ArticleScientific reports2025
Identification and analysis of the endoplasmic reticulum stress hub genes in sepsis-associated ARDS.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparison between eDNA and traditional morphological methods for fish diversity monitoring in rivers.Scientific reports · 2026Article
- Crosstalk between innate immune signaling pathways and integrated TLR, NLRP3 inflammasome, cGAS-STING, and NF-κB networks in sepsis.Frontiers in cell and developmental biology · 2026Review
- Mechanisms and therapeutics of immunometabolic reprogramming driving macrophage-ECs interactions in sepsis-associated ARDS from the gut-lung axis perspective.Frontiers in immunology · 2026Review
- Divergent T Cell Phenotypes Define Pediatric Crohn's Disease and Ulcerative Colitis.medRxiv : the preprint server for health sciences · 2025Article
- Integration of Network Pharmacology, Molecular Docking, and Experimental Validation to Identify the Effect of EGCG on Alleviating Chondrocyte Inflammatory Damage by Targeting ER Stress-STAT3 Crosstalk.Journal of inflammation research · 2025Article
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3 authors.
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Abstract
Acute respiratory distress syndrome (ARDS) is one of the most common and serious complications in the development of sepsis. Endoplasmic reticulum stress (ERS) plays an important role in the pathophysiologic process of sepsis-associated ARDS. The aim of this study was to identify and analyze hub genes related to ERS in sepsis-associated ARDS using bioinformatics and machine learning algorithms, which may serve as diagnostic markers and therapeutic targets. Based on the GSE32707 dataset from the GEO database, differentially expressed genes (DEGs) between patients with sepsis-associated acute respiratory distress syndrome (ARDS) and healthy controls were identified. A comprehensive evaluation was performed by integrating functional enrichment analysis, immune cell infiltration analysis, and weighted gene co-expression network analysis (WGCNA). By intersecting DEGs, key WGCNA module genes, and ERS-related genes(ERGs), ERS-associated differential genes in sepsis-related ARDS were obtained. Subsequently, three machine learning algorithms-least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM)-were used to further screen for hub ERS hub genes. The diagnostic value of these hub genes was assessed using receiver operating characteristic (ROC) curve analysis. Finally, their expression levels were validated in clinical samples using RT-qPCR. A total of 438 DEGs and five hub genes-STAT3, HSPB1, YWHAQ, LCN2, and SGK1-were identified.Diagnostic performance analysis demonstrated that all five genes had favorable discriminatory power, indicating their potential clinical utility.Further validation in clinical samples confirmed the reliability of the bioinformatics analysis. RT-qPCR results showed that STAT3 was significantly upregulated, while YWHAQ was significantly downregulated in sepsis-associated ARDS samples compared to healthy controls, with both differences reaching statistical significance. In conclusion, STAT3 and YWHAQ, as ERS-related key genes, not only play pivotal roles in sepsis-associated ARDS but also hold promise as diagnostic biomarkers and potential therapeutic targets.
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