关键词: 结核, 感染, 数据库, 胞葬作用, 基因

Abstract:

Objective: To screen efferocytosis-related genes as biomarkers for distinguishing active tuberculosis (ATB) and latent tuberculosis infection (LTBI) in human patients. Methods: The microarray dataset GSE28623, comprising peripheral blood samples from 46 ATB patients and 25 LTBI individuals, was downloaded from the GEO database. The R package limma was used to screen out differentially expressed genes (DEGs). Immune infiltration analysis was performed using CIBERSORT to evaluate the immune microenvironment status in patients with ATB and LTBI; subsequent GO and KEGG pathway enrichment analysis were conducted on the upregulated DEGs identified in ATB patients. Efferocytosis-related DEGs (EF-DEGs) were identified by intersecting the DEGs with an efferocytosis-related gene set. LASSO regression and SVM-RFE machine learning algorithms were then applied to screen for key ATB genes. Finally, dataset GSE101705 was used to further validate the expression levels of the key genes. ROC curves were plotted to evaluate the discriminatory performance of these key genes in distinguishing ATB from LTBI. Results: Differential analysis identified 460 upregulated DEGs and 991 downregulated DEGs. Immune infiltration analysis indicated that ATB patients exhibited significantly increased proportions of neutrophils (P=1.45×10^-7) and M0 macrophages (P=7.55×10^-6), while the proportions of naive CD4⁺ T cells (P=0.003), CD8⁺ T cells (P=1.45×10^-7) and naive B cells (P=0.026) were significantly decreased. GO enrichment analysis revealed significant enrichment of upregulated DEGs in ATB across three ontologies: biological processes for responses to molecules of bacterial origin, response to lipopolysaccharide, and myeloid leukocyte activation; cellular components including secretory granule lumen, cytoplasmic vesicle lumen, and vesicle lumen; molecular functions involving immune receptor activity, pattern recognition receptor activity, and lipopolysaccharide binding. KEGG pathway analysis further demonstrated that these ATB-upregulated DEGs were prominently involved in lipid and atherosclerosis, efferocytosis, and the NOD-like receptor signaling pathway. Thirteen EF-DEGs (PROS1, SIAH2, CD274, WDFY3, SCARF1, ABCA1, DYNLT1, FPR2, CLU, IL1B, TNFSF13B, ITGB3, PLAUR) were identified through intersecting the upregulated DEGs with the efferocytosis-related gene set. Through LASSO regression and SVM-RFE machine learning algorithms, CD274, PROS1, and SIAH2 were identified as key genes associated with ATB. External validation using dataset GSE101705 confirmed that the expression levels of CD274, PROS1, and SIAH2 were significantly higher in the peripheral blood of ATB patients compared to LTBI individuals. The AUC values for discriminating ATB were 89.5% (95%CI: 79.9%-99.1%), 88.4% (95%CI: 78.7%-98.1%), and 79.0% (95%CI: 64.6%-93.5%) for CD274, PROS1 and SIAH2, respectively. The combined discriminatory AUC value for the three genes reached 93.6% (95%CI: 86.5%-100.0%). Conclusion: Based on the analysis of public database peripheral blood gene expression profiles from ATB and LTBI patients, this study identified CD274, PROS1 and SIAH2 as potential biomarkers with good discriminatory value for ATB.

Key words: Tuberculosis, Infection, Database, Efferocytosis, Genes