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3-D Cell Culture System for Studying Invasion and Evaluating Therapeutics in Bladder Cancer
Published on: September 13, 2018
Multi-omics analysis reveals critical metabolic regulators in bladder cancer
Chengcheng Wei1, Changqi Deng1, Rui Dong2
1Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Background:
The crosstalk between genomic alterations and metabolic dysregulation in bladder cancer is largely unknown. A deep understanding of the interactions between cancer drivers and cancer metabolic changes will provide novel opportunities for targeted therapeutic strategies.
Methods:
Three primary bladder cancer specimens with paired normal tissues or blood samples were subjected to whole-exome sequencing, DNA methylation array and whole-transcriptome sequencing by next-generation sequencing technology. We applied the methods to multi-omics data combining the Cancer Genome Atlas (TCGA) bladder cancer samples, including somatic mutation, DNA copy number, DNA methylation and gene expression profile for validation.
Results:
We identified 34 mutated cancer driver genes in bladder cancer. KDM6A was the most significantly mutated cancer driver gene. Metabolic pathways were enriched in both differentially methylated regions (DMRs) and differentially expressed genes. Twenty-nine DMRs in the TSS200 region were highly correlated with the upregulation of gene expression, and 24 DMRs in the genome were highly correlated with the downregulation of gene expression. A total of 201 genes had highly correlated DNA methylation and expression. Thirty-four genes, including the known metabolic genes CXXC5, PRR5, ABCB8 and BAHD1, were further validated in the TCGA cohort. Multi-omics alterations identified two new candidate driver genes, WIPI2 and GFM2, that warrant future studies.
Conclusions:
This study provides a comprehensive and systematic analysis, focusing on identifying key regulatory factors that may lead to cancer metabolic heterogeneity. Further understanding and verification of the cancer genes driving metabolic reprogramming and their role in the progression of bladder cancer will help to identify new therapeutic targets.
Insights
Genomic alterations and metabolic changes in bladder cancer were analyzed using multi-omics data. Key regulatory factors driving metabolic reprogramming and potential new therapeutic targets were identified.
Area of Science:
- Oncology
- Genomics
- Metabolomics
Background:
- The interplay between genomic alterations and metabolic dysregulation in bladder cancer remains poorly understood.
- Investigating these interactions is crucial for developing targeted therapeutic strategies.
Purpose of the Study:
- To comprehensively analyze the relationship between genomic alterations and metabolic reprogramming in bladder cancer.
- To identify key regulatory factors contributing to cancer metabolic heterogeneity and potential therapeutic targets.
Main Methods:
- Whole-exome sequencing, DNA methylation array, and whole-transcriptome sequencing were performed on bladder cancer specimens.
- Multi-omics data from The Cancer Genome Atlas (TCGA) bladder cancer cohort were used for validation, including somatic mutations, copy number variations, DNA methylation, and gene expression.
- Analysis focused on identifying correlations between genomic alterations, DNA methylation patterns, and gene expression in metabolic pathways.
Main Results:
- Identified 34 mutated cancer driver genes, with KDM6A being the most significant.
- Metabolic pathways were enriched in differentially methylated regions (DMRs) and differentially expressed genes.
- Discovered correlations between DNA methylation and gene expression, identifying 201 genes with highly correlated patterns.
- Validated 34 genes, including known metabolic genes, in the TCGA cohort and identified WIPI2 and GFM2 as potential new driver genes.
Conclusions:
- This study offers a systematic analysis of factors driving bladder cancer metabolic heterogeneity.
- Further research into cancer genes driving metabolic reprogramming can lead to the identification of novel therapeutic targets for bladder cancer.
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