Integrated bioinformatic analysis of microarray data reveals shared gene signature between MDS and AML

Abstract

Myeloid disorders, especially myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), cause significant mobility and high mortality worldwide. Despite numerous attempts, the common molecular events underlying the development of MDS and AML remain to be established. In the present study, 18 microarray datasets were selected, and a meta-analysis was conducted to identify shared gene signatures and biological processes between MDS and AML. Using NetworkAnalyst, 191 upregulated and 139 downregulated genes were identified in MDS and AML, among which, PTH2R, TEC, and GPX1 were the most upregulated genes, while MME, RAG1, and CD79B were mostly downregulated. Comprehensive functional enrichment analyses revealed oncogenic signaling related pathway, fibroblast growth factor receptor (FGFR) and immune response related events, ‘interleukine-6/interferon signaling pathway, and B cell receptor signaling pathway’, were the most upregulated and downregulated biological processes, respectively. Network based meta-analysis ascertained that HSP90AA1 and CUL1 were the most important hub genes. Interestingly, our study has largely clarified the link between MDS and AML in terms of potential pathways, and genetic markers, which shed light on the molecular mechanisms underlying the development and transition of MDS and AML, and facilitate the understanding of novel diagnostic, therapeutic and prognostic biomarkers.

Circulating Small Noncoding RNAs Have Specific Expression Patterns in Plasma and Extracellular Vesicles in Myelodysplastic Syndromes and Are Predictive of Patient Outcome

“…It has been previously noted that there are significant biological and clinical differences between AML and MDS, and based on these observations, it has been warranted that MDS should not be considered only as an early phase of AML or as preleukemia [40]. However, based on the link between MDS and AML-MRC in terms of potential pathways and genetic biomarkers [41][42][43] and similar molecular characteristics described here and in several preceding papers, it is evident that MDS and AML-MRC share important features on the biological level, enabling the application of similar therapeutic approaches that specifically address both of these pathological entities.…”

Cell lineage-specific methylome and genome alterations in gout

“…To explore functional relationships between differentially methylated genes, regulatory networks were constructed for genes containing differentially methylated CpG sites using NetworkAnalyst ( Supplementary Figure 1 , Step IIc) [ 87 ] and were visualized with Cytoscape [ 87 ]. NetworkAnalyst integrates machine learning and Walktrap algorithms and uses protein-protein interaction data from the IMEx Interactome database to perform topology analysis that examines the overall network structure to identify important genes (hubs) that act as critical players in biological networks [ 87 ].…”

Computational modeling of methylation impact of AML drivers reveals new pathways and refines AML risk-stratification

“…Topics 2, 8 and 12 have shared components and is associated w ith ELN adverse category and a complex set of associated mutations in RUNX1, BCOR, ASXL1, TP53, SRSF2 and 5q and 7q deletions. Topic 2 has multiple DMRs near PTH2R, parathyroid hormone receptor, w hich w as show n to be the most upregulated gene in MDS and AML 79 and differentially expressed in patients w ith IDH2 mutations 80 . Topic 8 has DMRs in close proximity to KDM2B, a key lysine demethylase in AML; BCL7A, a BAF remodeling tumor suppressor 81 , and TCF7L2, a WNT pathw ay transcription factor implicated in regeneration of hematopoietic stem cells 82 .…”