miRNA Differential Expression Profile Analysis and Identification of Potential Key Genes in Active Tuberculosis

Abstract

Tuberculosis (TB), caused by Mycobacterium TB (MTB), remains a significant global health issue, particularly in developing nations. MicroRNAs (miRNAs) are non-coding RNAs (ncRNAs) that modulate immune responses and play a pivotal role in the pathogenesis of MTB by altering host immune defences. Insights into the regulatory functions of these miRNAs have revealed mechanisms through which MTB evades immune surveillance and establishes persistent infections, highlighting the critical role of miRNA networks in TB pathogenesis. The purpose of this study was to analyse miRNA expression in plasma from TB patients, to predict target genes, and to construct regulatory networks to elucidate the roles of miRNAs in TB pathogenesis. Plasma samples from three patients with active TB and three healthy controls were analysed using high-throughput small RNA sequencing. DEMs were identified using DESeq2, and target genes were predicted via TargetScan and miRWalk. Protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape. Functional enrichment analyses were performed using Gene Ontology (GO) and KEGG databases. A total of 23 DEMs were identified, including 17 upregulated and 6 downregulated miRNAs. hsa-miR-15a-5p emerged as the most significantly upregulated miRNA. PPI network analysis highlighted CCND1, CDK6 and CCND2 as central genes, potentially regulated by miR-15a-5p. GO and KEGG analyses revealed enrichment in pathways related to cell cycle regulation, kinase activity and protein complex formation, suggesting their involvement in TB pathogenesis. This study identifies hsa-miR-15a-5p and its target genes as key components in the regulatory landscape of TB. These findings offer new insights into the molecular mechanisms of TB and propose potential biomarkers and therapeutic targets for future research.

Keywords: active tuberculosis; differential expression; gene regulation; microRNA.

FIGURE 1

PCA plot of miRNA expression profiles in TB patients and healthy controls. Each point represents an individual sample. TB patient samples (experiment group) are shown as blue circles, and control samples as red squares.

FIGURE 2

DEMs between samples: (A) Volcano plot. (B) Differential miRNA clustering heatmap (S1–S3 were patient samples and S4–S6 were controls). (C) PPI regulatory network diagram (The network's colour transitions from yellow to red, and node size increases from small to large. The redder and larger a node, the greater its degree; the greener and smaller a node, the lesser its degree). (D) Venn diagram showing the intersection of the top 10 key nodes by Degree, Betweenness and MCC algorithms. Yellow: Top 10 key nodes by MCC algorithm; Blue: Top 10 key nodes by Degree algorithm; Green: Top 10 key nodes by Betweenness algorithm.

FIGURE 3

Differential miRNA target gene KEGG and GO enrichment bubble plot (The bubble size indicates the number of genes involved, and the colour gradient reflects the statistical significance, measured byFDR.). (A) KEGG Pathway enrichment; (B) GO Biological Process enrichment; (C) GO Molecular Function enrichment; (D) GO Cellular Component enrichment.