利用基于气相色谱-串联质谱的靶向代谢组学鉴定可辅助胰腺癌早期检测的高灵敏度生物标志物。

Identification of highly sensitive biomarkers that can aid the early detection of pancreatic cancer using GC/MS/MS-based targeted metabolomics.

作者信息

Hirata Yuichi, Kobayashi Takashi, Nishiumi Shin, Yamanaka Kodai, Nakagawa Takashi, Fujigaki Seiji, Iemoto Takao, Kobayashi Makoto, Okusaka Takuji, Nakamori Shoji, Shimahara Masashi, Ueno Takaaki, Tsuchida Akihiko, Sata Naohiro, Ioka Tatsuya, Yasunami Yohichi, Kosuge Tomoo, Kaneda Takashi, Kato Takao, Yagihara Kazuhiro, Fujita Shigeyuki, Yamada Tesshi, Honda Kazufumi, Azuma Takeshi, Yoshida Masaru

机构信息

Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan.

Division of Chemotherapy and Clinical Research, National Cancer Center Research Institute, Tokyo, Japan.

出版信息

Clin Chim Acta. 2017 May;468:98-104. doi: 10.1016/j.cca.2017.02.011. Epub 2017 Feb 16.

DOI:10.1016/j.cca.2017.02.011

PMID:28215548

Abstract

BACKGROUND

To improve prognosis of pancreatic cancer (PC) patients, the discovery of more reliable biomarkers for the early detection is desired.

METHODS

Blood samples were collected by 2 independent groups. The 1st set was included 55 early PC and 58 healthy volunteers (HV), and the 2nd set was included 16 PC and 16HV. The 16 targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry together with their corresponding stable isotopes. In the 1st set, the levels of these metabolites were evaluated, and diagnostic models were constructed via multivariate logistic regression analysis, leading to validation using the 2nd set.

RESULTS

In the 1st set, model X consisting of 4 candidates based on our previous report possessed higher sensitivity (74.1%) than carbohydrate antigen 19-9 (CA19-9). Model Y, consisting of 2 metabolites newly selected from 16 metabolites via stepwise method possessed higher sensitivity (70.4%) than CA19-9. Furthermore, combining model Y with CA19-9 increased its sensitivity (90.7%) and specificity (89.5%). In the 2nd set, combining model Y with CA19-9 displayed high sensitivity (81.3%) and specificity (93.8%). In particular, it displayed very high sensitivity (100%) for resectable PC.

CONCLUSIONS

Quantitative analysis confirmed that metabolomics-based diagnostic methods are useful for detecting PC early.

摘要

背景

为改善胰腺癌（PC）患者的预后，需要发现更可靠的早期检测生物标志物。

方法

由2个独立小组采集血样。第一组包括55例早期PC患者和58名健康志愿者（HV），第二组包括16例PC患者和16名HV。通过气相色谱/串联质谱联用其相应的稳定同位素对16种靶向代谢物进行定量分析。在第一组中，评估这些代谢物的水平，并通过多变量逻辑回归分析构建诊断模型，然后使用第二组进行验证。

结果

在第一组中，基于我们之前报告的由4种候选物组成的模型X比糖类抗原19-9（CA19-9）具有更高的灵敏度（74.1%）。通过逐步法从16种代谢物中新选出的2种代谢物组成的模型Y比CA19-9具有更高的灵敏度（70.4%）。此外，将模型Y与CA19-9结合可提高其灵敏度（90.7%）和特异性（89.5%）。在第二组中，将模型Y与CA19-9结合显示出高灵敏度（81.3%）和特异性（93.8%）。特别是，它对可切除的PC显示出非常高的灵敏度（100%）。