Schwamborn Kristina, Krieg René C, Reska Marcus, Jakse Gerhard, Knuechel Ruth, Wellmann Axel
Institute of Pathology, RWTH Aachen University, 52074 Aachen, Germany.
Int J Mol Med. 2007 Aug;20(2):155-9.
Prostate cancer has become one of the most common malignancies worldwide. Although lacking in specificity its diagnosis is still based partially on the serum-based test for prostate-specific antigen. As its pathogenesis has not yet been deciphered, the ongoing search for new and more reliable biomarkers remains a challenge to stratify disease onset and progression. Matrix-assisted laser desorption/ionization (MALDI)-Imaging is a promising technique to assist in this endeavor. It delivers accurate mass spectrometric information of the sample's proteins and enables the visualization of the spatial distribution of protein expression profiles and correlation of the information with the histomorphological features of the same tissue section. This study describes the analysis of 22 prostate sections (11 with and 11 without prostate cancer) by MALDI-Imaging. Specific protein expression patterns were obtained for normal and cancerous regions within the tissue sections. Applying a 'support vector machine' algorithm to classify the cancerous from the non-cancerous regions, an overall cross-validation, a sensitivity and specificity of 88, 85.21 and 90.74%, respectively, was achieved. Additionally four distinctively overexpressed peaks were identified: 2,753 and 6,704 Da for non-cancerous glands, and 4,964 and 5,002 Da for cancerous glands. The results of this first clinical study utilizing the new technique of MALDI-Imaging underline its vast potential to identify candidates for more reliable prostate cancer tumor markers and to enlighten the pathogenesis of prostate cancer.
前列腺癌已成为全球最常见的恶性肿瘤之一。尽管缺乏特异性,其诊断仍部分基于前列腺特异性抗原的血清检测。由于其发病机制尚未完全阐明,持续寻找新的、更可靠的生物标志物仍然是对疾病发病和进展进行分层的一项挑战。基质辅助激光解吸/电离(MALDI)成像技术是助力这一工作的一项很有前景的技术。它能提供样本蛋白质的精确质谱信息,并能可视化蛋白质表达谱的空间分布,以及该信息与同一组织切片组织形态学特征的相关性。本研究描述了通过MALDI成像技术对22个前列腺切片(11个有前列腺癌,11个无前列腺癌)进行的分析。获得了组织切片中正常区域和癌区域的特定蛋白质表达模式。应用“支持向量机”算法对癌区域和非癌区域进行分类,总体交叉验证的灵敏度和特异性分别为88%、85.21%和90.74%。此外,还鉴定出四个明显过表达的峰:非癌性腺的2753和6704道尔顿,癌性腺的4964和5002道尔顿。这项利用MALDI成像新技术的首次临床研究结果突显了其在识别更可靠的前列腺癌肿瘤标志物候选物以及阐明前列腺癌发病机制方面的巨大潜力。