Lebrecht Antje, Boehm Daniel, Schmidt Markus, Koelbl Heinz, Schwirz Raphaela L, Grus Franz H
Department of Obstetrics and Gynecology, Johannes Gutenberg University Mainz, D-55101 Mainz, Germany.
Cancer Genomics Proteomics. 2009 May-Jun;6(3):177-82.
Early detection of breast cancer reduces breast cancer-related mortality. Breast cancer biomarkers offer a promising means of detecting this disease at the earliest and most treatable stages.
The aim of this study was to generate a protein biomarker profile in tear fluid for breast cancer patients. This established biomarker profile was then used to discriminate between cancer patients and healthy controls. Potential biomarkers were screened in tear fluid from 50 women with breast cancer and 50 healthy women, matched for age. Tear fluid was drawn prior to surgery. Surface-enhanced laser desorption-ionisation time-of-flight mass spectrometry was used for protein profiling with two different active surfaces on the protein chips: a cationic exchanger (CM-10) and a reverse-phase surface (H50). The data were analyzed by multivariate statistical techniques and artificial neural networks.
A total of 404 peaks were found with different molecular weights at different laser intensities and a statistically significant (p<0.05) panel with 20 biomarkers was generated. Use of the biomarker panel resulted in 71.19% of the samples being correctly classified as cancer samples (42 out of 59) and 70.69% as control samples (41 out of 58), thus overall 70.94% were correctly classified. The diagnostic pattern was able to differentiate cancer patients from healthy women with a specificity and sensitivity of approximately 70% using tear fluid.
In this study a biomarker panel in tear fluid was successfully generated to allow breast cancer patients to be discriminated from healthy women. The study suggests that the proteomic pattern of tear fluid may be useful in the diagnosis of breast cancer and for high-throughput biomarker discovery.
早期发现乳腺癌可降低乳腺癌相关死亡率。乳腺癌生物标志物为在最早且最可治疗阶段检测该疾病提供了一种有前景的方法。
本研究的目的是生成乳腺癌患者泪液中的蛋白质生物标志物谱。然后使用这一既定的生物标志物谱来区分癌症患者和健康对照。在50名年龄匹配的乳腺癌女性和50名健康女性的泪液中筛选潜在生物标志物。泪液在手术前采集。采用表面增强激光解吸电离飞行时间质谱法对蛋白质芯片上的两种不同活性表面进行蛋白质谱分析：阳离子交换剂（CM - 10）和反相表面（H50）。数据通过多变量统计技术和人工神经网络进行分析。
在不同激光强度下共发现404个具有不同分子量的峰，并生成了一个具有20种生物标志物的统计学显著（p<0.05）的生物标志物组。使用该生物标志物组，71.19%的样本被正确分类为癌症样本（59个样本中的42个），70.69%被正确分类为对照样本（58个样本中的41个），总体正确分类率为70.94%。该诊断模式能够以约70%的特异性和敏感性，利用泪液将癌症患者与健康女性区分开来。
在本研究中成功生成了泪液中的生物标志物组，能够区分乳腺癌患者和健康女性。该研究表明，泪液的蛋白质组模式可能有助于乳腺癌的诊断以及高通量生物标志物的发现。
