An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis.
An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis.
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用于前列腺癌诊断的基于蛋白质组学和相互作用网络的综合分类器
DOI：
10.1371/journal.pone.0063941
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Zhou WL
中科院分区：
文献类型：
--
作者：
Jiang FN;He HC;Zhang YQ;Yang DL;Huang JH;Zhu YX;Mo RJ;Chen G;Yang SB;Chen YR;Zhong WD;Zhou WL
目的前列腺癌是临床上多灶性疾病，早期诊断对改善患者预后至关重要。然而，已发表的PCa诊断标记几乎没有重叠，并且使用独立数据进行验证的效果很差。因此，我们将蛋白质的差异表达与人类蛋白质相互作用网络的拓扑特征相结合，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，以提高前列腺癌的诊断能力。方法与结果采用二维荧光差凝胶电泳（2D-DIGE）结合质谱法，对前列腺癌及前列腺癌邻近良性组织进行分析，共鉴定出60个在前列腺癌组织中差异表达的蛋白作为候选标记物。然后用GeneGO Meta-Core软件分析它们的网络，选择3个枢纽蛋白（PTEN， SFPQ和HDAC1）。然后，基于上述枢纽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PCa诊断分类器。诊断性能验证表明，该分类器具有较高的预测准确率（85.96 ~ 90.18%）和ROC曲线下面积（接近1.0）。此外，通过ELISA和免疫组化分析，验证PTEN、SFPQ和HDAC1蛋白在PCa中的临床意义。更有趣的是，通过Cox回归的多因素分析，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。结论基于蛋白质组学和相互作用网络的分类器结合了人类蛋白质相互作用网络的差异表达和拓扑特征，可能是诊断前列腺癌的有力工具。我们还发现PTEN蛋白是前列腺癌患者生化无复发生存的一种新的预后标志物。
Aim 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. Methods and Results 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. Conclusions 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.
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影响因子：
6.2
作者：
Antonarakis, Emmanuel S.;Keizman, Daniel;Zhang, Zhe;Gurel, Bora;Lotan, Tamara L.;Hicks, Jessica L.;Fedor, Helen L.;Carducci, Michael A.;De Marzo, Angelo M.;Eisenberger, Mario A.
通讯作者：
Eisenberger, Mario A.
影响因子：
120.7
作者：
Kenfield, Stacey A.;Stampfer, Meir J.;Chan, June M.;Giovannucci, Edward
通讯作者：
Giovannucci, Edward
影响因子：
8
作者：
Noonan, E. J.;Place, R. F.;Dahiya, R.
通讯作者：
Dahiya, R.
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
通过基于 DIGE 的蛋白质组学分析分析与前列腺癌淋巴结转移相关的蛋白质标记
影响因子：
4.4
作者：
Pang, Jun;Liu, Wei-Peng;Gao, Xin
通讯作者：
Gao, Xin