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作 者:徐建军[1] 陈立如[1] 魏益平[1] 朱腾高[2] 陈焕文[2] 欧阳永中[2] 温华[1] 赵宗盛[1]
机构地区:[1]南昌大学第二附属医院心胸外科,南昌330006 [2]东华理工大学江西省质谱科学与仪器重点实验室,南昌330013
出 处:《广东医学》2014年第8期1179-1182,共4页Guangdong Medical Journal
基 金:国家重大科学仪器设备开发专项基金资助项目(编号:2011YQ170067);国家自然科学基金资助项目(编号:81160293)
摘 要:目的研究常压直接质谱分析技术(AMS)快速鉴别肺癌与癌旁组织的效果及应用。方法在不对肺组织进行任何预处理前提下,采用自制的甲醇萃取辅助的针尖电喷雾离子源对15例肺癌患者的肺癌和癌旁组织样品进行快速质谱分析,萃取剂甲醇的流速为4μL/min,设置离子源为正离子模式,质荷比(m/z)750-850Da,离子传输管温度150℃,喷雾电压3.5 kV,透镜电压65.0 V。应用matlab软件对谱图数据进行分析,肺癌与癌旁组织质谱峰丰度差异采用独立样本t检验;用主成分分析(PCA)方法对肺癌与癌旁组织样品的质谱指纹谱图进行快速分类和鉴别。结果肺癌和癌旁组织样品指纹谱图在m/z为756.7、772.6、780.7、808.7、824.7、832.7等处的相对丰度在肺癌和癌旁组织中差异有统计学意义(P〈0.01);PCA分类图中,肺癌组织和癌旁组织样品分别落在不同的区域,两类样品能有效区分。结论 AMS耦合PCA能够有效地将肺癌与癌旁组织样品的质谱指纹区分,从而快速鉴别出肺癌与癌旁组织,并获得相关样品的分子尺度信息,为快速诊断肺癌及肺癌肿瘤标志物研究提供新的思路和策略。Objective To investigate the effect and application of ambient mass spectrometry( AMS) for fast identification of lung cancer. Methods Intraoperatively collected tissue samples and adjacent normal lung tissues from 15 lung cancer patients were analyzed by home- made solvent- assisted tip electrospray ionization mass spectrometry without any pretreatment. Flow rate of solvent( methanol) was 4 μL / min with ion source mode was set as follows: positive ion mode,mass range of m / z 750 - 850 Da,of 150℃ ion transmission tube temperature,3. 5 kV spray voltage,65. 0 V lens voltage as. Data of the spectra was analyzed using matlab software. Student's t test was performed. Principal component analysis( PCA) was used as a data processing method for the fingerprinting spectra. Results Significant difference was observed in fingerprint spectra at 756. 7,772. 6,780. 7,808. 7,824. 7 and 832. 7 m / z between cancerous tissue and normal tissue( P〈0. 01). Pattern recognitions at the fingerprint spectrum level for clustering analysis of tissue samples were achieved successfully with lung cancer separated from normal tissues. Conclusion Coupling with PCA analysis,the cancerous tissue samples of NSCLC can be identified effectively from the adjacent normal tissue by AMS,suggesting potential application in fast diagnosis of lung cancer.
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