In silico transcriptomic analysis for biomarker discovery in TB and HIV co-infection

Tuberculosis (TB) and human immunodeficiency virus (HIV) are significant global health challenges, often coexisting and complicating disease progression, diagnosis, and treatment. Tuberculous meningitis (TBM), the most severe form of TB, adds to this burden. Identifying robust biomarkers is crucial for early detection, disease monitoring, and targeted therapies. This study employs an integrated in-silico approach to analyse gene expression across multiple cohorts, including healthy individuals, MTB-infected, HIV-positive, HIV-MTB co-infected, and TBM cases.

Gene expression datasets from the NCBI GEO database (GSE165708, GSE111459) were analysed to identify differentially expressed genes (DEGs) associated with MTB, HIV, and TBM. Data processing was conducted using R software and Bioconductor packages. Functional enrichment analysis (Gene Ontology and KEGG pathways) was performed to identify key biological processes and pathways. A gene co-expression network was constructed to pinpoint hub genes with diagnostic and prognostic potential.

Our Insilico-based analysis identified several novel biomarker candidates, including DDX58, IFIH1, IFIT3, ISG15, MX1, RSAD2, IFI44, CD8A, IL2RB, and LCK, implicated across MTB, HIV, HIV-MTB, and TBM infections. In parallel, we validated established immune-related biomarkers such as TNF, STAT1, IRF1, IRF7, IL1B, CD4, TLR2, and CD28, underscoring their pivotal roles in infection-associated immune modulation.

This study demonstrates the robust potential of in-silico approaches to uncover novel and established biomarkers in TB and HIV, offering new insights into the molecular mechanisms driving these complex infections. These findings provide a vital framework for future experimental validation and contribute to precision medicine at a molecular stratification level by enabling the identification of condition-specific and shared host transcriptional signatures across TB, HIV-TB co-infection, and TBM, which may inform future host-directed and pathway-targeted therapeutic strategies.