The Methodist Hospital Research Institute, Weill Cornell Medical College, Medical Systems Biology Laboratory, The Center for Bioinformatics and Biotechnology, Houston, USA.
IET Syst Biol. 2009 Nov;3(6):505-12. doi: 10.1049/iet-syb.2008.0168.
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.
利用质谱（MS）和微阵列表达谱发现生物标志物是分子诊断中很有前途的策略。在这里，作者提出了一种新的生物标志物发现管道，该管道集成了蛋白质和基因的疾病信息、基因组和蛋白质组水平的表达谱以及蛋白质-蛋白质相互作用（PPIs），以发现高可信度的网络生物标志物。使用该管道，共鉴定出 474 种与前列腺癌相关的分子（基因和蛋白质），并从综合信息中得出前列腺癌相关网络（PCRN）。因此，从由八个微阵列数据集和一个蛋白质组学数据集组成的多个表达谱中鉴定出一组候选网络生物标志物。具有 PPI 的网络生物标志物可以准确地区分前列腺癌患者和正常个体，与传统的生物标志物发现方法相比，这可能提供更可靠的生物标志物候选物。
