School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
PLoS One. 2013 May 30;8(5):e63941. doi: 10.1371/journal.pone.0063941. Print 2013.
Early diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis.
By two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96∼90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression.
Our data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.
前列腺癌（PCa）是一种临床异质性、多灶性疾病，早期诊断对于改善患者预后至关重要。然而，已发表的 PCa 诊断标志物之间重叠较少，并且使用独立数据进行验证的效果也较差。因此，我们通过结合差异蛋白表达与人类蛋白质相互作用网络的拓扑特征，开发了一种基于蛋白质组学和相互作用网络的综合分类器，以增强 PCa 诊断能力。
通过二维荧光差异凝胶电泳（2D-DIGE）结合 MS 技术，使用 PCa 和前列腺相邻良性组织，共鉴定出 60 种在 PCa 组织中差异表达的候选蛋白标志物。然后，使用 GeneGO Meta-Core 软件对其网络进行分析，选择了三个核心蛋白（PTEN、SFPQ 和 HDAC1）。之后，基于上述核心蛋白编码基因的微阵列基因表达数据，通过支持向量机（SVM）建模构建了 PCa 诊断分类器。验证诊断性能表明，该分类器具有较高的预测准确性（85.96%∼90.18%）和 ROC 曲线下面积（接近 1.0）。此外，通过 ELISA 和免疫组织化学分析进一步验证了 PTEN、SFPQ 和 HDAC1 蛋白在 PCa 中的临床意义。更有趣的是，根据 Cox 回归的多变量分析，PTEN 蛋白被确定为 PCa 患者生化无复发生存的独立预后标志物。
我们的数据表明，结合差异蛋白表达和人类蛋白质相互作用网络拓扑特征的综合蛋白质组学和相互作用网络分类器可能是 PCa 诊断的有力工具。我们还发现 PTEN 蛋白是 PCa 患者生化无复发生存的新型预后标志物。
