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.
Research
Zhan-Ping Xu
foshan hospital of traditional chinese medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8695-5486
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.
Methods
The Robust Rank Aggregation (RRA) approach was adopted to integrate four eligible bladder urothelial carcinoma (BLCA) microarray datasets from the GEO. Differentially expressed genes (DEGs) sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using WGCNA to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the WGCNA and RRA to screen DEGs.
Results
CDH11, COL6A3, EDNRA and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, Receiver operating characteristics (ROC) curves and Kaplan-Meier (KM) plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on GSEA for single hub gene, all the genes were closely linked to BC cell proliferation.
Conclusions
These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.
Keywords
Bladder cancer (BC), hub genes, bioinformatics, weighted gene co-expression network analysis, robust rank aggregation
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryFigure1.tif
Supplementary Figure 1. Study workflow. KEGG: Kyoto Encyclopedia of Genes and Genomes, GSEA: Gene Set Enrichment Analyses GEO: Gene Expression Omnibus, GO: Gene Ontology, TCGA: The Cancer Genome Atlas, TIMER: Tumor Immune Estimation Resource, WGCNA: Weighted Gene Co-expression Network Analysis.
- SupplementaryFigure2.tif
Supplementary Figure 2. Survival analysis of all hub genes in the WGCNA blue module. Kaplan-Meier plots of disease-free survival in two group divided by each hub genes’ best-separation value.
- SupplementaryFigure3.tif
Supplementary Figure 3. ROC curves for CDH11, COL6A3, EDNRA and SERPINF1. ROC, receiver operating characteristic, AUC, area under the ROC curve.
- Supplementaryfile1.xlsx
- Supplementaryfile2.xlsx
- TableI.docx
Supplementary Table Ⅰ. Details of the GEO bladder cancer data.
- TableII.docx
Supplementary Table Ⅱ. Top 15 GO enrichment terms linked to the upregulated genes.
- TableIII.docx
Supplementary Table Ⅲ. Top 15 GO enrichment terms associated with the downregulated genes.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 Nov, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
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.
Research
Zhan-Ping Xu
foshan hospital of traditional chinese medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8695-5486
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 Nov, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.
Methods
The Robust Rank Aggregation (RRA) approach was adopted to integrate four eligible bladder urothelial carcinoma (BLCA) microarray datasets from the GEO. Differentially expressed genes (DEGs) sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using WGCNA to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the WGCNA and RRA to screen DEGs.
Results
CDH11, COL6A3, EDNRA and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, Receiver operating characteristics (ROC) curves and Kaplan-Meier (KM) plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on GSEA for single hub gene, all the genes were closely linked to BC cell proliferation.
Conclusions
These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryFigure1.tif
Supplementary Figure 1. Study workflow. KEGG: Kyoto Encyclopedia of Genes and Genomes, GSEA: Gene Set Enrichment Analyses GEO: Gene Expression Omnibus, GO: Gene Ontology, TCGA: The Cancer Genome Atlas, TIMER: Tumor Immune Estimation Resource, WGCNA: Weighted Gene Co-expression Network Analysis.
- SupplementaryFigure2.tif
Supplementary Figure 2. Survival analysis of all hub genes in the WGCNA blue module. Kaplan-Meier plots of disease-free survival in two group divided by each hub genes’ best-separation value.
- SupplementaryFigure3.tif
Supplementary Figure 3. ROC curves for CDH11, COL6A3, EDNRA and SERPINF1. ROC, receiver operating characteristic, AUC, area under the ROC curve.
- Supplementaryfile1.xlsx
- Supplementaryfile2.xlsx
- TableI.docx
Supplementary Table Ⅰ. Details of the GEO bladder cancer data.
- TableII.docx
Supplementary Table Ⅱ. Top 15 GO enrichment terms linked to the upregulated genes.
- TableIII.docx
Supplementary Table Ⅲ. Top 15 GO enrichment terms associated with the downregulated genes.
Loading...