Alaiya Ayodele A, Franzén Bo, Hagman Anders, Dysvik Bjarte, Roblick Uwe J, Becker Susanne, Moberger Birgitta, Auer Gert, Linder Stig
Unit of Cancer Proteomics, Department of Oncology and Pathology, Karolinska Institutet and Hospital, Stockholm, Sweden.
Int J Cancer. 2002 Apr 20;98(6):895-9. doi: 10.1002/ijc.10288.
Ovarian tumors range from benign to aggressive malignant tumors, including an intermediate class referred to as borderline carcinoma. The prognosis of the disease is strongly dependent on tumor classification, where patients with borderline tumors have much better prognosis than patients with carcinomas. We here describe the use of hierarchical clustering analysis of quantitative protein expression data for classification of this type of tumor. An accurate classification was not achieved using an unselected set of 1,584 protein spots for clustering analysis. Different approaches were used to select spots that were differentially expressed between tumors of different malignant potential and to use these sets of spots for classification. When sets of proteins were selected that differentiated benign and malignant tumors, borderline tumors clustered in the benign group. This is consistent with the biologic properties of these tumors. Our results indicate that hierarchical clustering analysis is a useful approach for analysis of protein profiles and show that this approach can be used for differential diagnosis of ovarian carcinomas and borderline tumors.
卵巢肿瘤范围从良性到侵袭性恶性肿瘤，包括一类被称为交界性癌的中间类型。该疾病的预后很大程度上取决于肿瘤分类，交界性肿瘤患者的预后比癌患者好得多。我们在此描述使用定量蛋白质表达数据的层次聚类分析对这类肿瘤进行分类。使用未经筛选的1584个蛋白质点集进行聚类分析未实现准确分类。采用了不同方法来选择在不同恶性潜能肿瘤之间差异表达的点，并使用这些点集进行分类。当选择区分良性和恶性肿瘤的蛋白质集时，交界性肿瘤聚集在良性组中。这与这些肿瘤的生物学特性一致。我们的结果表明，层次聚类分析是分析蛋白质谱的有用方法，并表明该方法可用于卵巢癌和交界性肿瘤的鉴别诊断。
