ArticleScientific reports2023
Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Machine learning methods identified cellular senescence-related hub molecules in sepsis-induced acute respiratory distress syndrome (ARDS) and their upstream regulatory network.Molecular biology reports · 2026Article
- Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS.Communications medicine · 2026Article
- Screening of key genes in infectious ARDS and development of a disease risk prediction model.SAGE open medicine · 2026Article
- Identification and analysis of the endoplasmic reticulum stress hub genes in sepsis-associated ARDS.Scientific reports · 2025Article
- Immune Dysregulation and Hub Gene Identification in Non-Pulmonary Sepsis-Induced Acute Lung Injury: Insights from Transcriptomic and Experimental Analyses.Current medical science · 2025Article
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Bioinformatics approach reveals the critical role of inflammation-related genes in age-related hearing loss.Scientific reports · 2025Article
- From "metabolic storm" to "immune paralysis": the dynamic evolution of macrophages and metabolism reprogramming in ARDS.Frontiers in immunology · 2025Review
- TSLP pretreatment inhibits M1 macrophage polarization and attenuates LPS-induced iNKT cell-dependent acute lung injury.Frontiers in immunology · 2025Article
- Increased Levels of hsa-miR-199a-3p and hsa-miR-382-5p in Maternal and Neonatal Blood Plasma in the Case of Placenta Accreta Spectrum.International journal of molecular sciences · 2024Article
- Upregulation of PGC-1α expression by pioglitazone mediates prevention of sepsis-induced acute lung injury.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-induced acute respiratory distress syndrome (ARDS) is one of the leading causes of death in critically ill patients, and macrophages play very important roles in the pathogenesis and treatment of sepsis-induced ARDS. The aim of this study was to screen macrophage-related biomarkers for the diagnosis and treatment of sepsis-induced ARDS by bioinformatics and machine learning algorithms. A dataset including gene expression profiles of sepsis-induced ARDS patients and healthy controls was downloaded from the gene expression omnibus database. The limma package was used to screen 325 differentially expressed genes, and enrichment analysis suggested enrichment mainly in immune-related pathways and reactive oxygen metabolism pathways. The level of immune cell infiltration was analysed using the ssGSEA method, and then 506 macrophage-related genes were screened using WGCNA; 48 showed differential expression. PPI analysis was also performed. SVM-RFE and random forest map analysis were used to screen 10 genes. Three key genes, SGK1, DYSF and MSRB1, were obtained after validation with external datasets. ROC curves suggested that all three genes had good diagnostic efficacy. The nomogram model consisting of the three genes also had good diagnostic efficacy. This study provides new targets for the early diagnosis of sepsis-induced ARDS.
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