Amino acid metabolism‐related gene expression‐based risk signature can better predict overall survival for glioma. Issue 1 (17th December 2018)

• Record Type: Journal Article
• Authors: Liu, Yu‐Qing; Chai, Rui‐Chao; Wang, Yong‐Zhi; Wang, Zheng; Liu, Xing; Wu, Fan; Jiang, Tao
• Is Part Of: Cancer science. Volume 110:Issue 1(2019)
• Page Start: 321
• Page End: 333
• Publication Date: 2018-12-17
• Subjects: amino acid metabolism -- CGGA -- glioma -- prognosis -- risk signature; Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.13878
• Languages: English
• ISSNs: 1347-9032

Abstract

Abstract : Metabolic reprogramming has been proposed to be a hallmark of cancer. Aside from the glycolytic pathway, the metabolic changes of cancer cells primarily involve amino acid metabolism. However, in glioma, the characteristics of the amino acid metabolism‐related gene set have not been systematically profiled. In the present study, RNA sequencing expression data from 309 patients in the Chinese Glioma Genome Atlas database were included as a training set, while another 550 patients within The Cancer Genome Atlas database were used to validate. Consensus clustering of the 309 samples yielded two robust groups. Compared with Cluster1, Cluster2 correlated with a better clinical outcome. We then developed an amino acid metabolism‐related risk signature for glioma. Our results showed that patients in the high‐risk group had dramatically shorter overall survival than low‐risk counterparts in any subgroup, stratified by isocitrate dehydrogenase and 1p/19q status based on the 2016 World Health Organization classification guidelines. The 30‐gene signature showed better prognostic value than the traditional factors "age" and "grade" by analyzing the receiver operating characteristic curve with areas under curve of 0.966, 0.692, 0.898 and 0.975, 0.677, 0.885 for 3‐ and 5‐year survival, respectively. Moreover, univariate and multivariate analysis showed that the 30‐gene signature was an independent prognostic factor for glioma. Furthermore, Gene Ontology analysis and Gene Set Enrichment Analysis showed that tumors with a high risk score correlated with various aspects of the malignancy of glioma. In summary, we demonstrated a novel amino acid metabolism‐related risk signature for predicting prognosis for glioma. Abstract : In the present study, for the first time, we identified an amino acid metabolism‐related risk signature analyzed by bioinformatics for glioma patients. Further, the 30‐gene signature could not only strikingly stratify the clinical and molecular characteristics of glioma, but showed superior evaluation of prognosis than did the traditional factors. Of note, we identified that the amino acid metabolism‐related risk signature remained an independent prognostic factor after adjustment for clinical and molecular features.