diagnostic model was constructed using random forest and LASSO regression. The accuracy of the model was assessed through multivariate logistic regression, calibration curve analysis, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis. The model demonstrated excellent diagnostic performance, with an area under the curve (AUC) of 1.000 in both the training and validation sets. This study highlights the importance of PRGs in diabetic wound healing (DWH). Our findings not only elucidate the mechanisms of action of PRGs in diabetic wound healing but also provide a theoretical basis for the development of clinical early diagnosis and individualized treatment strategies, holding significant clinical application potential. Future research should validate these findings in larger populations and explore therapeutic interventions targeting these pathways to improve DWH outcomes.", "claims": [{"id": "claim-method", "statement": "Variations in gene expression were analyzed through the limma R package (version 3.58.1), with|logFC| >0 and P < 0.05 as the criteria for identifying DEGs (differentially expressed genes).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40750912", "locator": "sections[4] \"Differential gene expression analysis\" paragraph 2", "quote": "Variations in gene expression were analyzed through the limma R package (version 3.58.1), with|logFC| >0 and P < 0.05 as the criteria for identifying DEGs (differentially expressed genes)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (GSE147890 and GSE80178) analyzed in this study were extracted from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40750912", "locator": "sections[34] \"Data availability\" paragraph 2", "quote": "The datasets (GSE147890 and GSE80178) analyzed in this study were extracted from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The multivariable logistic regression model had an area under the ROC curve of 1.000 in both the training and validation datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40750912", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The model demonstrated excellent diagnostic performance, with an area under the curve (AUC) of 1.000 in both the training and validation sets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The multivariable logistic regression model had an area under the ROC curve of 1.000 in both the training and validation datasets.", "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:40750912"], "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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