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Primary research
Yuyan Zhu
Department of Urology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0479-3151
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Bladder cancer is the tenth most common cancer in the world, but existing biomarkers and prognostic models are limited.
Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used selected genes to construct a prognostic model. Kaplan-Meier analysis, Receiver Operating Characteristic curve, and univariate and multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.
Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The model and the 11 genes have excellent performance in predicting overall survival and have been confirmed in 5 cohorts. The model's predictive ability is stronger than other clinical features and has practical significance in clinical application.
Through the analysis of the weighted co-expression network, the gene module related to the model was found, and the key genes in this module were mainly enriched in the items related to the tumor microenvironment. When comparing the level of immune cell infiltration in high-risk samples, B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.
Conclusion: The model we developed has strong stability and good performance and can stratify the risk of bladder cancer patients, to achieve individualized treatment.
Keywords
Bladder cancer, Cox regression, Prognostic model, Overall survival
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CURRENT STATUS: Published
View the published article at Cancer Cell International.
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Editorial decision: Accept
On 08 Aug, 2020
Editor assigned
On 04 Aug, 2020
Submission checks complete
On 03 Aug, 2020
Editor invited
On 03 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 31 Jul, 2020
Review #2 received
Received 27 Jul, 2020
Reviewer #1 agreed
On 27 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Reviewers invited
Invitations sent on 26 Jul, 2020
Editor assigned
On 24 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
Version 1
Posted 16 Jun, 2020
No comments provided
Editorial decision: Major revision
On 08 Jul, 2020
Review #2 received
Received 07 Jul, 2020
Reviewer #3 agreed
On 29 Jun, 2020
Review #3 received
Received 29 Jun, 2020
Reviewer #2 agreed
On 26 Jun, 2020
Review #1 received
Received 25 Jun, 2020
Reviewers invited
Invitations sent on 23 Jun, 2020
Reviewer #1 agreed
On 23 Jun, 2020
Editor assigned
On 17 Jun, 2020
Editor invited
On 16 Jun, 2020
Submission checks complete
On 12 Jun, 2020
First submitted
On 11 Jun, 2020
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Subject Areas
Cancer Biology
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Cancer Cell International.
This is a preprint, 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
Primary research
Yuyan Zhu
Department of Urology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0479-3151
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: Published
View the published article at Cancer Cell International.
No community comments so far
Editorial decision: Accept
On 08 Aug, 2020
Editor assigned
On 04 Aug, 2020
Submission checks complete
On 03 Aug, 2020
Editor invited
On 03 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 31 Jul, 2020
Review #2 received
Received 27 Jul, 2020
Reviewer #1 agreed
On 27 Jul, 2020
Reviewer #2 agreed
On 27 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Reviewers invited
Invitations sent on 26 Jul, 2020
Editor assigned
On 24 Jul, 2020
Submission checks complete
On 23 Jul, 2020
Editor invited
On 23 Jul, 2020
Version 1
Posted 16 Jun, 2020
No comments provided
Editorial decision: Major revision
On 08 Jul, 2020
Review #2 received
Received 07 Jul, 2020
Reviewer #3 agreed
On 29 Jun, 2020
Review #3 received
Received 29 Jun, 2020
Reviewer #2 agreed
On 26 Jun, 2020
Review #1 received
Received 25 Jun, 2020
Reviewers invited
Invitations sent on 23 Jun, 2020
Reviewer #1 agreed
On 23 Jun, 2020
Editor assigned
On 17 Jun, 2020
Editor invited
On 16 Jun, 2020
Submission checks complete
On 12 Jun, 2020
First submitted
On 11 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Cancer Cell International.
Background: Bladder cancer is the tenth most common cancer in the world, but existing biomarkers and prognostic models are limited.
Method: In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used selected genes to construct a prognostic model. Kaplan-Meier analysis, Receiver Operating Characteristic curve, and univariate and multivariate Cox analysis were used to evaluate the prognostic model for the four cohorts. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.
Results: A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The model and the 11 genes have excellent performance in predicting overall survival and have been confirmed in 5 cohorts. The model's predictive ability is stronger than other clinical features and has practical significance in clinical application.
Through the analysis of the weighted co-expression network, the gene module related to the model was found, and the key genes in this module were mainly enriched in the items related to the tumor microenvironment. When comparing the level of immune cell infiltration in high-risk samples, B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.
Conclusion: The model we developed has strong stability and good performance and can stratify the risk of bladder cancer patients, to achieve individualized treatment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This is a list of supplementary files associated with this preprint. Click to download.
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