An Integrative Proteomics and Interaction Network-Based Classifier for Prostate Cancer Diagnosis

Fu-Neng Jiang,Yan-Qiong Zhang,Hui-Chan He,Yun-Xin Zhu,Jie-Hong Huang,Wen-Liang Zhou,Yan-Ru Chen,Sheng-Bang Yang,Wei-De Zhong,Deng-Liang Yang,Ru-Jun Mo,Guo Chen,Natasha Kyprianou

doi:10.1371/journal.pone.0063941

Abstract

AimEarly 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 ResultsBy 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.ConclusionsOur 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.

Highlights

Prostate cancer (PCa), a clinically heterogeneous-multifocal disease, is the most common malignancy in men and the second leading cause of male cancer-related death [1]
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 identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients

Summary

Introduction

Prostate cancer (PCa), a clinically heterogeneous-multifocal disease, is the most common malignancy in men and the second leading cause of male cancer-related death [1]. The incidence and mortality from this cause in China appear to be rapidly increasing, and the clinical outcome of PCa patients is difficult to predict. The 5-year cancer-specific survival rate is close to 80% in men with localized PCa but is only 34% in men with distant metastasis [3]. In view of the importance of early diagnosis to the application of curative treatments which are the only hope for increasing the life expectancy of PCa patients, there is an urgent need to develop effective systems which can predict the occurrence of this neoplasm

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