ArticleBMC pulmonary medicine2020
A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis.
Article in BMC pulmonary medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Insights into the host response to 'dormant' Mycobacterium tuberculosis utilizing 'Vitamin C-induced dormant Mtb' THP-1 cell infection model.BMC genomics · 2026Article
- Analysis of significance of CARD11 and MYO1G expressions in pulmonary tuberculosis and their predictive value for prognosis of recurrence.Journal of medical biochemistry · 2025Article
- Identification and role of differentially expressed genes/proteins between pulmonary tuberculosis patients and controls across lung tissues and blood samples.Immunity, inflammation and disease · 2024Article
- Advances in computational frameworks in the fight against TB: The way forward.Frontiers in pharmacology · 2023Review
- Review on Bovine Tuberculosis: An Emerging Disease Associated with Multidrug-ResistantPathogens (Basel, Switzerland) · 2022Review
- Biomarker Candidates for Alzheimer's Disease Unraveled through In Silico Differential Gene Expression Analysis.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlyingFrontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDue to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients.
methodsFirstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database.
resultsBased on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers.
conclusionsOverall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.
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