Abstract
Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.
| Original language | English (US) |
|---|---|
| Article number | ISBEAT000003000006000505000001 |
| Pages (from-to) | 505-512 |
| Number of pages | 8 |
| Journal | IET Systems Biology |
| Volume | 3 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 2009 |
ASJC Scopus subject areas
- Biotechnology
- Modeling and Simulation
- Molecular Biology
- Genetics
- Cell Biology