Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay Between Metabolism and Methylation in Myelodysplastic Syndrome

Background: The metabolic and methylation interactions among mutated genes in myelodysplastic syndrome (MDS) represent promising avenues for novel anticancer therapies. This study investigates the mutational and transcriptomic landscapes of MDS to identify hallmark gene mutations, deregulated pathways, and their implications for disease pathogenesis and therapeutic strategies. Methods: A retrospective, cross-sectional analysis was conducted using mutational data from the cBioPortal for Cancer Genomics (UTokyo, Tokyo, Japan; Nature 2011) and multicenter MDS cohorts (Wellcome Trust Sanger Institute, Hinxton, UK; 2020). Transcriptomic data were sourced from three publicly accessible datasets in the Gene Expression Omnibus database: GSE114922 (Wellcome Trust Centre for Human Genetics, Oxford, UK; 2018), GSE63569 (University of Oxford, Oxford, UK; 2014), and GSE183328 (National Institute for Bioprocessing Research and Training (NIBRT), Dublin, Ireland; 2022). Integrated mutational and transcriptomic data analyses were performed to uncover connections between genetic alterations and metabolic deregulation. Results: We identified a set of genes harboring mutations that form mutually exclusive modules, potentially driving alterations in cellular metabolism and DNA methylation in patients with MDS. Transcriptomic analyses revealed significant upregulation of these mutated genes, implicating them in disease pathogenesis. Pathway enrichment analysis further elucidated dysregulation in key metabolic processes, including oxidative phosphorylation (OXPHOS), glycolysis, and epigenetic regulation via DNA methylation. These findings highlight the molecular heterogeneity of MDS and its intricate interplay with metabolic and epigenetic networks. Moreover, risk stratification models incorporating DNA methyltransferase 3 alpha (<i>DNMT3A</i>) and tet methylcytosine dioxygenase 2 (<i>TET2</i>) mutations demonstrated robust predictive value for overall survival, reinforcing their clinical relevance and prognostic utility in oncological contexts. Conclusions: This study offers novel insights into the molecular, metabolic, and DNA methylation mechanisms driving MDS. Integrating mutational signatures with transcriptomic data reveals potential therapeutic targets within key metabolic and DNA methylation pathways. These findings lay the foundation for developing personalized treatment strategies and refining risk stratification models for MDS and related malignancies.