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Updated: Dec 21, 2025
Cell-Free DNA Integrity Analysis in Urine Samples
Published on: January 5, 2017
Identifying the mRNAs associated with Bladder cancer recurrence
Huifeng Cao1, Liang Cheng1, Junjuan Yu1
1Department of Urology, First Affiliated Hospital of Jiamusi University, Jiamusi City, Heilongjiang, China.
Objective:
To identify the mRNAs associated with bladder cancer (BC) recurrence.
Methods:
The transcription profile of GSE31684 including 39 recurrent BC tumor samples and 54 non-recurrent BC tumor samples as well as transcription profile of GSE13507 including 36 recurrent BC tumor samples and 67 non-recurrent BC tumor samples were downlaoded from the Gene Expression Omnibus. Then, the differentially expressed genes (DEGs) were identified using linear models for microarray data (limma) and the intersections of DEGs from the two datasets were further screened. The weighed gene co-expression network analysis (WGCNA) was used to screen the modules related to BC recurrence. Protein-protein interaction (PPI) network analysis was used to analyze the genes interaction. Their functions were predicted by Gene Ontology and KEGG pathway enrichment. Moreover, The Comparative Toxicogenomics Database 2017 update (CTD) was used to search the BC related pathway. The univariate cox regression analysis was used to identify DEGs associated to the recurrence. Kaplan-Meier plots were used to illustrate recurrence free survival time (RFS).
Results:
A total of 692 intersections DEGs were screened. WGCNA showed that 7 modules (2279 genes) were stable in both the datasets. A total of 169 intersection DEGs were mapped to the 7 modules. There existed 149 interaction relationships among 81 proteins (18 down-regulated and 63 up-regulated DEGs) in the PPI network. Two KEGG pathways including Focal adhesion and ECM-receptor interaction were enriched which were also found in the CTD. The univariate cox regression analysis showed that 3 DEGs (COL4A1, COL1A2 and COL5A1) were significant related to the prognosis. Multivariate cox regression analysis revealed that pathologic_N (N0-N1 vs N2-N3, p= 0.033) were independent prognostic factors for overall survival in patients with BC.
Conclusion:
COL4A1, COL1A2 and COL5A1 could be associated with BC recurrence.
Insights
This study identified key genes, COL4A1, COL1A2, and COL5A1, associated with bladder cancer (BC) recurrence. These findings may improve understanding and prediction of BC recurrence risk.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Bladder cancer (BC) recurrence poses a significant clinical challenge.
- Identifying molecular markers for BC recurrence is crucial for improved patient management and prognosis.
Purpose of the Study:
- To identify messenger RNAs (mRNAs) significantly associated with bladder cancer recurrence.
- To explore the molecular mechanisms underlying BC recurrence through gene expression profiling and network analysis.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets (GSE31684, GSE13507) for differential gene expression analysis.
- Applied Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) network analysis to identify key modules and genes.
- Performed Gene Ontology (GO), KEGG pathway enrichment, and Comparative Toxicogenomics Database (CTD) analysis for functional annotation.
- Employed univariate and multivariate Cox regression analyses to assess the prognostic significance of identified genes.
Main Results:
- Identified 692 intersection differentially expressed genes (DEGs) across two independent datasets.
- WGCNA highlighted 7 stable modules containing 169 intersection DEGs.
- PPI network analysis revealed 81 proteins with 149 interactions, enriched in Focal adhesion and ECM-receptor interaction pathways.
- Univariate Cox analysis identified COL4A1, COL1A2, and COL5A1 as significantly associated with prognosis; multivariate analysis confirmed pathologic_N as an independent prognostic factor.
Conclusions:
- COL4A1, COL1A2, and COL5A1 are potential molecular markers associated with bladder cancer recurrence.
- These genes and pathways may play a role in the biological processes driving BC recurrence.
- Further validation is warranted to confirm their clinical utility in predicting BC recurrence.
