Pan-cancer characterization of metabolism-related biomarkers identifies potential therapeutic targets

• Published in: Journal of translational medicine Vol. 19; no. 1; pp. 1 - 219
• Main Authors: Bi, Guoshu, Bian, Yunyi, Liang, Jiaqi, Yin, Jiacheng, Li, Runmei, Zhao, Mengnan, Huang, Yiwei, Lu, Tao, Zhan, Cheng, Fan, Hong, Wang, Qun
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 24.05.2021; BioMed Central; BMC
• Subjects: Algorithms; Biological markers; Cancer; Cancer cells; Care and treatment; Cell metabolism; DNA methylation; Gene expression; Genomes; Glycolysis; Health aspects; Hypothesis testing; Identification and classification; Metabolic pathways; Metabolism; Metabolites; MicroRNAs; miRNA; Oxidative phosphorylation; Pan-cancer; Phosphorylation; Physiological aspects; Survival analysis; Therapeutic targets; Tumors; Warburg effect; China
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-021-02889-0

Abstract Background Generally, cancer cells undergo metabolic reprogramming to adapt to energetic and biosynthetic requirements that support their uncontrolled proliferation. However, the mutual relationship between two critical metabolic pathways, glycolysis and oxidative phosphorylation (OXPHOS), remains poorly defined. Methods We developed a “double-score” system to quantify glycolysis and OXPHOS in 9668 patients across 33 tumor types from The Cancer Genome Atlas and classified them into four metabolic subtypes. Multi-omics bioinformatical analyses was conducted to detect metabolism-related molecular features. Results Compared with patients with low glycolysis and high OXPHOS (LGHO), those with high glycolysis and low OXPHOS (HGLO) were consistently associated with worse prognosis. We identified common dysregulated molecular features between different metabolic subgroups across multiple cancers, including gene, miRNA, transcription factor, methylation, and somatic alteration, as well as investigated their mutual interfering relationships. Conclusion Overall, this work provides a comprehensive atlas of metabolic heterogeneity on a pan-cancer scale and identified several potential drivers of metabolic rewiring, suggesting corresponding prognostic and therapeutic utility.