Li YaQi, Yu Qi, Zhu Rui, Wang Yi, Li Jiarui, Wang Qiang, Guo Wenna, Fu Shen, Zhu Liucun
School of Life Sciences, Shanghai University, Shanghai 200444, China.
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Comb Chem High Throughput Screen. 2018;21(6):444-452. doi: 10.2174/1871524918666180531085713.
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.
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.
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.
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.
These results indicated that our biomarker model could effectively stratify the risks in gastric adenocarcinoma patients with high prognostic prediction accuracy and sensitivity.
