Frontiers in cellular and infection microbiology · 2026
DNA damage repair-related gene signatures delineate immune-distinct subtypes and diagnostic cues in tuberculosis.
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Reyimu Abdusemer, Rahman Mamatali, Abudurousuli Amina, Cheng Xiang, Liu Xiu, Xu Aimin, Zou Xiaoguang
Аннотация
BACKGROUND: DNA damage repair-related genes (DDR-RGs) may shape tuberculosis (TB) pathogenesis, but their molecular characteristics, diagnostic utility, and cellular context remain insufficiently defined.
METHODS: We integrated multiple GEO transcriptome datasets, identified differentially expressed genes, constructed weighted gene co-expression network analysis modules, and intersected these results with curated DDR-RGs to obtain candidate genes. An ensemble framework of 110 machine-learning model combinations was used to select diagnostic markers, followed by external validation, consensus clustering, immune infiltration analysis, single-cell RNA-seq evaluation, and Comparative Toxicogenomics Database-based compound-association analysis.
RESULTS: The integrated training cohort included 134 samples; 94 differentially expressed genes and a 392-gene key module were identified, yielding 25 candidate DDR-RGs. The Ridge + SVM model performed best and selected ETV7, STAT1, FBXO6, and TAP1 as core DDR-RGs, achieving AUCs of 0.991, 0.925, 0.939, and 0.981 in the training and three validation cohorts. Consensus clustering separated TB patients into two DDR-related subtypes, with Cluster 1 enriched in immune-related pathways and characterized by distinct immune-cell infiltration. Single-cell analysis revealed cell-type-specific expression of core DDR-RGs in pulmonary TB, and compound-association analysis identified 35 candidate compounds, including six associated with multiple core genes.
CONCLUSION: These findings characterize DDR-related molecular heterogeneity in TB and support ETV7, STAT1, FBXO6, and TAP1 as candidate diagnostic biomarkers and exploratory candidates for host-directed studies.
METHODS: We integrated multiple GEO transcriptome datasets, identified differentially expressed genes, constructed weighted gene co-expression network analysis modules, and intersected these results with curated DDR-RGs to obtain candidate genes. An ensemble framework of 110 machine-learning model combinations was used to select diagnostic markers, followed by external validation, consensus clustering, immune infiltration analysis, single-cell RNA-seq evaluation, and Comparative Toxicogenomics Database-based compound-association analysis.
RESULTS: The integrated training cohort included 134 samples; 94 differentially expressed genes and a 392-gene key module were identified, yielding 25 candidate DDR-RGs. The Ridge + SVM model performed best and selected ETV7, STAT1, FBXO6, and TAP1 as core DDR-RGs, achieving AUCs of 0.991, 0.925, 0.939, and 0.981 in the training and three validation cohorts. Consensus clustering separated TB patients into two DDR-related subtypes, with Cluster 1 enriched in immune-related pathways and characterized by distinct immune-cell infiltration. Single-cell analysis revealed cell-type-specific expression of core DDR-RGs in pulmonary TB, and compound-association analysis identified 35 candidate compounds, including six associated with multiple core genes.
CONCLUSION: These findings characterize DDR-related molecular heterogeneity in TB and support ETV7, STAT1, FBXO6, and TAP1 as candidate diagnostic biomarkers and exploratory candidates for host-directed studies.