Metabolism-Associated Molecular Classification of Colorectal Cancer

• Published in: Frontiers in oncology Vol. 10; p. 602498
• Main Authors: Zhang, Meng, Wang, Hai-zhou, Peng, Ru-yi, Xu, Fei, Wang, Fan, Zhao, Qiu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 04.12.2020
• Subjects: classification; colorectal cancer; immune signatures; metabolism; non-negative matrix factorization; Oncology; metabolism; colorectal cancer; classification; non-negative matrix factorization; immune signatures
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2020.602498

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.