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<p>Low-grade glioma (LGG) is a highly heterogeneous tumor, and this study aims to develop a metabolism-based classifier to identify patients with distinct prognostic risks and treatment responses for precision therapy. We utilized gene expression profiles, mutation, and clinical data from the TCGA-LGG cohort. Unsupervised clustering was applied to identify metabolism subtypes, and differences in clinical features, survival, and drug sensitivity were analyzed. Four key feature genes—SNAP91, TAGLN2, GLMP, and MCUB—were identified through machine learning, and an artificial neural network (ANN) classifier was constructed to accurately classify LGG patients into two metabolism subtypes. The results revealed significant differences in gene expression profiles and mutational landscapes between the subtypes. The metabolism subtype is a clinically independent prognostic predictor of LGG, subtype C2 has a poorer prognosis. Drug sensitivity analysis showed that subtype C2 had lower 50% inhibitory concentration(IC50) and area under curve(AUC) values for TMZ. Subsequent experimental validation revealed that high expression of TAGLN2 drove tumor progression by reprogramming cellular metabolism, including enhancing oxidative phosphorylation and activating the PI3K/Akt signaling pathway​ in LGG cells. Our findings highlight the potential of metabolism subtyping as a tool for precision treatment strategies in LGG.</p>

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Development of a metabolic subtype classifier for low-grade glioma to guide precision therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Yiqi Tan,
  • Le Zeng,
  • Ganghua Zhang,
  • Jianing Fang,
  • Zhijing Yin,
  • Wenzhi Deng,
  • Ke Cao,
  • Jiaode Jiang

摘要

Low-grade glioma (LGG) is a highly heterogeneous tumor, and this study aims to develop a metabolism-based classifier to identify patients with distinct prognostic risks and treatment responses for precision therapy. We utilized gene expression profiles, mutation, and clinical data from the TCGA-LGG cohort. Unsupervised clustering was applied to identify metabolism subtypes, and differences in clinical features, survival, and drug sensitivity were analyzed. Four key feature genes—SNAP91, TAGLN2, GLMP, and MCUB—were identified through machine learning, and an artificial neural network (ANN) classifier was constructed to accurately classify LGG patients into two metabolism subtypes. The results revealed significant differences in gene expression profiles and mutational landscapes between the subtypes. The metabolism subtype is a clinically independent prognostic predictor of LGG, subtype C2 has a poorer prognosis. Drug sensitivity analysis showed that subtype C2 had lower 50% inhibitory concentration(IC50) and area under curve(AUC) values for TMZ. Subsequent experimental validation revealed that high expression of TAGLN2 drove tumor progression by reprogramming cellular metabolism, including enhancing oxidative phosphorylation and activating the PI3K/Akt signaling pathway​ in LGG cells. Our findings highlight the potential of metabolism subtyping as a tool for precision treatment strategies in LGG.