Using a consensus machine learning approach to identify host biomarkers associated with treatment response in
Ruiqi Zhang1, Fusheng Yao2, Wenqi Liu1
1National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen 518112, China.
Briefings in Bioinformatics
|December 2, 2025
Summary
Developing new biomarkers for tuberculosis (TB) treatment monitoring is crucial. This study identified Antimony (Sb), Copper (Cu), and Strontium (Sr) as promising trace element biomarkers for TB management.
Area of Science:
- Biochemistry
- Molecular Biology
- Computational Biology
Background:
- Monitoring tuberculosis (TB) treatment response lacks reliable biomarkers, risking under- or overtreatment.
- The standard six-month TB regimen necessitates better tools for evaluating treatment efficacy.
Purpose of the Study:
- To develop a novel evaluation model for TB treatment response using integrated biochemical profiles.
- To identify significant biomarkers for monitoring TB treatment effectiveness.
Main Methods:
- Integrated amino acid and trace element profiles with four machine learning feature selection methods.
- Investigated dynamic gene expression patterns linked to identified biomarkers.
- Analyzed public RNA-sequencing datasets to validate biomarker performance.
Main Results:
- Identified 34 potential biomarkers, with Antimony (Sb), Copper (Cu), and Strontium (Sr) consistently highlighted.
- Genes associated with Sb, Cu, and Sr showed high accuracy in distinguishing treated from untreated TB.
- Dynamic gene expression patterns involved programmed cell death, signal transduction, and immune response pathways.
Conclusions:
- The combination of Sb, Cu, and Sr shows potential as a novel laboratory criterion for monitoring TB treatment response.
- These biomarkers could offer a practical tool to guide clinical decisions and improve TB patient outcomes.
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