Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
PLoS One. 2012;7(7):e40459. doi: 10.1371/journal.pone.0040459. Epub 2012 Jul 11.
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.
为了提高结直肠癌患者的生活质量，建立新的筛查方法以早期诊断结直肠癌非常重要。
方法/主要发现：我们使用气相色谱/质谱（GC/MS）进行血清代谢组分析。首先，通过计算各种代谢物血清水平的 RSD% 值来评估我们基于 GC/MS 的血清代谢组学分析方法的准确性。其次，检查了血清代谢物水平的日内（早晨、白天和夜间）和日间（3 天之间）差异。然后，将血清代谢物水平与结直肠癌患者（N=60；每个阶段从 0 到 4 各有 12 人）和年龄及性别匹配的健康志愿者（N=60）进行比较，作为训练集。使用逐步变量选择方法对水平显示出显著变化的代谢物进行多元逻辑回归分析，并建立结直肠癌预测模型。该预测模型由 2-羟基丁酸、天冬氨酸、犬尿氨酸和胱胺组成，根据训练集数据，其 AUC、灵敏度、特异性和准确性分别为 0.9097、85.0%、85.0%和 85.0%。相比之下，CEA 的灵敏度、特异性和准确性分别为 35.0%、96.7%和 65.8%，CA19-9 分别为 16.7%、100%和 58.3%。使用结直肠癌患者（N=59）和健康志愿者（N=63）作为验证集来验证预测模型的有效性。在验证集中，预测模型的灵敏度、特异性和准确性分别为 83.1%、81.0%和 82.0%，这些值与训练集获得的值几乎相同。此外，该模型对检测 0-2 期结直肠癌具有很高的灵敏度（82.8%）。
结论/意义：我们通过 GC/MS 基于血清代谢组分析建立的预测模型对于早期发现结直肠癌具有重要价值，并且有可能成为结直肠癌的新型筛查测试。
