Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Arau, Perlis, Malaysia.
Cell and Tissue Engineering Lab (CTEL), Department of Biotechnology Engineering, Kulliyyah of Engineering, International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia.
BMC Cancer. 2018 Apr 2;18(1):362. doi: 10.1186/s12885-018-4235-7.
Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.
The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.
This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.
The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.
人体呼出的挥发性有机化合物(VOC)已被证明是早期肺癌诊断的有用信息来源。迄今为止,关于癌细胞产生和来源的重要 VOC 仍存在争议信息。因此,本研究旨在进行体外实验,涉及相关细胞系,以验证 VOC 提供细胞信息的能力。
采用不同统计方法的电子鼻技术对最佳分类器进行了性能评估和讨论。使用固相微萃取-气相色谱-质谱法对气体传感器进行了补充研究。为此,在生长培养基中培养肺癌细胞(A549 和 Calu-3)和对照细胞系、乳腺癌细胞(MCF7)和非癌细胞系(WI38VA13)。
本研究成功提供了一组可能的挥发性有机化合物,这些化合物可能是肺癌的特异性生物标志物,甚至在细胞生长的第 24 小时也能检测到。此外,基于线性判别分析的一对一支持向量机分类器能够在区分肺癌细胞与乳腺癌细胞和正常肺细胞方面产生高性能。
本工作的结果表明,癌细胞释放的特定 VOC 可以作为气味特征,并有可能用于使用气体阵列传感器设备对肺癌进行非侵入性筛查。