Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Grace Yang; Shaoyang Huang; Kevin Hu; Alex Lu; Jonathan Yang; Noah Meroueh; Pengtao Dang; Yijie Wang; Haiqi Zhu; Sha Cao; Chi Zhang

doi:10.3389/fonc.2023.1117810

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Front Oncol. 2023 Jun 12:13:1117810. doi: 10.3389/fonc.2023.1117810. eCollection 2023.

Authors

Grace Yang^{1

2}, Shaoyang Huang^{1

2}, Kevin Hu^{1

2}, Alex Lu^{1

3}, Jonathan Yang^{1

2}, Noah Meroueh^{1

2}, Pengtao Dang^{1

4}, Yijie Wang⁵, Haiqi Zhu^{1

5}, Sha Cao^{1

6}, Chi Zhang^{1

7}

Affiliations

¹ Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States.
² Carmel High School, Carmel, IN, United States.
³ Park Tudor School, Indianapolis, IN, United States.
⁴ Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, United States.
⁵ Department of Computer Science, Indiana University, Bloomington, IN, United States.
⁶ Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, United States.
⁷ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States.

Abstract

Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue.

Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data.

Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression.

Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

Keywords: TCA cycle; cancer metabolism; flux estimation; glutaminolysis; systems biology.

Grants and funding

This work was supported by NSF DBI IIBR 2047631, NSF IIS 2145314, American Cancer Society RSG-22-062-01-MM, and NCI 5P30CA082709-22.