Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
School of Bioscience, Systems Biology Research Centre, University of Skövde, Skövde, Sweden.
J Proteomics. 2019 Mar 30;196:57-68. doi: 10.1016/j.jprot.2019.01.017. Epub 2019 Jan 30.
Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT-LC-MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p < .05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. SIGNIFICANCE: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.
用于早期卵巢肿瘤检测的生物标志物仍亟待开发。卵巢肿瘤在囊内生长，大多数为良性。手术取样是确保准确诊断的唯一方法，但往往会导致发病率和女性激素丧失。本研究使用 TMT-LC-MS/MS 对明确分期为 I 期、1 型（低级别浆液性、黏液性、子宫内膜样；n=9）、2 型（高级别浆液性；n=9）和良性浆液性（n=9）的卵巢肿瘤进行了深入的蛋白质组学研究。数据可通过 ProteomeXchange 以标识符 PXD010939 获得。我们在发现阶段评估了新的生物信息学工具。这种创新的选择过程涉及不同的归一化方法、单变量统计的组合以及逻辑模型树和朴素贝叶斯树分类器。我们通过这种组合方法鉴定了 142 种蛋白质。在囊液和血清中验证了一个生物标志物面板和九个单独的蛋白质：转醛醇酶-1、果糖-1,6-二磷酸醛缩酶 A（ALDOA）、转酮醇酶、铜蓝蛋白、间皮素、聚集素、腱糖蛋白-XB、层粘连蛋白亚基 γ-1 和粘蛋白-16。在囊液中发现了 6 种蛋白具有显著性（p<0.05），而在血清中仅发现 ALDOA 具有显著性。该生物标志物面板的 ROC AUC 分别为 0.96 和 0.57。我们得出结论，分类算法通过选择可能被标准单变量检验遗漏的组合来补充传统的统计方法。意义：在发现阶段，我们对卵巢肿瘤囊液的明确组织学亚组进行了深入的蛋白质组分析，这些亚组对分期和类型（组织学和分级）进行了高度指定。我们提出了一种原始的方法来选择候选生物标志物，该方法结合了几种归一化策略、单变量统计和机器学习算法。所选蛋白质验证结果加强了我们之前的蛋白质组学和基因组学数据，表明囊液在早期卵巢癌诊断中优于血清。
