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We identified T-cell-associated metabolic differentially expressed genes (TCM\u2013DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM\u2013RFE, and Boruta). These TCM\u2013DEGs were then used to construct a diagnostic model and evaluate its clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The analysis revealed significant immunological alterations in TB patients, characterized by markedly elevated monocyte\/macrophage populations (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) accompanied by reduced T and NK cell counts. Notably, LTBI cases demonstrated an intermediate CD4+\/CD8+ T-cell ratio, indicative of dynamic immune homeostasis. The TB cohort exhibited increased inflammatory T-cell populations, while CD8+ T-cell-mediated MHC-I and BTLA signaling pathways were identified as key regulators of immune clearance and modulation.\n                    <\/jats:p>\n                    <jats:p>\n                      Transcriptomic profiling identified five metabolically significant differentially expressed genes (FHIT, MAN1C1, SLC4C7, NT5E, AKR1C3;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05) that effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (TB). The machine learning-driven diagnostic framework demonstrated remarkable consistency across independent validation cohorts (GSE39940, GSE39939), exhibiting AUC values spanning 0.867\u20130.873. Molecular subtyping analysis delineated two distinct TB phenotypes: an immune-activated M1 macrophage-dominant subtype and a CD8\u2009+\u2009T-cell infiltrated immunophenotype. Clinical validation substantiated the differential expression patterns of T-cell-related metabolic differentially expressed genes (TCM\u2013DEGs;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05), while the nomogram predictive model achieved exceptional discriminative capacity (C-index\u2009=\u20090.944), demonstrating superior clinical applicability through decision curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings reveal that TCM\u2013DEGs critically regulate TB progression through immune\u2013metabolic reprogramming and cell\u2013cell communication networks. The developed diagnostic model and molecular subtyping strategy enable precise TB\u2013LTBI differentiation and inform immunotherapy optimization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40001-025-02768-0","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T02:01:48Z","timestamp":1750039308000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes"],"prefix":"10.1186","volume":"30","author":[{"given":"Shoupeng","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghua","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"issue":"10481","key":"2768_CR1","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/S0140-6736(24)02479-6","volume":"405","author":"A Trajman","year":"2025","unstructured":"Trajman A, Campbell JR, Kunor T, et al. 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