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        <datestamp>2026-09-25T04:30:31Z</datestamp>
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          <dc:title>Table 1_DNA damage repair-related gene signatures delineate immune-distinct subtypes and diagnostic cues in tuberculosis.xlsx</dc:title>
          <dc:creator>Abdusemer Reyimu (25107322)</dc:creator>
          <dc:creator>Mamatali Rahman (25107325)</dc:creator>
          <dc:creator>Amina Abudurousuli (25107328)</dc:creator>
          <dc:creator>Xiang Cheng (112921)</dc:creator>
          <dc:creator>Xiu Liu (2592091)</dc:creator>
          <dc:creator>Aimin Xu (120142)</dc:creator>
          <dc:creator>Xiaoguang Zou (6739946)</dc:creator>
          <dc:subject>Clinical Microbiology</dc:subject>
          <dc:subject>compound screening</dc:subject>
          <dc:subject>DNA damage repair</dc:subject>
          <dc:subject>immune infiltration</dc:subject>
          <dc:subject>machine learning</dc:subject>
          <dc:subject>molecular subtypes</dc:subject>
          <dc:subject>single-cell RNA sequencing</dc:subject>
          <dc:subject>tuberculosis</dc:subject>
          <dc:description>Background&lt;p&gt;DNA damage repair-related genes (DDR-RGs) may shape tuberculosis (TB) pathogenesis, but their molecular characteristics, diagnostic utility, and cellular context remain insufficiently defined.&lt;/p&gt;Methods&lt;p&gt;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.&lt;/p&gt;Results&lt;p&gt;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.&lt;/p&gt;Conclusion&lt;p&gt;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.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-25T04:30:31Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fcimb.2026.1885489.s003</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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