Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40697417", "kind": "science-record", "revision": 4, "content_hash": "sha256:c8cdcac02ff6afab8b2d72a13ce7614b5427dd06535fe888ef07c14873033b0b", "created_at": "2026-11-01T20:30:48Z", "updated_at": "2026-11-01T22:56:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis.", "doi": "10.2147/JIR.S529689", "pmid": "40697417", "authors": ["Tingting Liu", "Ling Gao", "Xiaoyan Li"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Purpose: This study aims to identify key genes associated with Neutrophil Extracellular Traps (NETs) in sepsis-associated Acute Respiratory Distress Syndrome (ARDS) using bioinformatics and molecular docking for diagnostic and therapeutic purposes.\nMethods: We obtained the GSE32707 datasets from the GEO database and selected the gene expression profiles of sepsis-associated ARDS patients and healthy controls. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis and immune infiltration analysis. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to explore gene co-expression modules. The differential genes of the above screen were crossed with NETs gene sets to obtain the key NETs genes for sepsis-associated ARDS. Three machine learning algorithms were applied to refine the intersected genes. The expression of hub genes in clinical blood samples was verified by RT-qPCR. Molecular docking was conducted to predict small molecular compounds targeting hub genes.\nResults: Analysis of the GSE32707 dataset using R software revealed 485 differential genes for sepsis-associated ARDS. WGCNA identified 332 common genes in the gene module associated with sepsis-associated ARDS. The differential genes of the above screen were crossed with NETs gene sets to obtain the key NETs genes for sepsis-associated ARDS. Further through machine learning, LTF and PRTN3 were identified as hub genes with excellent diagnostic potential. RT-qPCR analysis showed that PRTN3 and LTF expression were significantly upregulated in sepsis-associated ARDS patients as compared with healthy controls. Molecular docking results showed that nimesulide and minocycline were identified as potential therapeutic drugs for sepsis-associated ARDS.\nConclusion: LTF and PRTN3 are identified as key NETs genes in sepsis-associated ARDS and show promise as effective molecular markers for disease diagnosis and potential therapeutic targets.", "claims": [{"id": "claim-method", "statement": "Screening for differentially expressed genes (DEGs) between the sepsis-associated ARDS group and the healthy control group in the GSE32707 dataset using the limma package in R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40697417", "locator": "sections[4] \"Screening for Differentially Expressed Genes\" paragraph 2", "quote": "Screening for differentially expressed genes (DEGs) between the sepsis-associated ARDS group and the healthy control group in the GSE32707 dataset using the limma package in R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data supporting the findings of this study are available in the NCBI Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40697417", "locator": "sections[30] \"Data Sharing Statement\" paragraph 2", "quote": "The data supporting the findings of this study are available in the NCBI Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results showed that LTF (AUC = 0.832), PRTN3 (AUC = 0.790) (Figure 8).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40697417", "locator": "sections[23] \"Validation and Diagnostic Efficacy Analysis of the Two Core Genes\" paragraph 2", "quote": "The results showed that LTF (AUC = 0.832), PRTN3 (AUC = 0.790) (Figure 8)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The results showed that LTF (AUC = 0.832), PRTN3 (AUC = 0.790) (Figure 8).", "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:40697417"], "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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