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哥伦比亚患者呼出气体分析用于胃癌诊断

Exhaled breath analysis for gastric cancer diagnosis in Colombian patients.

作者信息

Durán-Acevedo Cristhian Manuel, Jaimes-Mogollón Aylen Lisset, Gualdrón-Guerrero Oscar Eduardo, Welearegay Tesfalem Geremariam, Martinez-Marín Julián Davíd, Caceres-Tarazona Juan Martín, Sánchez-Acevedo Zayda Constanza, Beleño-Saenz Kelvin de Jesus, Cindemir Umut, Österlund Lars, Ionescu Radu

机构信息

Multisensor System and Pattern Recognition Research Group (GISM), Electronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia.

Department of Electronics, Electrical and Automatic Engineering, Rovira i Virgili University, Tarragona, Spain.

出版信息

Oncotarget. 2018 Jun 22;9(48):28805-28817. doi: 10.18632/oncotarget.25331.

Abstract

We present here the first study that directly correlates gastric cancer (GC) with specific biomarkers in the exhaled breath composition on a South American population, which registers one of the highest global incidence rates of gastric affections. Moreover, we demonstrate a novel solid state sensor that predicts correct GC diagnosis with 97% accuracy. Alveolar breath samples of 30 volunteers (patients diagnosed with gastric cancer and a controls group formed of patients diagnosed with other gastric diseases) were collected and analyzed by gas-chromatography/mass-spectrometry (GC-MS) and with an innovative chemical gas sensor based on gold nanoparticles (AuNP) functionalized with octadecylamine ligands. Our GC-MS analyses identified 6 volatile organic compounds that showed statistically significant differences between the cancer patients and the controls group. These compounds were different from those identified in previous studied performed on other populations with high incidence rates of this malady, such as China (representative for Eastern Asia region) and Latvia (representative for Baltic States), attributable to lifestyle, alimentation and genetics differences. A classification model based on principal component analysis of our sensor data responses to the breath samples yielded 97% accuracy, 100% sensitivity and 93% specificity. Our results suggest a new and non-intrusive methodology for early diagnosis of gastric cancer that may be deployed in regions lacking well-developed health care systems as a prediagnosis test for selecting the patients that should undergo deeper investigations (, endoscopy and biopsy).

摘要

我们在此展示了第一项直接将胃癌(GC)与南美人群呼出气体成分中的特定生物标志物相关联的研究,该地区是全球胃癌发病率最高的地区之一。此外,我们展示了一种新型固态传感器,其对胃癌诊断的预测准确率达97%。收集了30名志愿者(被诊断为胃癌的患者以及由被诊断为其他胃部疾病的患者组成的对照组)的肺泡呼气样本,并通过气相色谱/质谱联用仪(GC-MS)以及一种基于用十八烷基胺配体功能化的金纳米颗粒(AuNP)的创新型化学气体传感器进行分析。我们的GC-MS分析确定了6种挥发性有机化合物,它们在癌症患者和对照组之间显示出统计学上的显著差异。这些化合物与之前在其他该疾病高发病率人群(如中国(东亚地区代表)和拉脱维亚(波罗的海国家代表))中进行的研究中所确定的化合物不同,这归因于生活方式、饮食和基因差异。基于对我们的传感器对呼气样本数据响应进行主成分分析的分类模型,准确率达97%,灵敏度达100%,特异性达93%。我们的结果表明了一种用于胃癌早期诊断的新的非侵入性方法,该方法可部署在医疗保健系统欠发达的地区,作为一种预诊断测试,用于选择应接受更深入检查(如内窥镜检查和活检)的患者。

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