The network biomarker discovery in prostate cancer from both genomics and proteomics levels
Both mass spectrometry (MS) and microarray technologies are very promising for discovery of new biomarkers for clinical diagnosis. In order to identify the high-confidence biomarkers from expression datasets, we proposed a new pipeline for biomarker discovery, in which the disease information of proteins/genes, different levels of expression profiles (microarray datasets and proteomics datasets), and interactions between proteins have been integrated. In our analysis, we first identified 474 molecules (genes and proteins) related to prostate cancer from Ingenuity software and built up a prostate-cancer-related network (PCRN) by searching the interactions among these found proteins. Based on the PCRN, the network biomarkers are discovered from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. Through combining expression profiles of different levels and the protein information, we derived the network biomarkers with protein-protein interactions, which display high-performances in patient classification of prostate cancer.
Network biomarker Mass spectrometry Microarray Prostate cancer
Guangxu Jin Xiaobo Zhou Kemi Cui Stephen T.C. Wong
Center for Bioinformatics and Biotechnology, The Methodist Hospital Research Institute and Comell University, Houston,TX 77030
国际会议
The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)
云南丽江
英文
144-151
2008-10-31（万方平台首次上网日期，不代表论文的发表时间）