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Bioinformatic Profiling Identifies a Glucose-Related Risk Signature for the Malignancy of Glioma and the Survival of Patients

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Abstract

The aim of this study is to investigate the glucose metabolic status and its prognostic value in glioma. The Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and GSE16011 datasets were used to develop the glucose-related signature. A cohort of 305 glioma samples with whole genome microarray expression data from the Chinese Glioma Genome Atlas database was included for discovery. TCGA and GSE16011 datasets were used for validation. Gene Set Enrichment Analysis (GSEA) and Cytoscape were used to explore the bioinformatic implication. GSEA revealed the biological process associated with the glucose-related signature. Cytoscape visualized the correlation analysis among the genes. We also collected the blood glucose information of patients with gliomas to analyze the association with tumor malignancy and patients’ survival. In this study, we identified that glucose-related gene sets could distinguish the clinical and molecular features of gliomas, involved in the malignancy of gliomas. And then, we developed a glucose-related prognostic signature for patients with glioblastoma in the CGGA dataset, validated in other additional public datasets. GSEA illustrated that tumor with higher risk score of glucose-related signature could correlate with cell cycle phase. In addition, blood glucose concentration was associated with the malignancy of glioma and the survival of patients. These results might provide new view for the research of glioma malignancy and individual treatment. Our research provided important resources for future dissection of glucose metabolic role in glioma.

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Acknowledgments

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

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    Authors and Affiliations

    1. Department of Health Management, School of Public Health, Harbin Medical University, No. 157 Baojian Road, NanGang District, Harbin, Heilongjiang Province, 150081, China

      Shihong Zhao & Lihua Fan

    2. Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China

      Shihong Zhao, Jinquan Cai, Jianlong Li, Guiqiu Bao, Di Li, Yongli Li & Chuanlu Jiang

    3. Neuroscience Institute, Heilongjiang Academy of Medical Sciences, Harbin, 150086, China

      Jinquan Cai & Chuanlu Jiang

    4. Glioma Cooperative Group (CGCG), Beijing, 100050, China

      Jinquan Cai, Jianlong Li, Yongli Li & Chuanlu Jiang

    5. Department of Neurosurgery, Daqing LongNan Hospital, Daqing, 163001, China

      Xiuwei Zhai

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    1. Shihong Zhao
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    Corresponding authors

    Correspondence to Chuanlu Jiang or Lihua Fan.

    Ethics declarations

    The study protocol was approved by the ethics committees of participating hospitals, and all patients provided written informed consent.

    Conflict of Interest

    The authors declare that they have no conflict of interest.

    Funding

    Supported by grants from (1) The Research Special Fund For Public Welfare Industry of Heath (No. 201402008); (2) The National Key Research and Development Plan (No. 2016YFC0902500); (3) National Natural Science Foundation of China (No. 81572701, No. 81372700); (4) Special Fund Project of Translational Medicine in the Chinese-Russian Medical Research Center (No. CR201417); and (5) Research Project of Chinese Society of Neuro-oncology, CACA (CSNO-2014-MSD08).

    Additional information

    Shihong Zhao and Jinquan Cai contributed equally to this work.

    Electronic supplementary material

    Supplementary Figure S1

    The consensus clustering for the gene expression profiling of 305 samples from the CGGA datasets based on the top 50 variable expression genes (MAD >1.0) when k was equal to 6 to 10. (TIFF 1610 kb)

    High resolution image (GIF 393 kb)

    Table S1

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    Table S2

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    Table S3

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    Table S5

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    Zhao, S., Cai, J., Li, J. et al. Bioinformatic Profiling Identifies a Glucose-Related Risk Signature for the Malignancy of Glioma and the Survival of Patients. Mol Neurobiol 54, 8203–8210 (2017). https://doi.org/10.1007/s12035-016-0314-4

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