%0 Journal Article
%T Proteomics research on muscle-invasive bladder transitional cell carcinoma
%A Hai Niu
%A Zhen Dong
%A Gang Jiang
%A Ting Xu
%A Yan Liu
%A Yan Cao
%A Jun Zhao
%A Xin Wang
%J Cancer Cell International
%D 2011
%I BioMed Central
%R 10.1186/1475-2867-11-17
%X A total of 885/890 proteins commonly appeared in 4 paired samples. 295/337 of the 488/493 proteins that specific expressed in tumor/normal cells own gene ontology (GO) cellular component annotation. Compared with the entire list of the international protein index (IPI), there are 42/45 GO terms exhibited as enriched and 9/5 exhibited as depleted, respectively. Several pathways exhibit significantly changes between cancer and normal cells, mainly including spliceosome, endocytosis, oxidative phosphorylation, etc. Finally, descriptive statistics show that the PI Distribution of candidate biomarkers have certain regularity.The present study identified the proteome expression profile of muscle-invasive bladder cancer cells and normal urothelial cells, providing information for subcellular pattern research of cancer and offer candidate proteins for biomarker panel and network-based multi-target therapy.Despite elaborate characterization of the risk factors, muscle-invasive bladder transitional cell carcinoma (BTCC) is still a major epidemiological problem whose incidence continues to rise each year [1]. The standard therapeutic methods of muscle-invasive BTCC are radical cystectomy (RC) followed postoperative care. Though there are much progress in surgical techniques and perioperative chemoradiation, the 5-year disease specific survival after RC remains 50-60% [2]. At present, the detailed mechanism for the carcinogenesis and development of invasive bladder carcinoma remains to be elucidated.Though there were several proteomics research on muscle-invasive BTCC and make certain progress, the achievement of these researches were confined by the limited proteins identified from cancer tissue [3-5]. Nowadays, sophisticated proteomic, computational, and statistical tools offer us increased possibility of assimilating existing data to discover cancer biology and develop effective biomarkers for diagnosis and targeted therapy [6]. Meanwhile, with these technologies, some new c
%U http://www.cancerci.com/content/11/1/17