Faculty of Automation, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland.
Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska-Curie Institute - Oncology Center Gliwice Branch, ul. Wybrzeze Armii Krajowej 15, 44-101, Gliwice, Poland.
J Mol Histol. 2019 Feb;50(1):1-10. doi: 10.1007/s10735-018-9802-3. Epub 2018 Nov 3.
Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections-peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen's effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.
在头颈部癌症领域，鉴定用于癌症分子分类和区分癌性与正常上皮组织的生物标志物仍然是一个至关重要的问题。在此，我们旨在比较蛋白质组和脂质组成分区分口腔癌与正常黏膜的能力。通过 MALDI 质谱成像分析包括鳞状细胞癌和正常上皮组织在内的组织标本。在连续切片中对组织成分的两个分子域进行成像——肽（来自胰蛋白酶处理的蛋白质）和脂质（主要是两性离子磷脂），然后比较对应癌组织和正常上皮组织的感兴趣区域。癌组织区域的异质性高于正常上皮组织的异质性，肽成分的分布比脂质成分的分布更为异质。此外，与正常上皮组织相比，有更多的肽成分显示出癌组织和正常上皮组织之间丰度存在显著差异（肽和脂质成分的 Cohen 效应中位数分别为 0.49 和 0.31）。使用来自 3 位患者的组织标本测试了多成分癌症分类器（与正常上皮组织相比），然后使用第 4 位患者的组织标本进行验证。基于肽的特征和基于脂质的特征分别允许以 0.85 和 0.69 的加权准确率进行癌症分类。尽管如此，两种分类器的精度都非常高（分别为 0.98 和 0.94）。我们得出结论，尽管在蛋白质组域中癌性和正常黏膜之间的分子差异高于所分析的脂质组亚域，但脂质组成分的成像也能够区分口腔癌和正常上皮组织。因此，癌症蛋白质组和脂质组都是口腔恶性肿瘤生物标志物的有前途的来源。
