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Genetics and Genomics

Metabolic expression profiling stratifies diffuse lower-grade glioma into three distinct tumour subtypes

British Journal of Cancer volume 125, pages 255–264 (2021)Cite this article

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Abstract

Background

Lower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs.

Methods

The metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype.

Results

We successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction.

Conclusions

Our study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs.

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Fig. 1: Identification of distinct metabolic subtypes in diffuse lower-grade glioma through metabolism gene profiling.
Fig. 2: Clinical characteristics of metabolic subtypes in TCGA and CGGA cohorts.
Fig. 3: Immune infiltration of three metabolic subtypes in TCGA and CGGA cohorts.
Fig. 4: Association between metabolism-relevant signatures and LGG subtypes.
Fig. 5: Somatic variations associated with metabolic expression subtypes in the TCGA cohort.
Fig. 6: Identification of a metabolic signature associated with overall survival by Cox proportional hazards model.

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Acknowledgements

The authors conducting this work represent the Chinese Glioma Cooperative Group (CGCG).

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Author notes

  1. These authors contributed equally: Fan Wu, Yan-Wei Liu, Guan-Zhang Li.

Authors and Affiliations

  1. Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Fan Wu, You Zhai, Yue-Mei Feng, Wen-Ping Ma, Zheng Zhao & Wei Zhang

  2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Fan Wu, Yan-Wei Liu, Guan-Zhang Li, You Zhai, Yue-Mei Feng, Wen-Ping Ma, Zheng Zhao & Wei Zhang

  3. Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing, China

    Fan Wu, Yan-Wei Liu, Guan-Zhang Li, You Zhai, Yue-Mei Feng, Wen-Ping Ma, Zheng Zhao & Wei Zhang

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Contributions

W.Z., Z.Z. and W.M.: conceptualisation and supervision; F.W., Y.L. and G.L.: methodology, data curation and writing—original draft preparation; Y.Z. and Y.F.: data collection, software and writing—reviewing and editing.

Corresponding authors

Correspondence to Wen-Ping Ma, Zheng Zhao or Wei Zhang.

Ethics declarations

Ethics approval and consent to participate

This study was carried out in accordance with the Helsinki declaration and approved by the ethics committee of Tiantan hospital, and patient informed consents existed in these two public databases.

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Not applicable.

Data availability

All data in this study are available in TCGA, GLASS, GEO and CGGA datasets.

Competing interests

The authors declare no competing interests.

Funding information

This work was supported by National Natural Science Foundation of China (81672479, 82002994, 82002647 and 81702460) and Natural Science Foundation of Anhui Province (1908085QH335).

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Wu, F., Liu, YW., Li, GZ. et al. Metabolic expression profiling stratifies diffuse lower-grade glioma into three distinct tumour subtypes. Br J Cancer 125, 255–264 (2021). https://doi.org/10.1038/s41416-021-01418-6

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