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Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-3721-8586
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2019 (English)In: Frontiers in Oncology, ISSN 2234-943X, E-ISSN 2234-943X, Vol. 9, article id 681Article in journal (Refereed) Published
Abstract [en]

Colorectal cancer is the third most incidental cancer worldwide, and the response rate of current treatment for colorectal cancer is very low. Genome-scale metabolic models (GEMs) are systems biology platforms, and they had been used to assist researchers in understanding the metabolic alterations in different types of cancer. Here, we reconstructed a generic colorectal cancer GEM by merging 374 personalized GEMs from the Human Pathology Atlas and used it as a platform for systematic investigation of the difference between tumor and normal samples. The reconstructed model revealed the metabolic reprogramming in glutathione as well as the arginine and proline metabolism in response to tumor occurrence. In addition, six genes including ODC1, SMS, SRM, RRM2, SMOX, and SAT1 associated with arginine and proline metabolism were found to be key players in this metabolic alteration. We also investigated these genes in independent colorectal cancer patients and cell lines and found that many of these genes showed elevated level in colorectal cancer and exhibited adverse effect in patients. Therefore, these genes could be promising therapeutic targets for treatment of a specific colon cancer patient group.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019. Vol. 9, article id 681
Keywords [en]
colorectal cancer, genome scale metabolic model, polyamine metabolism, personalized medicine, transcriptomics
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:kth:diva-255737DOI: 10.3389/fonc.2019.00681ISI: 000477876200001Scopus ID: 2-s2.0-85072220274OAI: oai:DiVA.org:kth-255737DiVA, id: diva2:1342729
Note

QC 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-10-04Bibliographically approved

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Zhang, ChengArif, MuhammadMardinoglu, Adil

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