Identification of molecular diagnostic markers in sepsis-induced acute lung injury

Abstract

Background： Sepsis-induced acute lung injury (ALI) is characterized by disruption of the epithelial barrier and activation of alveolar macrophages (AMs), which leads to uncontrolled pulmonary inflammation. However, effective treatments for ALI are unavailable. This study aimed to discover potential diagnostic molecular biomarkers based on bioinformatics, which will benefit the diagnosis and treatment of sepsis-induced ALI. Methods: GSE10474 was analyzed for differentially expressed genes (DEGs) in sepsis patients with ALI (sepsis + ALI) compared with sepsis patients without ALI. Functional enrichment analysis and protein-protein interaction (PPI) network were performed. on the DEGs via R package “clusterProfiler”, and visualized via Cytoscape. Prediction analysis of microarrays (PAM) was performed to identify diagnostic biomarkers and the diagnostic ability of diagnostic biomarkers was accessed via receiver operating characteristic (ROC) curves. Moreover, interactions among diagnostic biomarkers were analyzed via GeneMANIA. We also analyzed the function of diagnostic biomarkers and predicted their corresponding drugs via Cytoscape plugin BiNGO and web tool DGIdb. At last, we analyzed the transcriptional regulation of the diagnostic biomarkers via the web tool miRNet. Results: 71 genes were found to be differentially expressed in the sepsis + ALI group, mainly involved in immune-related biological processes and pathways. STRING database indicated 31 DEGs have protein-protein interactions. In addition, the PAM identified 6 diagnostic biomarkers, including HIST1H4H, CDKN1A, HMOX1, NQO2, RHOB, and TREM1, from these 31 DEGs. Conclusion: In conclusion, through bioinformatics analyses, we identified 6 potential diagnostic biomarkers and targets for sepsis-induced ALI.