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

ABSTRACT:

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 marker

SUBMITTER: Jiang FN 

PROVIDER: S-EPMC3667836 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature