CaMeRe: A Novel Tool for Inference of Cancer Metabolic Reprogramming.

Type
Article

Authors
Li, Haoyang
Zhou, Juexiao
Sun, Huiyan
Qiu, Zhaowen
Gao, Xin
Xu, Ying

KAUST Department
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group

KAUST Grant Number
FCC/1/1976-18-01
FCC/1/1976-23-01
FCC/1/1976-25-01
FCC/1/1976-26-01
URF/1/3450-01-01
URF/1/3454-01-01

Date
2020-02-25

Submitted Date
2019-12-10

Abstract
Metabolic reprogramming is prevalent in cancer, largely due to its altered chemical environments such as the distinct intracellular concentrations of O2, H2O2 and H+, compared to those in normal tissue cells. The reprogrammed metabolisms are believed to play essential roles in cancer formation and progression. However, it is highly challenging to elucidate how individual normal metabolisms are altered in a cancer-promoting environment; hence for many metabolisms, our knowledge about how they are changed is limited. We present a novel method, CaMeRe (CAncer MEtabolic REprogramming), for identifying metabolic pathways in cancer tissues. Based on the specified starting and ending compounds, along with gene expression data of given cancer tissue samples, CaMeRe identifies metabolic pathways connecting the two compounds via collection of compatible enzymes, which are most consistent with the provided gene-expression data. In addition, cancer-specific knowledge, such as the expression level of bottleneck enzymes in the pathways, is incorporated into the search process, to enable accurate inference of cancer-specific metabolic pathways. We have applied this tool to predict the altered sugar-energy metabolism in cancer, referred to as the Warburg effect, and found the prediction result is highly accurate by checking the appearance and ranking of those key pathways in the results of CaMeRe. Computational evaluation indicates that the tool is fast and capable of handling large metabolic network inference in cancer tissues. Hence, we believe that CaMeRe offers a powerful tool to cancer researchers for their discovery of reprogrammed metabolisms in cancer. The URL of CaMeRe is http://csbl.bmb.uga.edu/CaMeRe/.

Citation
Li, H., Zhou, J., Sun, H., Qiu, Z., Gao, X., & Xu, Y. (2020). CaMeRe: A Novel Tool for Inference of Cancer Metabolic Reprogramming. Frontiers in Oncology, 10. doi:10.3389/fonc.2020.00207

Acknowledgements
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/3450-01-01, URF/1/3454-01-01 and the National Natural Science Foundation of China (No. 61902144).

Publisher
Frontiers Media SA

Journal
Frontiers in oncology

DOI
10.3389/fonc.2020.00207

Additional Links
https://www.frontiersin.org/article/10.3389/fonc.2020.00207/full

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