Microbial pathogenesis · 2026
Manganese metabolism-related gene signatures as diagnostic biomarkers for tuberculosis: Immune infiltration profiling and molecular subtype identification.
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Zhu Wenyuan, Fu Ting
Аннотация
BACKGROUND: Host metabolic programs are increasingly recognized as key modulators of tuberculosis (TB) immunopathology, yet manganese metabolism-related transcriptional signatures with diagnostic and immunological relevance remain insufficiently characterized. We aimed to identify manganese metabolism-linked biomarkers for TB diagnosis and to delineate their immune and regulatory context.
METHODS: Three Gene Expression Omnibus microarray datasets were analyzed. A curated Mn metabolism gene set was retrieved from GeneCards. Candidate diagnostic genes were prioritized by integrating Least Absolute Shrinkage and Selection Operator regression (glmnet), Support Vector Machine-Recursive Feature Elimination (caret), and Boruta feature selection. Diagnostic performance was assessed by Receiver Operating Characteristic analysis (pROC), and an eight-gene diagnostic model was visualized by a nomogram with calibration and Decision Curve Analysis. Immune landscapes were profiled using CIBERSORT (IOBR) and single-sample Gene Set Enrichment Analysis (Gene Set Variation Analysis, GSVA). We performed single-gene Gene Set Enrichment Analysis (GSEA) using MSigDB Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets to infer functional programs associated with each candidate gene. Non-negative Matrix Factorization clustering defined TB molecular subtypes, and a competing endogenous RNA (ceRNA) network was constructed using miRTarBase miRNA-mRNA interactions combined with curated lncRNA-miRNA links.
RESULTS: From 241 DEGs in GSE83456, 30 Mn-DEGs were obtained and were enriched in immune and infection-related pathways. Machine-learning integration converged on eight diagnostic genes. In training, individual AUCs ranged from 0.924 to 0.975 and the combined model reached 0.995; external validation achieved AUC = 1.000 in both cohorts. Immune analyses revealed TB-associated shifts toward innate/inflammatory signatures and gene-immune correlations, while single-gene GSEA highlighted host-defense, lysosomal, and innate signaling pathways. NMF identified two TB subtypes with distinct immune compositions and immune functional scores. The ceRNA network comprised 8 mRNAs, 28 miRNAs, and 65 lncRNAs.
CONCLUSION: An eight-gene manganese metabolism-associated signature enables accurate TB discrimination and captures immune heterogeneity, providing a framework for biomarker-guided stratification and mechanistic hypothesis generation.
METHODS: Three Gene Expression Omnibus microarray datasets were analyzed. A curated Mn metabolism gene set was retrieved from GeneCards. Candidate diagnostic genes were prioritized by integrating Least Absolute Shrinkage and Selection Operator regression (glmnet), Support Vector Machine-Recursive Feature Elimination (caret), and Boruta feature selection. Diagnostic performance was assessed by Receiver Operating Characteristic analysis (pROC), and an eight-gene diagnostic model was visualized by a nomogram with calibration and Decision Curve Analysis. Immune landscapes were profiled using CIBERSORT (IOBR) and single-sample Gene Set Enrichment Analysis (Gene Set Variation Analysis, GSVA). We performed single-gene Gene Set Enrichment Analysis (GSEA) using MSigDB Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene sets to infer functional programs associated with each candidate gene. Non-negative Matrix Factorization clustering defined TB molecular subtypes, and a competing endogenous RNA (ceRNA) network was constructed using miRTarBase miRNA-mRNA interactions combined with curated lncRNA-miRNA links.
RESULTS: From 241 DEGs in GSE83456, 30 Mn-DEGs were obtained and were enriched in immune and infection-related pathways. Machine-learning integration converged on eight diagnostic genes. In training, individual AUCs ranged from 0.924 to 0.975 and the combined model reached 0.995; external validation achieved AUC = 1.000 in both cohorts. Immune analyses revealed TB-associated shifts toward innate/inflammatory signatures and gene-immune correlations, while single-gene GSEA highlighted host-defense, lysosomal, and innate signaling pathways. NMF identified two TB subtypes with distinct immune compositions and immune functional scores. The ceRNA network comprised 8 mRNAs, 28 miRNAs, and 65 lncRNAs.
CONCLUSION: An eight-gene manganese metabolism-associated signature enables accurate TB discrimination and captures immune heterogeneity, providing a framework for biomarker-guided stratification and mechanistic hypothesis generation.