Construction and Verification of a Glycolysis-Associated Gene Signature for the Prediction of Overall Survival in Low Grade Glioma

The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification an...

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Published inFrontiers in genetics Vol. 13; p. 843711
Main Authors Liu, Wei, Liu, Chunshan, Chen, Chengcong, Huang, Xiaoting, Yi, Qi, Tian, Yunhong, Peng, Biao, Yuan, Yawei
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 23.03.2022
Subjects
Genetics
glycolysis
low-grade glioma
prognosis
risk model
signature
low-grade glioma
signature
prognosis
risk model
glycolysis
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Summary:The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low-risk patients precisely, and thus can be used as an independent clinical feature.
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Edited by: Li Xing, University of Saskatchewan, Canada
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
These authors have contributed equally to this work
Reviewed by: Longbo Zhang, Yale University, United States
Mingjie Wang, Shanghai Jiao Tong University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2022.843711