A novel serum metabolomics-based diagnostic approach for colorectal cancer

Shin Nishiumi, Takashi Kobayashi, Atsuki Ikeda, Tomoo Yoshie, Megumi Kibi, Yoshihiro Izumi, Tatsuya Okuno, Nobuhide Hayashi, Seiji Kawano, Tadaomi Takenawa, Takeshi Azuma, Masaru Yoshida

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157 Citations (Scopus)

Abstract

Background: To improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer. Methodology/Principal Findings: We performed serum metabolome analysis using gas-chromatography/mass-spectrometry (GC/MS). First, the accuracy of our GC/MS-based serum metabolomic analytical method was evaluated by calculating the RSD% values of serum levels of various metabolites. Second, the intra-day (morning, daytime, and night) and inter-day (among 3 days) variances of serum metabolite levels were examined. Then, serum metabolite levels were compared between colorectal cancer patients (N = 60; N = 12 for each stage from 0 to 4) and age- and sex-matched healthy volunteers (N = 60) as a training set. The metabolites whose levels displayed significant changes were subjected to multiple logistic regression analysis using the stepwise variable selection method, and a colorectal cancer prediction model was established. The prediction model was composed of 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine, and its AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, according to the training set data. In contrast, the sensitivity, specificity, and accuracy of CEA were 35.0%, 96.7%, and 65.8%, respectively, and those of CA19-9 were 16.7%, 100%, and 58.3%, respectively. The validity of the prediction model was confirmed using colorectal cancer patients (N = 59) and healthy volunteers (N = 63) as a validation set. At the validation set, the sensitivity, specificity, and accuracy of the prediction model were 83.1%, 81.0%, and 82.0%, respectively, and these values were almost the same as those obtained with the training set. In addition, the model displayed high sensitivity for detecting stage 0-2 colorectal cancer (82.8%). Conclusions/Significance: Our prediction model established via GC/MS-based serum metabolomic analysis is valuable for early detection of colorectal cancer and has the potential to become a novel screening test for colorectal cancer.

Original languageEnglish
Article numbere40459
JournalPloS one
Volume7
Issue number7
DOIs
Publication statusPublished - Jul 11 2012

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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    Nishiumi, S., Kobayashi, T., Ikeda, A., Yoshie, T., Kibi, M., Izumi, Y., Okuno, T., Hayashi, N., Kawano, S., Takenawa, T., Azuma, T., & Yoshida, M. (2012). A novel serum metabolomics-based diagnostic approach for colorectal cancer. PloS one, 7(7), [e40459]. https://doi.org/10.1371/journal.pone.0040459