Integrative transcriptomic and bioinformatic analyses predict candidate EMT-related genes in sepsis-associated acute lung injury

Sepsis-associated acute lung injury (sepsis-ALI) is a complex pathological condition; its underlying mechanisms remain mostly obscure. Thus, in this study, we aimed to explore potential candidate molecular markers, infer the regulatory signaling pathways, and describe the immunological profiles of sepsis-ALI. We developed a comprehensive bioinformatics analytical workflow by combining human transcriptome datasets with single-cell RNA sequencing databases and applied the ComBat algorithm to remove batch effects. A hierarchical gene screening process, incorporating the support vector machine, least absolute shrinkage and selection operator, and random forest machine learning algorithms, was applied. Five epithelial–mesenchymal transition (EMT)–related candidate genes (AURKA, MYB, CCNA2, CD24, and TYMS) were suggested across these algorithms. These candidate genes, which are involved in histone phosphorylation signaling, were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Single-sample gene set enrichment analysis was used to profile immune cell infiltration patterns, which led to the identification of seven differentially abundant immune cell populations. CellChat analysis suggested the MIF-(CD74 + CXCR4) axis serves as a putative mediator of intercellular communication based on ligand‑receptor expression patterns in sepsis-ALI. Pseudo-time trajectory analysis revealed that CD24 expression and MYB expression are associated with EMT progression and display stage‑specific functional relevance along the inferred cellular trajectory. Together, these integrative multi-resolution transcriptomic analysis findings describe EMT-related molecular characteristics, stage-specific regulatory pathways, and immune processes in sepsis-ALI. It is noteworthy that all these results are hypothesis-generating and derived solely from bioinformatics analyses without experimental validation. Identification of candidate genes and the putative role of the MIF-(CD74 + CXCR4) axis provides preliminary insights into sepsis-ALI pathophysiology; these findings will serve as a basis for further experimental research to develop potential precision-based and stage-specific experimental treatment strategies for sepsis-ALI in the future.