SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer

Background Clear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and metabolome. Given the subsequent tumour and inter-patient heterogeneity, drug-based treatments report limited success, calling for multi-omics...

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Bibliographic Details
Published in:Genome medicine Vol. 16; no. 1; pp. 144 - 26
Main Authors: Mora, Ariane, Schmidt, Christina, Balderson, Brad, Frezza, Christian, Bodén, Mikael
Format: Journal Article
Language:English
Published: London BioMed Central 04.12.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
AI applications in cancer discovery and care
Analysis
Anopheles
Bioinformatics
Biological analysis
Biomedical and Life Sciences
Biomedicine
Cancer
Cancer Research
Carcinoma, Renal Cell - genetics
Carcinoma, Renal Cell - metabolism
Care and treatment
Clear cell renal cell carcinoma
Clear cell-type renal cell carcinoma
Cohort Studies
Consortia
Datasets
Disease
DNA
DNA methylation
Electron transport
Enzymes
Gene expression
Gene Expression Regulation, Neoplastic
Genes
Genes, Neoplasm
Genomes
Genotype & phenotype
Glycolysis
Human Genetics
Humans
Integration
Kidney cancer
Kidney Neoplasms - genetics
Kidney Neoplasms - metabolism
Kidney Neoplasms - pathology
Machine learning
Medicine/Public Health
Metabolomics
Methylation
Mitochondrial DNA
Multi-omics
Mutation
Neoplasm Staging
Pancreatic cancer
Patients
Phenotype
Phenotypes
Physiological aspects
Proteins
Proteomes
Proteomics
Regulation
RNA
Squamous cell carcinoma
Statistics as Topic
Survival analysis
Systems Biology
Transcription Factors - metabolism
Transcriptomes
Tumors
Variational autoencoder
Integration
Machine learning
Variational autoencoder
Regulation
Clear cell renal cell carcinoma
Metabolism
Multi-omics
PanCan
ISSN:1756-994X, 1756-994X
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Summary:Background Clear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and metabolome. Given the subsequent tumour and inter-patient heterogeneity, drug-based treatments report limited success, calling for multi-omics studies to extract regulatory relationships, and ultimately, to develop targeted therapies. Yet, methods for multi-omics integration to reveal mechanisms of phenotype regulation are lacking. Methods Here, we present SiRCle ( Si gnature R egulatory Cl ust e ring), a method to integrate DNA methylation, RNA-seq and proteomics data at the gene level by following central dogma of biology, i.e. genetic information proceeds from DNA, to RNA, to protein. To identify regulatory clusters across the different omics layers, we group genes based on the layer where the gene’s dysregulation first occurred. We combine the SiRCle clusters with a variational autoencoder (VAE) to reveal key features from omics’ data for each SiRCle cluster and compare patient subpopulations in a ccRCC and a PanCan cohort. Results Applying SiRCle to a ccRCC cohort, we showed that glycolysis is upregulated by DNA hypomethylation, whilst mitochondrial enzymes and respiratory chain complexes are translationally suppressed. Additionally, we identify metabolic enzymes associated with survival along with the possible molecular driver behind the gene’s perturbations. By using the VAE to integrate omics’ data followed by statistical comparisons between tumour stages on the integrated space, we found a stage-dependent downregulation of proximal renal tubule genes, hinting at a loss of cellular identity in cancer cells. We also identified the regulatory layers responsible for their suppression. Lastly, we applied SiRCle to a PanCan cohort and found common signatures across ccRCC and PanCan in addition to the regulatory layer that defines tissue identity. Conclusions Our results highlight SiRCle’s ability to reveal mechanisms of phenotype regulation in cancer, both specifically in ccRCC and broadly in a PanCan context. SiRCle ranks genes according to biological features. https://github.com/ArianeMora/SiRCle_multiomics_integration .
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ISSN:1756-994X
1756-994X
DOI:10.1186/s13073-024-01415-3