Abstract
Background Acute respiratory distress syndrome (ARDS) is a major lung injury disease, and the most common cause is sepsis. Angiogenesis is vital in the process of diseaseoccurrence. Several angiogenesis related pathways have been identified to play an important role in ARDS. Hence, it was vital to screen the angiogenesis-related biomarkers for the treatment of sepsis-induced ARDS (SI-ARDS).Methods We introduced transcriptome data to filter differentially expressed genes (DEGs) in SI-ARDS. Venn diagram was executed to identify angiogenesis-related differentially expressed genes (AR-DEGs). Pearson correlation was utilised to obtain AR-DEGs highly correlated with SI-ARDS. PPI network was executed to gain core genes. Further, least absolute shrinkage and selection operator (LASSO) regression was implemented to retain biomarkers. Receiver operating characteristic (ROC) curves were conducted to estimate diagnostic model. The immune infltration circumstance was analyzed by ssGSEA algorithms. The miRNAs-transcription factor (TFs) and ceRNA network were predicted via miRTarBase, miRNet and AnimalTFDB database, respectively.Results We identified 108 DEGs associated with SI-ARDS. Then, 22 AR-DEGs highly correlated with SI-ARDS were obtainedpearson correlation. Subsequently, 6 angiogenesis-related biomarkers were identified, including LTF, OLFM4, CEACAM8, MME, BPI, and TFPI. Moreover, we got six significantly differential immune cells in ARDS samples induced by sepsis, among which neutrophils and MDSC infiltration had the highest correlation with TFPI, MME. Finally, the constructed ceRNA regulatory network was composed of 87 nodes and 192 edges. Some potential TFs targeting angiogenesis-related biomarkers were identified, including CEBPE and DCH1.Conclusion Overall, we obtained six angiogenesis-related biomarkers (LTF, OLFM4, CEACAM8, MME, BPI, TFPI) associated with SI-ARDS, which laid a theoretical foundation for the treatment of SI-ARDS.