Integrative transcriptomic meta-analysis reveals conserved transcriptional signatures and predictive biomarkers for active tuberculosis: a pathway-based machine learning approach

Background: Tuberculosis (TB) caused 1.23 million deaths in 2024, with accurate diagnosis hampered by population heterogeneity and limited biomarker generalizability. We developed an integrative framework combining multi-cohort transcriptomics and machine learning to identify host-derived transcriptional signatures of active TB. Methods: Five transcriptomic datasets (GSE83456, GSE107995, GSE158802, GSE19435, GSE25534) comprising 529 samples were analyzed. After standardized preprocessing, we performed differential expression analysis, inverse variance-weighted meta-analysis, and single-sample gene set enrichment analysis (ssGSEA) for three KEGG pathways. Machine learning classifiers were developed using logistic regression with SHapley Additive exPlanations (SHAP)-based interpretability. Results: . Conclusion: Our integrative framework identifies a conserved 347-gene transcriptional signature and three key immune pathways that transcend population and technical heterogeneity. The high diagnostic accuracy and biologically interpretable feature sets provide validated biomarkers for TB diagnosis and support clinical translation toward precision medicine approaches in global TB control. Clinical trial registration: https://www.chictr.org.cn/, identifier ChiCTR2300074328.