J Cancer 2020; 11(2):441-449. doi:10.7150/jca.30923 This issue

Research Paper

Twenty Metabolic Genes Based Signature Predicts Survival of Glioma Patients

Wenfang Xu*, Zhenhao Liu*, He Ren*, Xueqing Peng, Aoshen Wu, Duan Ma, Gang Liu, Lei Liu

Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Institutes of Biomedical Sciences, Fudan University, 200032, Shanghai, P.R.China.
*These authors equally contributed to this work.

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Citation:
Xu W, Liu Z, Ren H, Peng X, Wu A, Ma D, Liu G, Liu L. Twenty Metabolic Genes Based Signature Predicts Survival of Glioma Patients. J Cancer 2020; 11(2):441-449. doi:10.7150/jca.30923. Available from https://www.jcancer.org/v11p0441.htm

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Abstract

Background: Glioma, caused by carcinogenesis of brain and spinal glial cells, is the most common primary malignant brain tumor. To find the important indicator for glioma prognosis is still a challenge and the metabolic alteration of glioma has been frequently reported recently.

Methods: In our current work, a risk score model based on the expression of twenty metabolic genes was developed using the metabolic gene expressions in The Cancer Genome Atlas (TCGA) dataset, the methods of which included the cox multivariate regression and the random forest variable hunting, a kind of machine learning algorithm, and the risk score generated from this model is used to make predictions in the survival of glioma patients in the training dataset. Subsequently, the result was further verified in other three verification sets (GSE4271, GSE4412 and GSE16011). Risk score related pathways collected in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were identified using Gene Set Enrichment Analysis (GSEA).

Results: The risk score generated from our model makes good predictions in the survival of glioma patients in the training dataset and other three verification sets. By assessing the relationships between clinical indicators and the risk score, we found that the risk score was an independent and significant indicator for the prognosis of glioma patients. Simultaneously, we conducted a survival analysis of the patients who received chemotherapy and who did not, finding that the risk score was equally valid in both cases. And signaling pathways related to the genesis and development of multiple cancers were also identified.

Conclusions: In summary, our risk score model is predictive for 967 glioma patients' survival from four independent datasets, and the risk score is a meaningful and independent parameter of the clinicopathological information.

Keywords: Glioma, Marker, Risk Score, Random Forest Variable Hunting, Metabolic Genes