ArticleJournal of inflammation research2025
Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- From Food Contaminant to Therapeutic Target: Identification of KCNE2 and 5-Azacytidine for Gastric Cancer via Multi-Omics, Machine Learning, and In Vitro Validation.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Screening of key genes in infectious ARDS and development of a disease risk prediction model.SAGE open medicine · 2026Article
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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. Methods: 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. Results: 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. Conclusion: 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.
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