Background: The diverse anticancer measures display varied efficacy in different patients. Thus, appropriate therapy should be chosen for individual patients, and prognostic prediction, based on biomarkers, is a prerequisite for personalized therapy. Objective: In this study, the prognostic model was established based on the genes that were significantly correlated with the survival time for patient death risk evaluation. Method: Univariate Cox proportional hazards regression analysis was utilized for screening the genes significantly correlated with the patients' survival time. Multivariate Cox proportional hazards regression analysis was utilized for establishing the model. Kaplan-Meier and ROC analyses were used for the validation of the prognostic prediction potential of the constructed model. Results: ROC analysis was conducted in the training and validation datasets, and their AUROC values were 0.774 and 0.723, respectively. In comparison to the known prognostic biomarkers, our prognostic biomarker model constituted by the combination of 6 genes displayed superiority in prediction capability. Conclusions: These results indicated that our biomarker model could effectively stratify the risks in gastric adenocarcinoma patients with high prognostic prediction accuracy and sensitivity.
[1]Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China;
[2]Fudan Univ, Dept Radiat Oncol, Shanghai Canc Ctr, Shanghai 200032, Peoples R China;
[3]Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai 200032, Peoples R China;
[4]Fudan Univ Canc Hosp, Shanghai Proton & Heavy Ion Ctr, Dept Radiat Oncol, Shanghai 201321, Peoples R China;
[5]Simon Fraser Univ, Dept Mol Biol & Biochem, Burnaby, BC, Canada;
[6]Nanjing Univ, Sch Life Sci, State Key Lab Pharmaceut Biotechnol, Nanjing 210093, Jiangsu, Peoples R China
(8.0+极速模式)