Using Integrated Bioinformatics Strategy to Identify Differentially Expressed Genes and Hub Genes of Human Hosts with Tuberculosis

ORIGINAL ARTICLE
Peng Yue, Yan Dong, Fukai Bao, Aihua Liu

 
For citation: Yue P, Dong Y, Bao F, Liu A. Using Integrated Bioinformatics Strategy to Identify Differentially Expressed Genes and Hub Genes of Human Hosts with Tuberculosis. International Journal of Biomedicine. 2025;15(4):704-714. doi:10.21103/Article15(4)_OA10
 
Originally published December 5, 2025

Abstract: 

Background: To date, the molecular mechanisms underlying the occurrence, development, and prognosis of tuberculosis remain incompletely understood. The study aimed to identify the host hub involved in tuberculosis.
Methods and Results: Four gene expression profiles (GSE51029, GSE52819, GSE54992, and GSE65517) were downloaded from Gene Expression Omnibus (GEO). First, the selected data sets of the Mycobacterium tuberculosis (MTB) infection group and the healthy control group were analyzed through GEO2R, and the genes that met the following conditions: |log FC|> 1 and P-values <0.05, are considered differentially expressed genes (DEGs). Secondly, the DEGs shared by the 4 microarray datasets were further identified. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs, the host hub genes were identified by the Cytohubba plugin, and module networks in DEG networks were screened by the plugin Molecular Complexity Detection (MCODE). Other bioinformatics methods were performed, including protein-protein interaction (PPI) network analysis and the construction of miRNA-hub gene networks and transcription factor (TF)-hub gene networks. Finally, the expression of the host hub genes was verified by real-time PCR.
Four GEO microarray datasets were integrated, and a total of 46 DEGs were identified. The results of the GO analysis showed that the biological functions of DEGs were primarily involved in regulating the immune response process, cytokine/chemokine activity, and receptor-ligand activity. DEGs were also significantly enriched in membrane rafts, the mitochondrial outer membrane, cytoplasmic vesicle cavities, and nuclear chromatin. KEGG enrichment analysis showed that the NOD-like receptor signaling pathway and the Toll-like receptor signaling pathway were 2 important pathways. In addition, 5 highly differentially expressed hub genes, STAT1, TLR7, CXCL8, CCR2, and CCL20, were screened out. Finally, based on the NetworkAnalyst database, we screened targeted miRNAs and TF of hub genes and found that hsa-miR-335-3p may play a key role in the regulation of these hub genes.
Conclusion: 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 5 essential host hub genes that can be potentially useful for early detection, prognostic determination, risk assessment, and targeted tuberculosis therapy.

Keywords: 
tuberculosis • Mycobacterium tuberculosis • GEO dataset • miRNA • hub gene network • bioinformatics analysis
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Received October 30, 2025.
Accepted November 29, 2025.
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