Scholarlink

<p>Metabolic reprogramming and immunosuppressive tumor microenvironment (TME) are hallmark features driving pancreatic ductal adenocarcinoma (PDAC) progression. Despite the therapeutic potential of targeting immunometabolism, effective strategies remain scarce in clinical practice, likely due to cell-specific metabolic heterogeneity within PDAC TME. Here, we show integration of three algorithms to estimate metabolic fluxomes and pathways using scRNA-seq data, generating a comprehensive cell type-specific metabolic atlas. Leveraging 460 PDAC samples, we establish a TME-metabolism subtyping system, classifying PDAC into three subtypes (TMS1-3) with distinct immune-metabolic profiles and clinical outcomes. TMS1, characterized by low immune infiltrates, is susceptible to ferroptosis inducers. TMS2, enriched in macrophages, responds to chemoimmunotherapy with inhibition of glutamine synthetase. TMS3, characterized by matrix remodeling, responds to glycolysis inhibitors and albumin-paclitaxel. Finally, we develop a computational classifier for subtype discrimination. Together, this study delineates the metabolic heterogeneity of the PDAC TME and proposes a classification system that suggests promising therapeutic targets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Metabolic characterization of the tumor microenvironment orchestrates therapeutic strategies and clinical outcomes in pancreatic cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

查看全文
  • Rong Tang,
  • Yangyi Li,
  • Cong Zhou,
  • Chunbin Zhu,
  • Chen Chen,
  • Liquan Jin,
  • Yueyue Chen,
  • Yingna Liao,
  • Yuan Liu,
  • Qiong Du,
  • Yubin Lei,
  • Zijian Wu,
  • Jin Xu,
  • Wei Wang,
  • Xiaoyu Yin,
  • Chenghao Shao,
  • Si Shi,
  • Xianjun Yu

摘要

Metabolic reprogramming and immunosuppressive tumor microenvironment (TME) are hallmark features driving pancreatic ductal adenocarcinoma (PDAC) progression. Despite the therapeutic potential of targeting immunometabolism, effective strategies remain scarce in clinical practice, likely due to cell-specific metabolic heterogeneity within PDAC TME. Here, we show integration of three algorithms to estimate metabolic fluxomes and pathways using scRNA-seq data, generating a comprehensive cell type-specific metabolic atlas. Leveraging 460 PDAC samples, we establish a TME-metabolism subtyping system, classifying PDAC into three subtypes (TMS1-3) with distinct immune-metabolic profiles and clinical outcomes. TMS1, characterized by low immune infiltrates, is susceptible to ferroptosis inducers. TMS2, enriched in macrophages, responds to chemoimmunotherapy with inhibition of glutamine synthetase. TMS3, characterized by matrix remodeling, responds to glycolysis inhibitors and albumin-paclitaxel. Finally, we develop a computational classifier for subtype discrimination. Together, this study delineates the metabolic heterogeneity of the PDAC TME and proposes a classification system that suggests promising therapeutic targets.