See the published version of this preprint at Journal of Ovarian Research.
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Changyu Wang
Huazhong University of Science and Technology Tongji Medical College
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3583-1379
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Ovarian cancer is the second fatal malignancy of the female reproductive system. Based on the cancer stem cell (CSC) theory, its poor prognosis of ovarian cancer attributed to tumor recurrence caused by CSCs. A variety of cell surface-specific markers have been employed to identify ovarian cancer stem cells (OCSCs). In this study, we attempted to explore the common feature in ovarian cancer stem cells sorted by multiple approaches.
Methods: We collected the gene expression profiles of OCSCs were from 5 public cohorts and employed R software and Bioconductor packages to establish differently expressed genes (DEGs) between OCSCs and parental cells. We extracted the integrated DEGs by protein-protein interaction (PPI) network construction and explored potential treatment by the Cellminer database.
Results: We identified and integrated the DEGs of OCSCs sorted by multiple isolation approaches. Besides, we identified OCSCs share characteristics in the lipid metabolism and extracellular matrix changes. Moreover, we obtained sixteen co-expressed core genes, such as FOXQ1, MMP7, AQP5, RBM47, ETV4, NPW, SUSD2, SFRP2, IDO1, ANPEP, CXCR4, SCNN1A, SPP1 and IFI27 (upregulated) and SERPINE1, DUSP1, CD40, and IL6 (downregulated). Through correlation analysis, we screened out ten potential drugs to target the core genes.
Conclusion: Based on the comprehensive analysis of the genomic datasets with different sorting methods of OCSCs, we figured out the common driving genes to regulating OCSC and obtained ten new potential therapies for eliminating ovarian cancer stem cells. Hence, the findings of our study might have potential clinical significance.
Keywords
bioinformatic analysis, ovarian cancer, ovarian cancer stem cells (OCSCs), cancer stem cells markers, differentially expressed genes (DEGs), therapeutic targets
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This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.tif
Data processing in the GSE28799 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE28799. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE28799. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS2.tif
Data processing in the GSE53759 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE53759. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE53759. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS3.tif
Data processing in the GSE94358 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE94358. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE94358. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS4.tif
PPI networks analysis of the blue module A. Cluster dendrogram of 20 samples in GSE33874. B. PPI networks of genes in the blue module. C. The top 3 subnets of network in (B).
- TableS1.xls
- TableS2.xls
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View the published article at Journal of Ovarian Research.
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Editorial decision: Accept
On 31 Aug, 2020
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Received 30 Aug, 2020
Reviewer #2 agreed
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Received 22 Jul, 2020
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Reviewer #1 agreed
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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 Journal of Ovarian Research.
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
Changyu Wang
Huazhong University of Science and Technology Tongji Medical College
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3583-1379
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 Journal of Ovarian Research.
Version 1
Posted 23 Jul, 2020
No community comments so far
Editorial decision: Accept
On 31 Aug, 2020
Review #2 received
Received 30 Aug, 2020
Reviewer #2 agreed
On 16 Aug, 2020
Review #1 received
Received 22 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Reviewer #1 agreed
On 21 Jul, 2020
Editor assigned
On 20 Jul, 2020
Submission checks complete
On 19 Jul, 2020
Editor invited
On 19 Jul, 2020
First submitted
On 18 Jul, 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 Journal of Ovarian Research.
Background: Ovarian cancer is the second fatal malignancy of the female reproductive system. Based on the cancer stem cell (CSC) theory, its poor prognosis of ovarian cancer attributed to tumor recurrence caused by CSCs. A variety of cell surface-specific markers have been employed to identify ovarian cancer stem cells (OCSCs). In this study, we attempted to explore the common feature in ovarian cancer stem cells sorted by multiple approaches.
Methods: We collected the gene expression profiles of OCSCs were from 5 public cohorts and employed R software and Bioconductor packages to establish differently expressed genes (DEGs) between OCSCs and parental cells. We extracted the integrated DEGs by protein-protein interaction (PPI) network construction and explored potential treatment by the Cellminer database.
Results: We identified and integrated the DEGs of OCSCs sorted by multiple isolation approaches. Besides, we identified OCSCs share characteristics in the lipid metabolism and extracellular matrix changes. Moreover, we obtained sixteen co-expressed core genes, such as FOXQ1, MMP7, AQP5, RBM47, ETV4, NPW, SUSD2, SFRP2, IDO1, ANPEP, CXCR4, SCNN1A, SPP1 and IFI27 (upregulated) and SERPINE1, DUSP1, CD40, and IL6 (downregulated). Through correlation analysis, we screened out ten potential drugs to target the core genes.
Conclusion: Based on the comprehensive analysis of the genomic datasets with different sorting methods of OCSCs, we figured out the common driving genes to regulating OCSC and obtained ten new potential therapies for eliminating ovarian cancer stem cells. Hence, the findings of our study might have potential clinical significance.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.tif
Data processing in the GSE28799 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE28799. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE28799. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS2.tif
Data processing in the GSE53759 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE53759. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE53759. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS3.tif
Data processing in the GSE94358 dataset. A. The volcano plot showed differentially expressed genes (DEGs) between the two groups of samples in GSE94358. Based on an adjusted P < 0.05 and |log fold change| > 1, the red spots represent the upregulated genes and the blue spots represent the downregulated genes; the grey spots represent genes with no significant difference. B. The heatmap of the top 100 DEGs in GSE94358. Orange indicates relative upregulated genes; Blue indicates the relative downregulated gene; yellow suggests no significant change in gene expression;
- FigureS4.tif
PPI networks analysis of the blue module A. Cluster dendrogram of 20 samples in GSE33874. B. PPI networks of genes in the blue module. C. The top 3 subnets of network in (B).
- TableS1.xls
- TableS2.xls
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