Research Paper Volume 10, Issue 11 pp 3185—3209

Identification of an energy metabolism-related signature associated with clinical prognosis in diffuse glioma

Zhengui Zhou 1, 2, *, , Ruoyu Huang 1, 3, 4, *, , Ruichao Chai 1, 3, 4, *, , Xiaohong Zhou 2, , Zhiping Hu 2, , Wenbiao Wang 2, , Baoguo Chen 2, , Lintao Deng 2, , Yuqing Liu 1, 3, 4, , Fan Wu 1, 3, 4, ,

  • 1 Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China
  • 2 Department of Cerebral Surgery, The People’s Hospital of Gongan County, Hu Bei, Gongan 434300, China
  • 3 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
  • 4 Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing 100050, China
* Equal contribution

received: July 19, 2018 ; accepted: October 27, 2018 ; published: November 8, 2018 ;

https://doi.org/10.18632/aging.101625
How to Cite

Copyright: Zhou et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Now, numerous exciting findings have been yielded in the field of energy metabolism within glioma cells. In addition to aerobic glycolysis, multiple catabolic pathways are employed for energy production. However, the prognostic significance of energy metabolism in glioma remains obscure. Here, we explored the relationship between energy metabolism gene profile and outcome of diffuse glioma patients using The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Altas (CGGA) datasets. Based on the gene expression profile, consensus clustering identified two robust clusters of glioma patients with distinguished prognostic and molecular features. With the Cox proportional hazards model with elastic net penalty, an energy metabolism-related signature was built to evaluate patients’ prognosis. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low and high-risk groups of patients in both cohorts. Moreover, the signature, significantly associated with the clinical and molecular features, could serve as an independent prognostic factor for glioma patients. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) showed that gene sets correlated with high-risk group were involved in immune and inflammatory response, with the low-risk group were mainly related to glutamate receptor signaling pathway. Our results provided new insight into energy metabolism role in diffuse glioma.

Abbreviations

CGGA: Chinese Glioma Genome Atlas; TCGA: The Cancer Genome Atlas; OS: overall survival; LGG: lower grade glioma; GBM: glioblastoma; HR: hazard ratio; CI: confidence interval; GO: gene ontology; GSEA: gene set enrichment analysis; OXPHOS: oxidative phosphorylation; TCA: tricarboxylic acid; AUC: area under the curve; PCA: principal components analysis.