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<p>Diffuse large B-cell lymphoma (DLBCL) features an immunosuppressive tumor microenvironment (TME), yet the molecular drivers connecting metabolic reprogramming to immune evasion remain poorly defined. Here, we deployed an integrative single-cell transcriptomic analysis combined with a machine learning (ML) framework to systematically identify key immune-suppressive hubs in DLBCL. Through ML-driven prioritization of a 33-gene panel, PAICS emerged as a central node within an immunosuppressive B-cell subgroup. Functional assays confirmed that PAICS promotes lymphoma proliferation, survival, and tumor growth while establishing an immunosuppressive TME-marked by reduced IFN‑γ, elevated TGF‑β and IL‑10, and enhanced CD8⁺ T cell exhaustion. Mechanistically, we uncovered the IRF4-PAICS-LDHA axis: IRF4 transcriptionally activates PAICS, which physically interacts with LDHA to augment its activity, thereby skewing the NAD⁺/NADH balance toward metabolic immunosuppression. Importantly, our AI-aided approach not only identified this axis but also predicted its vulnerability to metabolic intervention: both methotrexate treatment and LDHA knockdown restored metabolic balance, reversed T‑cell exhaustion, and suppressed tumor growth. These findings highlight the power of ML in uncovering multi-targetable metabolic-immune networks and in guiding therapeutic strategies to overcome immune evasion in DLBCL.</p>

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AI-guided discovery of the IRF4-PAICS-LDHA axis as a multitarget hub linking tumor metabolism to CD8+ T cell exhaustion in DLBCL

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Zeyuan Wang,
  • Liye wang,
  • Siyu Qian,
  • Yue Zhang,
  • Qing Yang,
  • Zhenzhen Yang,
  • Shaoxuan Wu,
  • Meng Dong,
  • Zhiqi Zhang,
  • Xufeng Wei,
  • Minglei Yang,
  • Hui Meng,
  • Enjie Liu,
  • Guozhong Jiang,
  • Xudong Zhang,
  • Wencai Li,
  • Qingjiang Chen

摘要

Diffuse large B-cell lymphoma (DLBCL) features an immunosuppressive tumor microenvironment (TME), yet the molecular drivers connecting metabolic reprogramming to immune evasion remain poorly defined. Here, we deployed an integrative single-cell transcriptomic analysis combined with a machine learning (ML) framework to systematically identify key immune-suppressive hubs in DLBCL. Through ML-driven prioritization of a 33-gene panel, PAICS emerged as a central node within an immunosuppressive B-cell subgroup. Functional assays confirmed that PAICS promotes lymphoma proliferation, survival, and tumor growth while establishing an immunosuppressive TME-marked by reduced IFN‑γ, elevated TGF‑β and IL‑10, and enhanced CD8⁺ T cell exhaustion. Mechanistically, we uncovered the IRF4-PAICS-LDHA axis: IRF4 transcriptionally activates PAICS, which physically interacts with LDHA to augment its activity, thereby skewing the NAD⁺/NADH balance toward metabolic immunosuppression. Importantly, our AI-aided approach not only identified this axis but also predicted its vulnerability to metabolic intervention: both methotrexate treatment and LDHA knockdown restored metabolic balance, reversed T‑cell exhaustion, and suppressed tumor growth. These findings highlight the power of ML in uncovering multi-targetable metabolic-immune networks and in guiding therapeutic strategies to overcome immune evasion in DLBCL.