Evidence map›Paper›PMID 42856839›Full record

ArticleFrontiers in cellular and infection microbiology2026

DNA damage repair-related gene signatures delineate immune-distinct subtypes and diagnostic cues in tuberculosis.

Abdusemer Reyimu, Mamatali Rahman, Amina Abudurousuli, Xiang Cheng, Xiu Liu, Aimin Xu, Xiaoguang Zou

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Abdusemer Reyimu *Department of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Mamatali Rahman *Department of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Amina Abudurousuli *Department of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Xiang ChengDepartment of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Xiu LiuDepartment of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Aimin XuDepartment of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.
Xiaoguang ZouDepartment of Laboratory Medicine, The First People's Hospital of Kashi, Xinjiang, Kashi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

DNA DamageDNA RepairMycobacterium tuberculosisTranscriptomeTuberculosisBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningBiomarkerscompound screeningDNA damage repairimmune infiltrationmachine learningmolecular subtypessingle-cell RNA sequencingtuberculosis

Identifiers

PMID42856839
PMCPMC13649433

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.