» Articles » PMID: 18799019

Metabolite Profiling of Human Colon Carcinoma--deregulation of TCA Cycle and Amino Acid Turnover

Overview
Journal Mol Cancer
Publisher Biomed Central
Date 2008 Sep 19
PMID 18799019
Citations 135
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Apart from genetic alterations, development and progression of colorectal cancer has been linked to influences from nutritional intake, hyperalimentation, and cellular metabolic changes that may be the basis for new diagnostic and therapeutic approaches. However, in contrast to genomics and proteomics, comprehensive metabolomic investigations of alterations in malignant tumors have rarely been conducted.

Results: In this study we investigated a set of paired samples of normal colon tissue and colorectal cancer tissue with gas-chromatography time-of-flight mass-spectrometry, which resulted in robust detection of a total of 206 metabolites. Metabolic phenotypes of colon cancer and normal tissues were different at a Bonferroni corrected significance level of p=0.00170 and p=0.00005 for the first two components of an unsupervised PCA analysis. Subsequent supervised analysis found 82 metabolites to be significantly different at p<0.01. Metabolites were connected to abnormalities in metabolic pathways by a new approach that calculates the distance of each pair of metabolites in the KEGG database interaction lattice. Intermediates of the TCA cycle and lipids were found down-regulated in cancer, whereas urea cycle metabolites, purines, pyrimidines and amino acids were generally found at higher levels compared to normal colon mucosa.

Conclusion: This study demonstrates that metabolic profiling facilitates biochemical phenotyping of normal and neoplastic colon tissue at high significance levels and points to GC-TOF-based metabolomics as a new method for molecular pathology investigations.

Citing Articles

Metabolic Heterogeneity of Tumor Cells and its Impact on Colon Cancer Metastasis: Insights from Single-Cell and Bulk Transcriptome Analyses.

Jia Y, Feng G, Chen S, Li W, Jia Z, Wang J J Cancer. 2024; 15(13):4175-4196.

PMID: 38947396 PMC: 11212087. DOI: 10.7150/jca.94630.


Black raspberry-mediated metabolic changes in patients with familial adenomatous polyposis associated with rectal polyp regression.

Huang Y, Chen H, Niu B, Wu W, Gao H, Yu J Food Front. 2024; 5(2):259-266.

PMID: 38779578 PMC: 11107796. DOI: 10.1002/fft2.323.


A metabolomics study on carcinogenesis of ground-glass nodules.

Zhang X, Tong X, Chen Y, Chen J, Li Y, Ding C Cytojournal. 2024; 21:12.

PMID: 38628288 PMC: 11021118. DOI: 10.25259/Cytojournal_68_2023.


Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer.

Jayakrishnan T, Mariam A, Farha N, Rotroff D, Aucejo F, Barot S Sci Rep. 2024; 14(1):4294.

PMID: 38383634 PMC: 10881959. DOI: 10.1038/s41598-024-54560-5.


Alterations in the gut microbiota and their metabolites in human intestinal epithelial cells of patients with colorectal cancer.

Jahani-Sherafat S, Azimirad M, Raeisi H, Azizmohammad Looha M, Tavakkoli S, Ahmadi Amoli H Mol Biol Rep. 2024; 51(1):265.

PMID: 38302841 DOI: 10.1007/s11033-024-09273-3.


References
1.
Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey R, Willmitzer L . Metabolite profiling for plant functional genomics. Nat Biotechnol. 2000; 18(11):1157-61. DOI: 10.1038/81137. View

2.
Usadel B, Nagel A, Thimm O, Redestig H, Blaesing O, Palacios-Rojas N . Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol. 2005; 138(3):1195-204. PMC: 1176394. DOI: 10.1104/pp.105.060459. View

3.
Bonnet S, Archer S, Allalunis-Turner J, Haromy A, Beaulieu C, Thompson R . A mitochondria-K+ channel axis is suppressed in cancer and its normalization promotes apoptosis and inhibits cancer growth. Cancer Cell. 2007; 11(1):37-51. DOI: 10.1016/j.ccr.2006.10.020. View

4.
Wessels L, Reinders M, Hart A, Veenman C, Dai H, He Y . A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics. 2005; 21(19):3755-62. DOI: 10.1093/bioinformatics/bti429. View

5.
Pan J, Mak T . Metabolic targeting as an anticancer strategy: dawn of a new era?. Sci STKE. 2007; 2007(381):pe14. DOI: 10.1126/stke.3812007pe14. View