Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis
Abstract While dysregulated autophagy has been linked to acute respiratory distress syndrome (ARDS) development in sepsis, the exact regulatory mechanisms driving this process remain unclear. This study systematically investigated autophagy-related genes in sepsis-induced ARDS using integrative bioi...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92409-7 |
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| author | Wei Wang Jianfeng Zhao Hui Li Dabing Huang Shuiqiao Fu Zhitao Li |
| author_facet | Wei Wang Jianfeng Zhao Hui Li Dabing Huang Shuiqiao Fu Zhitao Li |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Abstract While dysregulated autophagy has been linked to acute respiratory distress syndrome (ARDS) development in sepsis, the exact regulatory mechanisms driving this process remain unclear. This study systematically investigated autophagy-related genes in sepsis-induced ARDS using integrative bioinformatics, including weighted gene coexpression network analysis (WGCNA), differential gene expression analysis (DEGs), receiver operating characteristic (ROC) curve analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein‒protein interaction (PPI) network analysis, and immune infiltration analysis. Hub genes were further validated by qPCR in Beas-2B cells receiving lipopolysaccharide (LPS) stimulation. We identified 18 autophagy-related DEGs with diagnostic potential for sepsis-induced ARDS. These DEGs were linked to endocytosis, protein kinase inhibition, and enigmatic Ficolin-1-rich granules. The downregulated hallmark signaling pathways involved apoptosis, complement, IL-2/STAT5, and KRAS signaling. Immune infiltration analysis revealed alterations in 7 immune cell subsets, including CD8 + T-cell exhaustion, natural killer cell reduction, and the type 1 helper T-cell response. When Beas-2B cells were treated with LPS, we discovered that 6 out of the 18 hub genes were significantly downregulated. Our findings provide novel insights into autophagy-mediated ARDS pathogenesis in sepsis. The hub genes represent promising candidates for clinical biomarker development and therapeutic targeting, which necessitates further validation. |
| format | Article |
| id | doaj-art-9f858e0ebea34cb99f417e40adc65157 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-9f858e0ebea34cb99f417e40adc651572025-08-20T03:06:01ZengNature PortfolioScientific Reports2045-23222025-03-0115111210.1038/s41598-025-92409-7Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysisWei Wang0Jianfeng Zhao1Hui Li2Dabing Huang3Shuiqiao Fu4Zhitao Li5Department of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityDepartment of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityDepartment of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityDepartment of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityDepartment of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityDepartment of Surgical Intensive Care Unit, First Affiliated Hospital, School of Medicine, Zhejiang UniversityAbstract While dysregulated autophagy has been linked to acute respiratory distress syndrome (ARDS) development in sepsis, the exact regulatory mechanisms driving this process remain unclear. This study systematically investigated autophagy-related genes in sepsis-induced ARDS using integrative bioinformatics, including weighted gene coexpression network analysis (WGCNA), differential gene expression analysis (DEGs), receiver operating characteristic (ROC) curve analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein‒protein interaction (PPI) network analysis, and immune infiltration analysis. Hub genes were further validated by qPCR in Beas-2B cells receiving lipopolysaccharide (LPS) stimulation. We identified 18 autophagy-related DEGs with diagnostic potential for sepsis-induced ARDS. These DEGs were linked to endocytosis, protein kinase inhibition, and enigmatic Ficolin-1-rich granules. The downregulated hallmark signaling pathways involved apoptosis, complement, IL-2/STAT5, and KRAS signaling. Immune infiltration analysis revealed alterations in 7 immune cell subsets, including CD8 + T-cell exhaustion, natural killer cell reduction, and the type 1 helper T-cell response. When Beas-2B cells were treated with LPS, we discovered that 6 out of the 18 hub genes were significantly downregulated. Our findings provide novel insights into autophagy-mediated ARDS pathogenesis in sepsis. The hub genes represent promising candidates for clinical biomarker development and therapeutic targeting, which necessitates further validation.https://doi.org/10.1038/s41598-025-92409-7AutophagyAcute respiratory distress syndromeSepsisBioinformatics |
| spellingShingle | Wei Wang Jianfeng Zhao Hui Li Dabing Huang Shuiqiao Fu Zhitao Li Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis Scientific Reports Autophagy Acute respiratory distress syndrome Sepsis Bioinformatics |
| title | Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis |
| title_full | Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis |
| title_fullStr | Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis |
| title_full_unstemmed | Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis |
| title_short | Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis |
| title_sort | autophagy related biomarkers identified in sepsis induced ards through bioinformatics analysis |
| topic | Autophagy Acute respiratory distress syndrome Sepsis Bioinformatics |
| url | https://doi.org/10.1038/s41598-025-92409-7 |
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