Metabonomics identifies serum metabolite markers of colorectal cancer

Binbin Tan, Yunping Qiu, Xia Zou, Tianlu Chen, Guoxiang Xie, Yu Cheng, Taotao Dong, Linjing Zhao, Bo Feng, Xiaofang Hu, Lisa X. Xu, Aihua Zhao, Menghui Zhang, Guoxiang Cai, Sanjun Cai, Zhanxiang Zhou, Minhua Zheng, Yan Zhang, Wei Jia

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.

Original languageEnglish (US)
Pages (from-to)3000-3009
Number of pages10
JournalJournal of Proteome Research
Volume12
Issue number6
DOIs
StatePublished - 2013
Externally publishedYes

Fingerprint

Metabolomics
Metabolites
Colorectal Neoplasms
Biomarkers
Discriminant analysis
Mass spectrometry
Statistical tests
Liquid chromatography
Discriminant Analysis
Least-Squares Analysis
Glutamine
Metabolism
Gas chromatography
Urea
Mass Spectrometry
Fatty Acids
Serum
Citric Acid Cycle
Nonparametric Statistics
Liquid Chromatography

Keywords

  • colorectal cancer
  • diagnostic markers
  • metabolomics/metabonomics
  • serum metabolites

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

Metabonomics identifies serum metabolite markers of colorectal cancer. / Tan, Binbin; Qiu, Yunping; Zou, Xia; Chen, Tianlu; Xie, Guoxiang; Cheng, Yu; Dong, Taotao; Zhao, Linjing; Feng, Bo; Hu, Xiaofang; Xu, Lisa X.; Zhao, Aihua; Zhang, Menghui; Cai, Guoxiang; Cai, Sanjun; Zhou, Zhanxiang; Zheng, Minhua; Zhang, Yan; Jia, Wei.

In: Journal of Proteome Research, Vol. 12, No. 6, 2013, p. 3000-3009.

Research output: Contribution to journalArticle

Tan, B, Qiu, Y, Zou, X, Chen, T, Xie, G, Cheng, Y, Dong, T, Zhao, L, Feng, B, Hu, X, Xu, LX, Zhao, A, Zhang, M, Cai, G, Cai, S, Zhou, Z, Zheng, M, Zhang, Y & Jia, W 2013, 'Metabonomics identifies serum metabolite markers of colorectal cancer', Journal of Proteome Research, vol. 12, no. 6, pp. 3000-3009. https://doi.org/10.1021/pr400337b
Tan, Binbin ; Qiu, Yunping ; Zou, Xia ; Chen, Tianlu ; Xie, Guoxiang ; Cheng, Yu ; Dong, Taotao ; Zhao, Linjing ; Feng, Bo ; Hu, Xiaofang ; Xu, Lisa X. ; Zhao, Aihua ; Zhang, Menghui ; Cai, Guoxiang ; Cai, Sanjun ; Zhou, Zhanxiang ; Zheng, Minhua ; Zhang, Yan ; Jia, Wei. / Metabonomics identifies serum metabolite markers of colorectal cancer. In: Journal of Proteome Research. 2013 ; Vol. 12, No. 6. pp. 3000-3009.
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abstract = "Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.",
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AU - Tan, Binbin

AU - Qiu, Yunping

AU - Zou, Xia

AU - Chen, Tianlu

AU - Xie, Guoxiang

AU - Cheng, Yu

AU - Dong, Taotao

AU - Zhao, Linjing

AU - Feng, Bo

AU - Hu, Xiaofang

AU - Xu, Lisa X.

AU - Zhao, Aihua

AU - Zhang, Menghui

AU - Cai, Guoxiang

AU - Cai, Sanjun

AU - Zhou, Zhanxiang

AU - Zheng, Minhua

AU - Zhang, Yan

AU - Jia, Wei

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N2 - Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.

AB - Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.

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