Background: The morbidity and mortality associated with ALI continue to be significant. Few medical therapies have demonstrated efficacy in curbing the progression of ALI or improving its outcomes. However, time-series expression data has enhanced our ability to query dynamical processes, and WGCNA and maSigPro have emerged as a promising approach for processing large datasets. Therefore, it is possible for us to explore the molecular mechanism in the progression of ALI.Methods: Downloaded time

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... series gene expression dataset GSE2565 from the Gene Expression Omnibus (GEO), and normalized the dataset using the "sva" R package. maSigPro were used to screen Differential expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were performed to identify hub modules. Gene Ontology and pathway enrichment analyses of genes identified in the hub module were conducted by Metascape. Integration of module analysis and CytoHubba application for identifying hub genes. Finally, receiver operating characteristic (ROC) curve analysis was to measure the predictive accuracy of the hub genes.Results: In our study, 3005 DEGs were included in WGCNA and 8 modules were identified. Module-trait analysis presented that red module with the most negative correlation with 8hr mainly involved in the regulation of circadian rhythm; pink module with the most positive correlation with 8hr mainly involved in regulation of cell death. Five hub genes (Bnip3, Cdh11, Fam134b, Sult1a1 and Zbtb16) were identified followed by ROC curve analysis.Conclusion: Our analysis based on time-series expression data identified significant co-expression modules and pathways correlated with early phase of acute lung injury. The hub genes identified may contribute to provide new insights for the molecular mechanisms in acute lung injury.