Investigation of the relationship between chronic hepatitis B and tuberculosis using bioinformatics and systems biology approaches

Abstract

Background: Hepatitis B virus (HBV) is a globally prevalent pathogen that poses significant public health challenges. Active HBV replication can trigger immune responses that result in liver damage. Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains one of the leading causes of death from a single infectious agent worldwide. Notably, in TB patients with HBV infection and, the incidence of adverse events is six times higher than in those with TB alone, and HBV infection increases the risk of latent TB. However, the relationship between HBV and TB have not been thoroughly investigated.

Methods: To elucidate the relationship between HBV and TB, we performed an integrated bioinformatics analysis using expression profiling and RNA sequencing data from the GSE83148 and GSE126614 datasets. We identified differentially expressed genes (DEGs) associated with both diseases and analyzed shared biological pathways, key genes, transcriptional regulatory networks, and gene-disease associations. Furthermore, we predicted potential therapeutic agents targeting these shared molecular features.

Results: A total of 35 overlapping DEGs were identified for in-depth analysis. Functional enrichment revealed that these genes are involved in both immune-related pathways and cellular metabolic regulation, underscoring their potential role in the progression of HBV and TB. Protein-protein interaction (PPI) network analysis highlighted four hub genes: CCL2, CD69, EGR2, and CCL20. Additionally, 35 transcription factors (TFs) were predicted to regulate these hub genes. Several candidate drugs, including etoposide, 8-azaguanine, menaquinone, emetine and N-acetyl-L-cysteine, were identified as potential therapeutic options. The DEGs were also significantly associated with other conditions such as pneumonia.

Conclusion: This study provides novel insights into the relationship between HBV and TB, offering potential targets for diagnosis and treatment. Our findings may contribute to the development of integrated strategies to manage HBV infection and TB more effectively.

Keywords: differentially expressed genes; drug molecule; hepatitis B virus; hub gene; protein–protein network (PPI); tuberculosis.

Figure 1

Schematic illustration of the overall general work flow of this study.

Figure 2

The study incorporates HBV (GSE83148) and TB (GSE126614). The Venn diagram revealed 35 common DEGs of HBV and TB.

Figure 3

The bar chart of the GO assessment of the shared DEGs between HBV and TB. (A) Biological processes. (B) Cellular components. (C) Molecular function.

Figure 4

The bar graphs of the pathway enrichment of the shared DEGs between HBV and TB. (A) Reactome. (B) WikiPathways. (C) KEGG.

Figure 5

PPI network of the mutual DEGs between HBV and TB. The size and color depth of the circles represent the extent of protein intercorrelation. The most prominent nodes have been identified as hub genes. The nodes and the edges of the figure represent DEGs and the interactions between the nodes, respectively. The PPI network contains 15 edges and 18 nodes.

Figure 6

PPI network from all the shared DEGs is constructed by Cytohubba plugin in Cytosacpe. Red nodes present the selected top four hub genes. The network has 13 nodes and 17 edges.

Figure 7

Fold changes of HBV and TB at transcriptional level. Nine categories in different colors indicate nine responsive groups (|log₂ Fold Change| ≥1 and p-value <0.05).

Figure 8

The expression level of hub genes in HBV patients in the GSE84044 dataset. Each sample was evaluated for fibrosis stage (Scheuer S) and inflammation grade (Scheuer G) based on the Scheuer scoring system, according to the severity of inflammation and fibrosis. ^**p < 0.01, ^***p < 0.001, and ^****p < 0.0001.

Figure 9

DEG-TFs interaction network created by the NetworkAnalyst. The red nodes represent gene symbols interacting with TFs while the blue nodes represent TFs.