title: An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Fu-neng
authors.initials: FN
authors.lastname: Jiang
authors.forename: Hui-chan
authors.initials: HC
authors.lastname: He
authors.forename: Yan-qiong
authors.initials: YQ
authors.lastname: Zhang
authors.forename: Deng-Liang
authors.initials: DL
authors.lastname: Yang
authors.forename: Jie-Hong
authors.initials: JH
authors.lastname: Huang
authors.forename: Yun-xin
authors.initials: YX
authors.lastname: Zhu
authors.forename: Ru-jun
authors.initials: RJ
authors.lastname: Mo
authors.forename: Guo
authors.initials: G
authors.lastname: Chen
authors.forename: Sheng-bang
authors.initials: SB
authors.lastname: Yang
authors.forename: Yan-ru
authors.initials: YR
authors.lastname: Chen
authors.forename: Wei-de
authors.initials: WD
authors.lastname: Zhong
authors.forename: Wen-Liang
authors.initials: WL
authors.lastname: Zhou
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism; D056284:Histone Deacetylase 1 / Q000378:metabolism; D006801:Humans; D008297:Male; D008875:Middle Aged; D000072670:PTB-Associated Splicing Factor; D051059:PTEN Phosphohydrolase / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D060066:Protein Interaction Maps; D040901:Proteomics; D016601:RNA-Binding Proteins / Q000378:metabolism; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor; D000072670:PTB-Associated Splicing Factor; D016601:RNA-Binding Proteins; D051059:PTEN Phosphohydrolase; C110550:HDAC1 protein, human; D056284:Histone Deacetylase 1