title: Protein signatures for classification and prognosis of gastric cancer a signaling pathway-based approach.
abstract: Current methods have limited accuracy in predicting survival and stratifying patients with gastric cancer for appropriate treatment. We sought to identify protein signatures of gastric cancer for classification and prognostication. The Protein Pathway Array (initial study) and Western blot (confirmation) were used to assess the protein expression in a total of 199 fresh frozen gastric samples. There were 56 paired samples divided into a training set (n = 37) and a validation set (n = 19) for the identification of differentially expressed proteins between tumor and normal tissues. There were 56 tumor samples used to identify proteins correlating with tumor and nodal staging. All 93 tumor samples were used to identify candidate proteins for predicting survival. We confirmed the survival prediction of the candidate proteins by using an additional cohort of gastric cancer samples (n = 50). There were 22 proteins differentially expressed between normal and tumor tissues. Nine proteins were selected to build the predictor to classify normal and tumor samples. Ten proteins were differentially expressed among different T stages and four of these were associated with invasive behavior. An additional four proteins were associated with lymph node metastasis. Two proteins were identified as independent risk factors for overall survival. This study indicated that some dysregulated signaling proteins could be selected as useful biomarkers for tumor classification and predicting outcome in gastric cancer patients.
authors.affiliation: Department of General Surgery, The First Hospital, Jilin University, Changchun, China.
authors.forename: Daguang
authors.initials: D
authors.lastname: Wang
authors.forename: Fei
authors.initials: F
authors.lastname: Ye
authors.forename: Yabin
authors.initials: Y
authors.lastname: Sun
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.forename: Hongyi
authors.initials: H
authors.lastname: Liu
authors.forename: Jing
authors.initials: J
authors.lastname: Jiang
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
authors.forename: Chengkui
authors.initials: C
authors.lastname: Liu
authors.forename: Weihua
authors.initials: W
authors.lastname: Tong
authors.forename: Ling
authors.initials: L
authors.lastname: Gao
authors.forename: Yezhou
authors.initials: Y
authors.lastname: Sun
authors.forename: Weijia
authors.initials: W
authors.lastname: Zhang
authors.forename: Terry
authors.initials: T
authors.lastname: Seetoe
authors.forename: Peng
authors.initials: P
authors.lastname: Lee
authors.forename: Jian
authors.initials: J
authors.lastname: Suo
authors.forename: David Y
authors.initials: DY
authors.lastname: Zhang
mesh_terms: D000328:Adult; D001345:Autoradiography; D016000:Cluster Analysis; D003710:Demography; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009363:Neoplasm Proteins / Q000378:metabolism*; D010766:Phosphorylation; D011379:Prognosis; D016016:Proportional Hazards Models; D040901:Proteomics / Q000379:methods*; D012307:Risk Factors; D015398:Signal Transduction; D013274:Stomach Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000378:metabolism* / Q000473:pathology
chemical_list: D009363:Neoplasm Proteins