第944回千葉医学会例会・第1外科教室談話会

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Table1_Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile.XLS

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.</p

MOESM17 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 17: Figure S17. Enrichment analysis of key genes in DNA methylation network. A. Enrichment analysis of key genes in DNA methylation correlation network. B. Enrichment analysis of key genes in KEGG pathway network

MOESM4 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 4: Figure S4. The enrichment analysis of all differential methylated genes in ESCA. The figure shows the enriched pathways and the top 16 GO terms

MOESM18 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 18: Figure S18. The node degree distribution of the KEGG pathway network

MOESM13 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 13: Figure S13. The enrichment analysis of differential methylated genes in LUSC. A. The enrichment analysis of hypermethylated genes in LUSC. B. The enrichment analysis of hypomethylated genes in LUSC

MOESM3 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 3: Figure S3. The enrichment analysis of all differential methylated genes in COAD. The figure shows the enriched pathways and the top 20 GO terms

MOESM1 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 1: Figure S1. The numbers of differentially methylated genes in seven cancers

MOESM2 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 2: Figure S2. The enrichment analysis of all differential methylated genes in BRCA. The figure shows the enriched pathways and the top 17 GO terms

MOESM10 of An analysis about heterogeneity among cancers based on the DNA methylation patterns

Additional file 10: Figure S10. The enrichment analysis of differential methylated genes in COAD. A. The enrichment analysis of hypermethylated genes in COAD. B. The enrichment analysis of hypomethylated genes in COAD