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Research article
Ying Tang
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5666-8139
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Due to tumor heterogeneity, the diagnosis, treatment, and prognosis of patients with lung squamous cell carcinoma (LUSC) are difficult. DNA methylation can affect tumor heterogeneity by participating in gene expression.
Methods: In this study, we collected the clinical information of LUSC patients in the Cancer Genome Atlas (TCGA) database and the relevant methylated sequences of the University of California Santa Cruz (UCSC) database to construct methylated subtypes and performed prognostic analysis.
Results: 965 potential independent prognosis methylation sites were finally identified and the genes were identified. Based on consensus clustering analysis, seven subtypes were identified by using 965 CpG sites and corresponding survival curves were plotted. The prognostic analysis model was constructed according to the methylation sites’ information of the subtype with the best prognosis. Internal and external verifications were used to evaluate the prediction model.
Conclutions: Models based on differences in DNA methylation levels may help to classify the molecular subtypes of LUSC patients, and provide more individualized treatment recommendations and prognostic assessments for different clinical subtypes. GNAS, FZD2, FZD10 are the core three genes that may be related to the prognosis of LUSC patients.
Keywords
Lung squamous cell carcinoma, DNA methylation, molecular subtype, TCGA, prognosis
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This is a list of supplementary files associated with this preprint. Click to download.
- AppendixS1..xlsx
Appendix S1. The clinical information and follow-up data of 504 patients
- AppendixS2.xlsx
Appendix S2. The clinical information of External training dataset
- AppendixS3.xlsx
Appendix S3. The clinical information of external testing dataset
- AppendixS4..xlsx
Appendix S4. Univariate Cox regression analysis of the training dataset（1159）
- AppendixS5.xlsx
Appendix S5. Multivariate Cox regression analysis of the 965 methylation sites（965）
- AppendixS6.xlsx
Appendix S6. Functional enrichment analysis and the identified 27 enriched pathways
- AppendixS7.xlsx
Appendix S7. The available expression profile of 965 sites in 266 training set samples
- AppendixS8.xlsx
Appendix S8. Calculating differences of each methylation sites among 7 clusters
- AppendixS9.xlsx
Appendix S9. The 41 cluster-specific methylation sites
- AppendixS10.xlsx
Appendix S10. Genome annotations of the 41 cluster-specific methylation sites
- AppendixS11.xlsx
Appendix S11. Functional enrichment analysis and the enriched 30 pathways
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CURRENT STATUS: Published
View the published article at BMC Cancer.
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Posted 15 Oct, 2020
No community comments so far
Editorial decision: Major revision
On 04 Nov, 2020
Review #2 received
Received 01 Nov, 2020
Review #1 received
Received 23 Oct, 2020
Reviewer #2 agreed
On 12 Oct, 2020
Reviewers invited
Invitations sent on 09 Oct, 2020
Reviewer #1 agreed
On 09 Oct, 2020
Editor assigned
On 08 Oct, 2020
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On 07 Oct, 2020
Editor invited
On 07 Oct, 2020
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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 BMC Cancer.
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
Research article
Ying Tang
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5666-8139
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 BMC Cancer.
Version 1
Posted 15 Oct, 2020
No community comments so far
Editorial decision: Major revision
On 04 Nov, 2020
Review #2 received
Received 01 Nov, 2020
Review #1 received
Received 23 Oct, 2020
Reviewer #2 agreed
On 12 Oct, 2020
Reviewers invited
Invitations sent on 09 Oct, 2020
Reviewer #1 agreed
On 09 Oct, 2020
Editor assigned
On 08 Oct, 2020
Submission checks complete
On 07 Oct, 2020
Editor invited
On 07 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Cancer.
Background: Due to tumor heterogeneity, the diagnosis, treatment, and prognosis of patients with lung squamous cell carcinoma (LUSC) are difficult. DNA methylation can affect tumor heterogeneity by participating in gene expression.
Methods: In this study, we collected the clinical information of LUSC patients in the Cancer Genome Atlas (TCGA) database and the relevant methylated sequences of the University of California Santa Cruz (UCSC) database to construct methylated subtypes and performed prognostic analysis.
Results: 965 potential independent prognosis methylation sites were finally identified and the genes were identified. Based on consensus clustering analysis, seven subtypes were identified by using 965 CpG sites and corresponding survival curves were plotted. The prognostic analysis model was constructed according to the methylation sites’ information of the subtype with the best prognosis. Internal and external verifications were used to evaluate the prediction model.
Conclutions: Models based on differences in DNA methylation levels may help to classify the molecular subtypes of LUSC patients, and provide more individualized treatment recommendations and prognostic assessments for different clinical subtypes. GNAS, FZD2, FZD10 are the core three genes that may be related to the prognosis of LUSC patients.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
This is a list of supplementary files associated with this preprint. Click to download.
- AppendixS1..xlsx
Appendix S1. The clinical information and follow-up data of 504 patients
- AppendixS2.xlsx
Appendix S2. The clinical information of External training dataset
- AppendixS3.xlsx
Appendix S3. The clinical information of external testing dataset
- AppendixS4..xlsx
Appendix S4. Univariate Cox regression analysis of the training dataset（1159）
- AppendixS5.xlsx
Appendix S5. Multivariate Cox regression analysis of the 965 methylation sites（965）
- AppendixS6.xlsx
Appendix S6. Functional enrichment analysis and the identified 27 enriched pathways
- AppendixS7.xlsx
Appendix S7. The available expression profile of 965 sites in 266 training set samples
- AppendixS8.xlsx
Appendix S8. Calculating differences of each methylation sites among 7 clusters
- AppendixS9.xlsx
Appendix S9. The 41 cluster-specific methylation sites
- AppendixS10.xlsx
Appendix S10. Genome annotations of the 41 cluster-specific methylation sites
- AppendixS11.xlsx
Appendix S11. Functional enrichment analysis and the enriched 30 pathways
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