Abstract
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.
Keywords: Gastric adenocarcinoma, prognostic biomarkers, prognostic model, risk evaluation, anticancer.
Combinatorial Chemistry & High Throughput Screening
Title:A Six-Gene Signature Predicts Clinical Outcome of Gastric Adenocarcinoma
Volume: 21 Issue: 6
Author(s): YaQi Li, Qi Yu, Rui Zhu, Yi Wang, Jiarui Li, Qiang Wang, Wenna Guo, Shen Fu*Liucun Zhu*
Affiliation:
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032,China
- School of Life Sciences, Shanghai University, Shanghai 200444,China
Keywords: Gastric adenocarcinoma, prognostic biomarkers, prognostic model, risk evaluation, anticancer.
Abstract: 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.
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Cite this article as:
Li YaQi , Yu Qi , Zhu Rui , Wang Yi, Li Jiarui , Wang Qiang , Guo Wenna , Fu Shen *, Zhu Liucun *, A Six-Gene Signature Predicts Clinical Outcome of Gastric Adenocarcinoma, Combinatorial Chemistry & High Throughput Screening 2018; 21 (6) . https://dx.doi.org/10.2174/1871524918666180531085713
DOI https://dx.doi.org/10.2174/1871524918666180531085713 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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