Monocyte-related gene biomarkers for latent and active tuberculosis.
Yu LiYaju DengJie HePublished in: Bioengineered (2022)
Monocytes are closely associated with tuberculosis (TB). Latent tuberculosis in some patients gradually develops into its active state. This study aimed to investigate the role of hub monocyte-associated genes in distinguishing latent TB infection (LTBI) from active TB. The gene expression profiles of 15 peripheral blood mononuclear cells (PBMCs) samples were downloaded from the gene expression omnibus (GEO) database, GSE54992. The monocyte abundance was high in active TB as evaluated by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The limma test and correlation analysis documented 165 differentially expressed monocyte-related genes (DEMonRGs) between latent TB and active TB. Functional annotation and enrichment analyses of the DEMonRGs using the database for annotation, visualization, and integration discovery (DAVID) tools showed enrichment of inflammatory response mechanisms and immune-related pathways. A protein-protein interaction network was constructed with a node degree ≥10. The expression levels of these hub DEMonRGs ( SERPINA1, FUCA2 , and HP ) were evaluated and verified using several independent datasets and clinical settings. Finally, a single sample scoring method was used to establish a gene signature for the three DEMonRGs, distinguishing active TB from latent TB. The findings of the present study provide a better understanding of monocyte-related molecular fundamentals in TB progression and contribute to the identification of new potential biomarkers for the diagnosis of active TB.
Keyphrases
- mycobacterium tuberculosis
- dendritic cells
- gene expression
- peripheral blood
- bioinformatics analysis
- pulmonary tuberculosis
- inflammatory response
- endothelial cells
- genome wide
- small molecule
- protein protein
- dna methylation
- machine learning
- genome wide identification
- adverse drug
- microbial community
- prognostic factors
- long non coding rna
- antiretroviral therapy
- data analysis