Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis - Liu T; Gao L; Li X (Journal of Inflammation Research)
Tingting Liu,1 Ling Gao,1 Xiaoyan Li2 1Department of Respiratory and Critical Care Medicine, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, Shanxi, 030032, People’s Republic of China; 2Department of Pulmonary Critical Care Medicine, Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, 201318, People’s Republic of ChinaCorrespondence: Xiaoyan Li, Department of Pulmonary Critical Care Medicine, Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, 201318, People’s Republic of China, Email xy740922@126.comPurpose: 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.Keywords: sepsis, ARDS, neutrophil extracellular traps, bioinformatics