Comprehensive Bioinformatics Analysis for the Identification of Hub Genes and Critical Signaling Pathways Differentiating Latent and Active Tuberculosis.

Abstract

Objectives: Population with Latent tuberculosis infection (LTBI) is the principal source of active tuberculosis (ATB) cases. The identification of reliable diagnostic biomarkers is critical for the prevention and control of the progression from LTBI to ATB. The aim of this study is to screen biomarkers that can distinguish LTBI from ATB patients by using a comprehensive bioinformatics analysis strategy.

Methods: The transcriptomic datasets were obtained from the GEO database. Hub genes and critical signal pathways for differentiating latent and active TB, were identified by a comprehensive bioinformatics analysis strategy comprising Weighted Gene Co-Expression Network Analysis (WGCNA), Differentially Expressed Gene (DEG), Protein-Protein Interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and hub genes were verified by RT-qPCR in this study.

Results: The transcriptome profiles of GSE193777, GSE157657, GSE168519, GSE107991, and GSE107992 were extracted from the GEO database, in which a total of 18,397 protein-coding genes from 206 samples were included in the bioinformatics analysis. Combined with Weighted Gene Co-Expression Network, differentially expressed gene, functional enrichment, and proteinprotein interaction analyses, six hub genes were identified. The results of RT-qPCR confirmed that the expression levels of four hub genes (HLA-DOA, ECH1, PARN and TRAPPC4) were downregulated in the LTBI group compared with the ATB group.

Conclusion: Our findings may provide crucial clues to potential biomarkers that can distinguish patients with LTBI from those with ATB, aiding the understanding of the mechanism underlying the progression of LTBI to ATB.