Identification of differentially expressed genes and hub genes of human hosts with tuberculosis through an integrated bioinformatics strategy

Abstract Background: Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis . Until now, molecular mechanisms underlying the occurrence, development and prognosis of tuberculosis have not been fully characterized. The aim of the study was to identify hub genes involved in tuberculosis. Methods: We used four microarray datasets (GSE51029, GSE52819, GSE54992, and GSE65517) from the Gene Expression Omnibus (GEO) and GEO2R software to identify differentially expressed genes (DEGs) between samples from humans infected with M. tuberculosis and a healthy control group (using cutoffs of LogFC > 1 and p value < 0.05). DEGs shared by the four microarray datasets were further identified. Next, we carried out functional enrichment analysis using the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG); Then, the host hub genes with a relatively high number of connections to other DEGs, were identified by Cytoscape. Other bioinformatics methods are also performed, including protein–protein interaction (PPI) network analysis and construction of miRNA–hub gene networks and transcription factors (TF)–hub gene networks. Finally, the expression of the host hub genes was verified using the reverse transcription polymerase chain reaction(RT–PCR). Results: A total of 46 DEGs were identified. GO analysis showed that the biological functions of DEGs were mainly in immune response regulation, cytokine/chemokine activity, and receptor ligand activity. DEGs were significantly enriched in membrane rafts, the mitochondrial outer membrane, cytoplasmic vesicle cavities, and nuclear chromatin. KEGG enrichment analysis showed involvement of the genes in the NOD-like receptor and toll-like receptor signaling pathways. Five highly differentially expressed hub genes – STAT1, TLR7, CXCL8, CCR2, and CCL20 – were identified. Finally, based on NetworkAnalyst's database, we constructed miRNA–hub gene networks and TF–hub gene networks. Conclusions: In summary, bioinformatics analyses were used to identify DEGs to find potential biomarkers that may be associated with tuberculosis. This study provides a set of candidate DEGs and five important hub genes that can be potential for the early detection, prognostic determination, risk assessment, and targeted therapy of tuberculosis.