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Research Article
Huanbing Liu
First Affiliated Hospital of Nanchang University
Corresponding Author
License:
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Background
Gastric cancer is one of the leading causes of cancer-related death worldwide. How to eliminate gastric cancer is an urgent public health problem. Prediction of prognosis is critical to the development of clinical treatment regimens. The aim of this study was to establish a stable prognostic gene set to guide the clinical diagnosis and treatment of gastric cancer.
Methods
A public microarray dataset of TCGA providing clinical information was obtained. The selection operator regression method was used to reduce the dimensionality of stable prognostic genes identified via the bootstrap method and survival analysis.
Conclusion
We established two prognostic models, respectively designated as stable gene risk score of OS(SGRS-OS) and stable gene risk score of PFI(SGRS-PFI) consisting of 18 and 21 genes. With specific risk score formulae, the SGRS set possesses a strong ability to predict overall survival and progression-free interval through both univariate and multivariate analyses. Compared with the TNM stage, the SGRS set showed much higher predictive accuracy. Further analysis revealed that patients with higher SGRS exhibited worse chemotherapy outcomes. Our SGRS set may be an effective tool to predict survival and guide treatment in patients with gastric cancer.
Keywords
Gastric cancer, Molecular typing, Prognosis, Prediction of efficacy of chemotherapy, Immune infiltration
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No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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CURRENT STATUS: Under Review
Version 1
Posted 08 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 19 Mar, 2021
Reviews received
Received 07 Mar, 2021
Reviewers agreed
On 19 Feb, 2021
Reviewers invited
Invitations sent on 19 Feb, 2021
Editor assigned
On 19 Feb, 2021
Editor invited
On 05 Feb, 2021
Submission checks complete
On 05 Feb, 2021
First submitted
On 27 Jan, 2021
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Huanbing Liu
First Affiliated Hospital of Nanchang University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 08 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 19 Mar, 2021
Reviews received
Received 07 Mar, 2021
Reviewers agreed
On 19 Feb, 2021
Reviewers invited
Invitations sent on 19 Feb, 2021
Editor assigned
On 19 Feb, 2021
Editor invited
On 05 Feb, 2021
Submission checks complete
On 05 Feb, 2021
First submitted
On 27 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Gastric cancer is one of the leading causes of cancer-related death worldwide. How to eliminate gastric cancer is an urgent public health problem. Prediction of prognosis is critical to the development of clinical treatment regimens. The aim of this study was to establish a stable prognostic gene set to guide the clinical diagnosis and treatment of gastric cancer.
Methods
A public microarray dataset of TCGA providing clinical information was obtained. The selection operator regression method was used to reduce the dimensionality of stable prognostic genes identified via the bootstrap method and survival analysis.
Conclusion
We established two prognostic models, respectively designated as stable gene risk score of OS(SGRS-OS) and stable gene risk score of PFI(SGRS-PFI) consisting of 18 and 21 genes. With specific risk score formulae, the SGRS set possesses a strong ability to predict overall survival and progression-free interval through both univariate and multivariate analyses. Compared with the TNM stage, the SGRS set showed much higher predictive accuracy. Further analysis revealed that patients with higher SGRS exhibited worse chemotherapy outcomes. Our SGRS set may be an effective tool to predict survival and guide treatment in patients with gastric cancer.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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