Cross-platform method for identifying candidate network biomarkers for prostate cancer.
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.
Jin
G
G
The Methodist Hospital Research Institute, Weill Cornell Medical College, Medical Systems Biology Laboratory, The Center for Bioinformatics and Biotechnology, Houston, USA.
Zhou
X
X
Cui
K
K
Zhang
X-S
XS
Chen
L
L
Wong
S T C
ST
D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism*; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D049490:Systems Biology / Q000379:methods
D014408:Biomarkers, Tumor