Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research

作者：Zhanpeng Chen, Qiong J. Wang, Quan Ma, Jinyun Chen, Xingxing Kong, Yuqin Zeng, Lanlan Liu, Shuihua Lu, Xiaomin Wang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-86951-7 · 被引用次数：3 · 研究领域：Tuberculosis Research and Epidemiology、Genetics, Bioinformatics, and Biomedical Research、vaccines and immunoinformatics approaches

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 wit...