ArticleInternational urology and nephrology2024
Multi-omics analysis reveals critical metabolic regulators in bladder cancer.
Article in International urology and nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Integration of genetic, proteomic, and transcriptomic data identifies therapeutic targets and prognostic biomarkers in bladder cancer.Translational andrology and urology · 2025Article
- Signal integrator function of CXXC5 in Cancer.Cell communication and signaling : CCS · 2025Review
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Authors and funding
8 authors at 4 institutions in 1 country.
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Abstract
backgroundThe 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.
methodsThree 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.
resultsWe 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.
conclusionsThis 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.
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