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1. A novel serum metabolomics-based diagnostic approach for colorectal cancer.

Author

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

Subjects
Medicine, Science
Abstract

To improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer.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%).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.

Published
2012
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2. A Novel Serum Metabolomics-Based Diagnostic Approach for Colorectal Cancer

Author

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

Subjects
Male, Multivariate analysis, Colorectal cancer, Epidemiology, Metabolite, Malates, lcsh:Medicine, Hydroxybutyrates, Bioinformatics, Gastroenterology, Biochemistry, chemistry.chemical_compound, Gastrointestinal Cancers, Pathology, Medicine, Stage (cooking), lcsh:Science, Early Detection of Cancer, Kynurenine, Aged, 80 and over, Multidisciplinary, Colon Adenocarcinoma, Middle Aged, Reference Standards, Oncology, Area Under Curve, Female, Colorectal Neoplasms, Cancer Epidemiology, Cancer Screening, Research Article, Adult, medicine.medical_specialty, Colon, Cystamine, Gastroenterology and Hepatology, Gas Chromatography-Mass Spectrometry, Statistics, Nonparametric, Metabolomics, Text mining, Diagnostic Medicine, Internal medicine, Gastrointestinal Tumors, Metabolome, Cancer Detection and Diagnosis, Early Detection, Biomarkers, Tumor, Humans, Biology, Aged, Aspartic Acid, business.industry, lcsh:R, Case-control study, Cancers and Neoplasms, medicine.disease, Biomarker Epidemiology, Metabolism, Logistic Models, chemistry, ROC Curve, Case-Control Studies, Multivariate Analysis, lcsh:Q, business, Biomarkers, General Pathology
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.

Published
2012

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