A novel serum metabolomics-based diagnostic approach for colorectal cancer

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.

Figure 1. Comparison of serum metabolite levels between colorectal cancer patients and healthy volunteers.

The mean fold changes in the levels of 107 metabolites observed in a comparison between colorectal cancer patients (N = 60) and healthy volunteers (N = 60) (training set) are shown in Figure 1. In GC/MS analysis, multiple peaks are sometimes detected for a particular metabolite due to TMS-derivatization, isomeric form, etc. In such cases, the peak that most reflected the level of the metabolite was adopted for the subsequent evaluation. In addition, each metabolite had the term ‘_1’, ‘_2’, or ‘(-TMS)’ added to the end of its name, as described in a previous report . This figure does not include background metabolites or minor peak-derived metabolites.

Figure 2. ROC curve of the established prediction model.

The solid curve is the ROC curve for the prediction model established on the basis of the training set. The AUC and cut-off values were 0.9097 {95% confidence interval (95% CI): 0.8438–0.9495} and 0.4945, respectively. The sensitivity, specificity, and accuracy of this model are summarized in Table 5.