ArticleScientific reports2025
Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Integrating single-cell RNA-seq and bulk RNA-seq to identify hub pathways and diagnostic biomarkers in active pulmonary tuberculosis.BMC infectious diseases · 2026Article
- The Evolving Landscape of Host Biomarkers for Diagnosis and Monitoring of Tuberculosis.Biomedicines · 2025Review
- Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes.European journal of medical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global public health issue with high mortality rates and challenges posed by drug-resistant strains, emphasizing the continued need for new therapeutic targets and effective treatment strategies. Transcriptomics is a highly effective tool for the development of novel anti-tuberculosis drugs. However, most studies focus only on changes in gene expression levels at specific time points. This study screened for genes with altered expression patterns from available transcriptomic data and analysed their association with the TB progression. Initially, a total of 1228 genes with altered expression patterns were identified through two-way analysis of variance (ANOVA). We define genes with a P-value less than 0.05 for the combined effect of infection and time on gene expression as those with altered expression patterns. Gene Ontology (GO) enrichment analysis revealed that the biological functions of these genes mainly involve DNA translation, RNA processing, and transcriptional regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that these genes are primarily associated with fatty acid degradation, pyruvate metabolism, arginine and proline metabolism, as well as cholesterol metabolism signaling pathways. Subsequent Protein-protein interaction (PPI) analysis and Receiver Operating Characteristic (ROC) curve analysis identified four core genes closely associated with TB progression, namely Rac Family Small GTPase 1 (RAC1), Ring-Box 1 (RBX1), Mitochondrial Ribosomal Protein L33 (MRPL33), and ELAV Like RNA Binding Protein 1 (ELAVL1). Q-PCR experiments confirmed that Mtb infection led to changes in the gene expression patterns of RAC1, RBX1, MRPL33, and ELAVL1 in THP-1 cells. These four genes may serve as core biomarkers for TB progression and can be utilized in the development of more effective anti-tuberculosis drugs and host therapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.