Multi-omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma.
Anastasia BrativnykJørgen AnkillÅslaug HellandThomas FleischerPublished in: International journal of cancer (2024)
Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.
Keyphrases
- dna methylation
- genome wide
- gene expression
- transcription factor
- copy number
- poor prognosis
- case report
- end stage renal disease
- binding protein
- healthcare
- mental health
- newly diagnosed
- machine learning
- prognostic factors
- single cell
- fatty acid
- long non coding rna
- deep learning
- immune response
- dna binding
- patient reported outcomes
- toll like receptor
- young adults
- squamous cell carcinoma
- dna damage
- inflammatory response
- long noncoding rna
- rna seq
- electronic health record
- nuclear factor
- single molecule
- data analysis