An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis

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 integra...

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Bibliographic Details
Published in:PloS one 2013-05, Vol.8 (5), p.e63941-e63941
Main Authors: Jiang, Fu-neng, He, Hui-chan, Zhang, Yan-qiong, Yang, Deng-Liang, Huang, Jie-Hong, Zhu, Yun-xin, Mo, Ru-jun, Chen, Guo, Yang, Sheng-bang, Chen, Yan-ru, Zhong, Wei-de, Zhou, Wen-Liang
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Androgens
Biology
Biomarkers, Tumor - metabolism
Cancer
Cancer therapies
Classifiers
Diagnosis
Diagnostic systems
Disease
DNA microarrays
Enzyme-linked immunosorbent assay
Fluorescence
Gel electrophoresis
Gene expression
Histone Deacetylase 1 - metabolism
Hospitals
Humans
Immunohistochemistry
Laboratories
Life sciences
Male
Markers
Medical diagnosis
Medical prognosis
Medical research
Medicine
Middle Aged
Multivariate analysis
Patients
Prostate cancer
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - metabolism
Protein Interaction Maps
Proteins
Proteomics
PTB-Associated Splicing Factor
PTEN Phosphohydrolase - metabolism
PTEN protein
Regression analysis
Reproducibility of Results
RNA-Binding Proteins - metabolism
Support vector machines
Survival
Tissues
Urology
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0063941