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Research article
Shilong Fu
Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7416-3070
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC.
Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes.
Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC=0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003).
Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.
Keywords
Cervical squamous cell carcinoma, methylation, prognosis, risk model, SNX10, PTGDS
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Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
- FigureSupp.docx
- SupplementaryFigure12.tif
Figure S12. Regression analysis of the 5 genes identified. (A) The expression level of the 5 genes in low- and high-risk groups. (B) The univariate Cox proportional hazards regression. (C) The multivariate Cox proportional hazards regression. (D) The heatmap of the 5 genes in high- and low-risk patients in TCGA dataset.
- SupplementaryFigure11.tiff
Figure S11. Five genes were screened by Multivariate cox regression analysis
- SupplementaryFigure10.tif
Figure S10. Seven genes were screened by Univariate cox regression analysis.
- SupplementaryFigure9.tif
Figure S9. Gene expression in GSE7803 and GSE29570. (A) PTGDS in GSE7803. (B) SNX10 in GSE7803. (C) PTGDS in GSE29570. (D) SNX10 in GSE29570.
- SupplementaryFigure8.tif
Figure S8. Survival analysis of genes in GEPIA. (A) APOD, (B) CXCL8, (C) MMP1, (D) MMP3, (E) PLOD2, (F) PTGDS, (G) SNX10, (H) SPP1.
- SupplementaryFigure7.tif
Figure S7. Validation of genes expression in GEPIA. (A) APOD, (B) CXCL8, (C) MMP1, (D) MMP3, (E) PLOD2, (F) PTGDS, (G) SNX10, (H) SPP1.
- SupplementaryFigure6.tif
Figure S6. (A) A significant module selected from protein–protein interaction network. (B)GO analysis of these significant molecule.(C) KEGG analysis of these significant molecule.
- SupplementaryFigure5.tif
Figure S5. (A) Significant function enrichment of DEGs. Blue represents the GO groups, green represents downregulated genes and red represents upregulated genes. (B) Significant pathway enrichment of DEGs. Blue represents signaling pathway, green represents downregulated genes and red represents upregulated genes.
- SupplementaryFigure4.tif
Figure S4. GO enrichment analysis of DEGs in cervical cancer. (A) GO analysis divided DEGs into three functional groups: molecular function, biological processes, and cell composition. (B) GO enrichment significance items of DEGs in different functional groups.(C) Distribution of DEGs in cervical cancer for different GO-enriched functions.
- SupplementaryFigure3.tif
Figure S3. Hierarchical clustering heatmap of DEGs screened on the basis of |fold change| .2.0 and a corrected P-value ,0.05. Red indicates that the expression of genes is relatively upregulated, green indicates that the expression of genes is relatively downregulated, and black indicates no significant changes in gene expression; gray indicates that the signal strength of genes was not high enough to be detected. (A) GSE6791 data, (B) GSE9750 data, (C) GSE39001 data and (D) GSE63514 data.
- SupplementaryFigure2.tif
Figure S2. Differential expression of data between two sets of samples. The red points represent upregulated genes screened on the basis of |fold change| .2.0 and a corrected P-value of ,0.05. The green points represent downregulation of the expression of genes screened on the basis of |fold change| .2.0 and a corrected P-value of ,0.05. The black points represent genes with no significant difference. FC is the fold change.(A)GSE6791 data, (B) GSE9750 data, (C) GSE39001 data and (D) GSE63514 data.
- SupplementaryFigure1.tif
Figure S1. Standardization of gene expression. The blue bar represents the data before normalization, and the red bar represents the normalized data.(A) The standardization of GSE6791 data, (B) the standardization of GSE9750 data, (C) the standardization of GSE39001 data (D) the standardization of GSE63514 data.
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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
Shilong Fu
Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7416-3070
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 Revision
Version 4
Posted 12 Jan, 2021
No community comments so far
Submission checks complete
On 30 Dec, 2020
Editorial decision: Minor revision
On 20 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 23 Nov, 2020
Editor assigned
On 18 Nov, 2020
Submission checks complete
On 18 Nov, 2020
Editor invited
On 18 Nov, 2020
No comments provided
Editorial decision: Minor revision
On 09 Nov, 2020
Review #2 received
Received 07 Nov, 2020
Review #1 received
Received 24 Oct, 2020
Reviewer #2 agreed
On 14 Oct, 2020
Reviewers invited
Invitations sent on 13 Oct, 2020
Reviewer #1 agreed
On 13 Oct, 2020
Editor assigned
On 28 Sep, 2020
Submission checks complete
On 27 Sep, 2020
Editor invited
On 27 Sep, 2020
No comments provided
Editorial decision: Major revision
On 20 Aug, 2020
Review #2 received
Received 18 Aug, 2020
Review #1 received
Received 16 Aug, 2020
Reviewer #3 agreed
On 11 Aug, 2020
Reviewer #2 agreed
On 09 Aug, 2020
Reviewers invited
Invitations sent on 08 Aug, 2020
Reviewer #1 agreed
On 08 Aug, 2020
First submitted
On 28 Jul, 2020
Editor assigned
On 28 Jul, 2020
Submission checks complete
On 27 Jul, 2020
Editor invited
On 27 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC.
Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes.
Results: Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P<0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency(AUC=0.738). GSEA showed that the two genes were associated with the chemokine signaling pathway(P<0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p=0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients(P=0.007 and 0.003).
Conclusions: PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.
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.
- FigureSupp.docx
- SupplementaryFigure12.tif
Figure S12. Regression analysis of the 5 genes identified. (A) The expression level of the 5 genes in low- and high-risk groups. (B) The univariate Cox proportional hazards regression. (C) The multivariate Cox proportional hazards regression. (D) The heatmap of the 5 genes in high- and low-risk patients in TCGA dataset.
- SupplementaryFigure11.tiff
Figure S11. Five genes were screened by Multivariate cox regression analysis
- SupplementaryFigure10.tif
Figure S10. Seven genes were screened by Univariate cox regression analysis.
- SupplementaryFigure9.tif
Figure S9. Gene expression in GSE7803 and GSE29570. (A) PTGDS in GSE7803. (B) SNX10 in GSE7803. (C) PTGDS in GSE29570. (D) SNX10 in GSE29570.
- SupplementaryFigure8.tif
Figure S8. Survival analysis of genes in GEPIA. (A) APOD, (B) CXCL8, (C) MMP1, (D) MMP3, (E) PLOD2, (F) PTGDS, (G) SNX10, (H) SPP1.
- SupplementaryFigure7.tif
Figure S7. Validation of genes expression in GEPIA. (A) APOD, (B) CXCL8, (C) MMP1, (D) MMP3, (E) PLOD2, (F) PTGDS, (G) SNX10, (H) SPP1.
- SupplementaryFigure6.tif
Figure S6. (A) A significant module selected from protein–protein interaction network. (B)GO analysis of these significant molecule.(C) KEGG analysis of these significant molecule.
- SupplementaryFigure5.tif
Figure S5. (A) Significant function enrichment of DEGs. Blue represents the GO groups, green represents downregulated genes and red represents upregulated genes. (B) Significant pathway enrichment of DEGs. Blue represents signaling pathway, green represents downregulated genes and red represents upregulated genes.
- SupplementaryFigure4.tif
Figure S4. GO enrichment analysis of DEGs in cervical cancer. (A) GO analysis divided DEGs into three functional groups: molecular function, biological processes, and cell composition. (B) GO enrichment significance items of DEGs in different functional groups.(C) Distribution of DEGs in cervical cancer for different GO-enriched functions.
- SupplementaryFigure3.tif
Figure S3. Hierarchical clustering heatmap of DEGs screened on the basis of |fold change| .2.0 and a corrected P-value ,0.05. Red indicates that the expression of genes is relatively upregulated, green indicates that the expression of genes is relatively downregulated, and black indicates no significant changes in gene expression; gray indicates that the signal strength of genes was not high enough to be detected. (A) GSE6791 data, (B) GSE9750 data, (C) GSE39001 data and (D) GSE63514 data.
- SupplementaryFigure2.tif
Figure S2. Differential expression of data between two sets of samples. The red points represent upregulated genes screened on the basis of |fold change| .2.0 and a corrected P-value of ,0.05. The green points represent downregulation of the expression of genes screened on the basis of |fold change| .2.0 and a corrected P-value of ,0.05. The black points represent genes with no significant difference. FC is the fold change.(A)GSE6791 data, (B) GSE9750 data, (C) GSE39001 data and (D) GSE63514 data.
- SupplementaryFigure1.tif
Figure S1. Standardization of gene expression. The blue bar represents the data before normalization, and the red bar represents the normalized data.(A) The standardization of GSE6791 data, (B) the standardization of GSE9750 data, (C) the standardization of GSE39001 data (D) the standardization of GSE63514 data.
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