A novel serum metabolomics-based diagnostic approach for colorectal cancer.
A novel serum metabolomics-based diagnostic approach for colorectal cancer.
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DOI：
10.1371/journal.pone.0040459
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Yoshida M
中科院分区：
文献类型：
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作者：
Nishiumi S;Kobayashi T;Ikeda A;Yoshie T;Kibi M;Izumi Y;Okuno T;Hayashi N;Kawano S;Takenawa T;Azuma T;Yoshida M
为提高结直肠癌患者的生活质量，建立新的结直肠癌早期诊断筛查方法具有重要意义。我们使用气相色谱/质谱法（GC/MS）进行血清代谢组学分析。首先，通过计算各种代谢物血清水平的RSD%值，评价了基于GC/MS的血清代谢组学分析方法的准确度。其次，检查血清代谢物水平的日内（早晨、白天和夜间）和日间（3天内）方差。然后，将结肠直肠癌患者（N = 60;对于0至4的每个阶段，N=12）与作为训练集的年龄和性别匹配的健康志愿者（N=60）之间的血清代谢物水平进行比较。 采用逐步变量选择法对代谢物水平发生显著变化的代谢物进行多元Logistic回归分析，建立结直肠癌预测模型。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%。CEA的敏感性、特异性和准确性分别为35.0%、96.7%和65.8%; CA 19 -9的敏感性、特异性和准确性分别为16.7%、100%和58.3%。使用结直肠癌患者（N = 59）和健康志愿者（N = 63）作为验证集来确认预测模型的有效性。 在验证集上，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，该模型显示出检测0-2期结直肠癌的高灵敏度（82.8%）。我们建立的基于GC/MS的血清代谢组学分析的预测模型对结直肠癌的早期检测有价值，并有可能成为结直肠癌的一种新的筛查方法。
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.