PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS)
Version 1
: Received: 18 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (11:25:32 CET)
Version 2
: Received: 16 February 2024 / Approved: 22 February 2024 / Online: 22 February 2024 (11:39:16 CET)
How to cite:
Alzamzami, W.; Alatawi, S. Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS). Preprints2024, 2024021079. https://doi.org/10.20944/preprints202402.1079.v2
Alzamzami, W.; Alatawi, S. Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS). Preprints 2024, 2024021079. https://doi.org/10.20944/preprints202402.1079.v2
Alzamzami, W.; Alatawi, S. Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS). Preprints2024, 2024021079. https://doi.org/10.20944/preprints202402.1079.v2
APA Style
Alzamzami, W., & Alatawi, S. (2024). Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS). Preprints. https://doi.org/10.20944/preprints202402.1079.v2
Chicago/Turabian Style
Alzamzami, W. and Sael Alatawi. 2024 "Integrated Multi-Cohort Analysis of Whole-Genome and Transcriptome Data Reveals Interplay between Metabolism and Methylation in Myelodysplastic Syndrome (MDS)" Preprints. https://doi.org/10.20944/preprints202402.1079.v2
Abstract
Objectives: Metabolic interactions amongst mutated genes in myelodysplastic syndrome (MDS) offer promising avenues for novel anticancer treatments. Our comprehensive study delves into these mutational and transcriptomic landscapes, pinpointing hallmark gene mutations and deregulated gene expression that could influence MDS patients’ metabolomes.
Methods: The study utilized a retrospective, cross-sectional approach, employing mutational data on the cBio Cancer Genomics Portal conducted and reported by the University of Tokyo in 2011 and multi-center MDS cohorts regulated by Wellcome Trust Sanger Institute, United Kingdom, all in 2020. For transcriptomic data, we selected three publicly accessible independent cohorts on the Gene Expression Omnibus (GEO) database (GSE114922 in Wellcome Trust Centre for Human Genetics, United Kingdome, GSE63569 in University of Oxford, United Kingdome, and GSE183328 in CIMA, Spain) held in 2015, 2018, and 2022, respectively. To compile clinical, mutational, and transcriptomic data on MDS patients from multiple datasets and studies. This meta-analysis included genomic data derived from cellular genomics sources to assess mutations in specific genes, alongside an examination of transcriptomic data from three separate datasets that have been previously published.
Results: DNMT3A presented a 20% mutation frequency, playing a pivotal role in MDS metabolomics. The DNMT3A gene mutations displayed significant mutual exclusivity with the SRSF2, ASXL1, JAK2, and TP53 genes. The mutational analysis also showed that the gene expression landscape in MDS is associated with alterations to DNA methylation pathways.
Conclusion: This analysis suggests a potential therapeutic niche. Identifying signature genes in MDS that have metabolic and methylation affiliations could illuminate the disease's intricate biology and inspire novel treatments.
Keywords
Myelodysplastic syndrome (MDS); Metabolism; DNA Methylation; DNMT3A; TET2
Subject
Medicine and Pharmacology, Hematology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
