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Metabolism-Associated Molecular Classification of Colorectal Cancer

The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system ba...

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Autores principales: Zhang, Meng, Wang, Hai-zhou, Peng, Ru-yi, Xu, Fei, Wang, Fan, Zhao, Qiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746835/
https://www.ncbi.nlm.nih.gov/pubmed/33344254
http://dx.doi.org/10.3389/fonc.2020.602498
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author Zhang, Meng
Wang, Hai-zhou
Peng, Ru-yi
Xu, Fei
Wang, Fan
Zhao, Qiu
author_facet Zhang, Meng
Wang, Hai-zhou
Peng, Ru-yi
Xu, Fei
Wang, Fan
Zhao, Qiu
author_sort Zhang, Meng
collection PubMed
description The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for “multi-molecular” based personalized therapies.
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spelling pubmed-77468352020-12-19 Metabolism-Associated Molecular Classification of Colorectal Cancer Zhang, Meng Wang, Hai-zhou Peng, Ru-yi Xu, Fei Wang, Fan Zhao, Qiu Front Oncol Oncology The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for “multi-molecular” based personalized therapies. Frontiers Media S.A. 2020-12-04 /pmc/articles/PMC7746835/ /pubmed/33344254 http://dx.doi.org/10.3389/fonc.2020.602498 Text en Copyright © 2020 Zhang, Wang, Peng, Xu, Wang and Zhao http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhang, Meng
Wang, Hai-zhou
Peng, Ru-yi
Xu, Fei
Wang, Fan
Zhao, Qiu
Metabolism-Associated Molecular Classification of Colorectal Cancer
title Metabolism-Associated Molecular Classification of Colorectal Cancer
title_full Metabolism-Associated Molecular Classification of Colorectal Cancer
title_fullStr Metabolism-Associated Molecular Classification of Colorectal Cancer
title_full_unstemmed Metabolism-Associated Molecular Classification of Colorectal Cancer
title_short Metabolism-Associated Molecular Classification of Colorectal Cancer
title_sort metabolism-associated molecular classification of colorectal cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746835/
https://www.ncbi.nlm.nih.gov/pubmed/33344254
http://dx.doi.org/10.3389/fonc.2020.602498
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