Introduction: Tuberculosis (TB) is a major global public health problem. Its pathogenesis,
however, is not fully understood. The purpose of this study was to identify key genes for TB by a
bioinformatics analysis of gene expression profiles.
Methods: We downloaded the gene expression profiles of TB from the Gene Expression Omnibus and
identified differentially-expressed genes (DEGs) and highly-enriched pathways between TB patients
and healthy controls. We then identified differentially co-expressed genes (DCGs), differentially coexpressed
links (DCLs), differentially-regulated genes (DRGs) and differentially-regulated links
(DRLs) using Differential Co-Expression Analysis (DCEA) and Differential Regulation Analysis
(DRA). In addition, we constructed a TF bridged DCL-centered network by mapping the DCGs to
known regulatory data between transcription factors (TFs) and target genes. We then calculated the
TED, TDD and regulatory impact factor (RIF) of each TF.
Results: A total of 5540 DEGs, 61DCGs, 3915 DCLs, 59 DRGs and 1139 DRLs were identified
between TB patients and healthy controls. KEGG pathway enrichment analysis identified the lysosome
as the most significantly-enriched pathway. Based on their TED, TDD and RIF scores, the REL, TAL1,
RELA, NFKB1, NF-kappaB2, Cart-1, TCF3, MZF1, POU2F2 and EPAS1 transcription factors may
play key roles in tuberculosis. Of these genes, REL and TAL1 were the only two among the top 20
genes of the three algorithms and may therefore paly more significant roles in tuberculosis.
Conclusions: REL and TAL1 may play more significant roles in tuberculosis. However, more
laboratory work is needed to validate our results.