Identification of Immune Infiltration and Effective Immune Biomarkers in Acute Lung Injury by Bioinformatics Analysis

Abstract

Acute lung injury (ALI) is a serious complication in clinical settings. This study aimed to elucidate the immune molecular mechanisms underlying ALI by bioinformatics analysis. Human ALI and six ALI mouse model datasets were collected. Immune cell infiltration between the ALI samples and non-ALI controls was estimated using the ssGSEA algorithm. Least absolute shrinkage and selection operator (LASSO) regression analysis and Wilcoxon test were performed to obtain the significantly different immune cell infiltration types. Immune feature genes were screened by differential analysis and the weighted correlation network analysis (WGCNA) algorithm. Functional enrichment was then performed and candidate hub biomarkers were identified. Finally, the receiver operator characteristic curve (ROC) analysis was used to predict their diagnostic performances. Three significantly different immune cell types (B cells, CD4 T cells, and CD8 T cells) were identified between the ALI samples and controls. A total of 13 immune feature genes were obtained by WGCNA and differential analysis and found to be significantly associated with immune functions and lung diseases. Four hub genes, including CD180, CD4, CD74, and MCL1 were identified using cytoHubba and were shown to have good specificity and sensitivity for the diagnosis of ALI. Correlation analysis suggested that CD4 was positively associated with T cells, whereas MCL1 was negatively correlated with B and T cells. We found that CD180, CD4, CD74, and MCL1 can serve as specific immune biomarkers for ALI. MCL1–B cell, MCL1–T cell, and CD4–T cell axes may be involved in the progression of ALI.