Department of Respiratory Medicine, Second Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China.
Chin Med J (Engl). 2010 Nov;123(22):3309-13.
In recent years the proportion of lung adenocarcinoma (adCA) which occurs in lung cancer patients has increased. Using laser capture microdissection (LCM) combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA.
We used LCM and magnetic bead based weak cation exchange (MB-WCX) to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-OF-MS). We screened for the best pattern using a radial basic function neural network algorithm.
About 2.895 × 10(6) and 1.584 × 10(6) cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration (M/Z) between 800 Da and 10 000 Da. According to t test, the expression of two protein peaks at 7521.5 M/Z and 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%.
Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.
近年来，肺癌患者中肺腺癌（adCA）的比例有所增加。本研究采用激光捕获显微切割（LCM）联合液质联用技术，通过对 adCA 组织中蛋白质的研究，筛选肺癌生物标志物。
采用 LCM 和基于磁珠的弱阳离子交换（MB-WCX）技术，从 6 例新鲜 adCA 及其配对正常肺组织中分离和纯化同质的 adCA 细胞和正常细胞。采用基质辅助激光解吸电离飞行时间质谱（MALDI-OF-MS）对蛋白质进行分析和鉴定。采用径向基函数神经网络算法筛选最佳模式。
通过 LCM 从 6 例新鲜 adCA 及其配对正常肺组织中分别获得约 2.895×10(6)和 1.584×10(6)个细胞，显微镜下观察细胞群体纯度均大于 95%。在质量电荷比（M/Z）为 800 Da 到 10000 Da 之间，分别比较 adCA 与配对正常肺组织之间差异表达的蛋白质，发现分别有 221 和 239 个蛋白峰。根据 t 检验，在组织中，M/Z 为 7521.5 和 5079.3 的两个蛋白峰表达差异最大，在 adCA 中表达较弱。通过样本分布图谱，这两个蛋白峰可以准确地区分 adCA 和配对正常肺组织。通过径向基函数神经网络算法建立了一个由 3358.1 M/Z、5079.3 M/Z 和 7521.5 M/Z 三个蛋白组成的可区分 adCA 和配对正常肺组织的判别模式，具有 100%的灵敏度和 100%的特异性。
采用 LCM 联合液质联用技术筛选肺 adCA 差异蛋白，建立了生物标志物模型。该技术有望成为筛选和早期诊断肺 adCA 的有力工具。
