Ayyildiz Dilara, Gov Esra, Sinha Raghu, Arga Kazim Yalcin
1 Department of Bioengineering, Marmara University , Istanbul, Turkey .
2 Department of Biomedical Sciences and Biotechnology, University of Udine , Udine, Italy .
OMICS. 2017 May;21(5):285-294. doi: 10.1089/omi.2017.0010. Epub 2017 Apr 4.
Ovarian cancer is one of the most common cancers and has a high mortality rate due to insidious symptoms and lack of robust diagnostics. A hitherto understudied concept in cancer pathogenesis may offer new avenues for innovation in ovarian cancer biomarker development. Cancer cells are characterized by an increase in network entropy, and several studies have exploited this concept to identify disease-associated gene and protein modules. We report in this study the changes in protein-protein interactions (PPIs) in ovarian cancer within a differential network (interactome) analysis framework utilizing the entropy concept and gene expression data. A compendium of six transcriptome datasets that included 140 samples from laser microdissected epithelial cells of ovarian cancer patients and 51 samples from healthy population was obtained from Gene Expression Omnibus, and the high confidence human protein interactome (31,465 interactions among 10,681 proteins) was used. The uncertainties of the up- or downregulation of PPIs in ovarian cancer were estimated through an entropy formulation utilizing combined expression levels of genes, and the interacting protein pairs with minimum uncertainty were identified. We identified 105 proteins with differential PPI patterns scattered in 11 modules, each indicating significantly affected biological pathways in ovarian cancer such as DNA repair, cell proliferation-related mechanisms, nucleoplasmic translocation of estrogen receptor, extracellular matrix degradation, and inflammation response. In conclusion, we suggest several PPIs as biomarker candidates for ovarian cancer and discuss their future biological implications as potential molecular targets for pharmaceutical development as well. In addition, network entropy analysis is a concept that deserves greater research attention for diagnostic innovation in oncology and tumor pathogenesis.
卵巢癌是最常见的癌症之一，由于症状隐匿且缺乏可靠的诊断方法，其死亡率很高。癌症发病机制中一个迄今未被充分研究的概念可能为卵巢癌生物标志物开发的创新提供新途径。癌细胞的特征是网络熵增加，多项研究已利用这一概念来识别与疾病相关的基因和蛋白质模块。在本研究中，我们在一个差异网络（相互作用组）分析框架内，利用熵概念和基因表达数据，报告了卵巢癌中蛋白质 - 蛋白质相互作用（PPI）的变化。从基因表达综合数据库（Gene Expression Omnibus）获得了一个包含六个转录组数据集的汇编，其中包括来自卵巢癌患者激光显微切割上皮细胞的140个样本和来自健康人群的51个样本，并使用了高可信度的人类蛋白质相互作用组（10,681种蛋白质之间的31,465种相互作用）。通过利用基因的组合表达水平的熵公式来估计卵巢癌中PPI上调或下调的不确定性，并识别出不确定性最小的相互作用蛋白质对。我们鉴定出105种具有差异PPI模式的蛋白质，它们分散在11个模块中，每个模块都表明卵巢癌中显著受影响的生物学途径，如DNA修复、细胞增殖相关机制、雌激素受体的核质转运、细胞外基质降解和炎症反应。总之，我们提出了几种PPI作为卵巢癌的生物标志物候选物，并讨论了它们作为药物开发潜在分子靶点的未来生物学意义。此外，网络熵分析是一个值得在肿瘤学诊断创新和肿瘤发病机制方面给予更多研究关注的概念。
