Discover oncology · 2026
An integrated multi-omics analysis reveals a core epigenetically-activated transcriptional network driving oncogenic signaling in acute myeloid leukemia.
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Zeng Lihua, Lei Haohao, Bi Jingnan, Run Guowei, Yu Bizhen, Ji Linhua
Аннотация
PURPOSE: Acute Myeloid Leukemia (AML) is driven by complex interactions between genetic mutations and epigenetic dysregulation. While alterations in chromatin modifiers are frequent, the precise downstream transcriptional networks they enable and how these networks execute the leukemogenic program remain incompletely defined.
METHODS: We employed an integrative bioinformatics strategy. Transcriptomic data from GSE84881 (AML stromal cells) and GSE9476 (AML blasts) identified differentially expressed genes, refined via GeneCards and CellMarker to a 32-gene AML signature. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses followed. Core hubs were validated for spatial (single-cell t-SNE) and subtype-specific expression using the Hematologic Malignancy database. Perturbation analysis (GPSAdb2.0 BioTrigger) expanded the network, with pathway enrichment on responsive genes.
RESULTS: The 32-gene signature enriched strongly in hematopoietic differentiation and unexpectedly in cross-lineage developmental pathways (e.g., gland, epithelial development). PPI topology revealed nine hubs: AFF1, TAL1, IKZF1, GATA1, NOTCH1, BCL2, IL1B, IRF4, ZAP70. Single-cell t-SNE showed distinct, non-overlapping localization patterns among AML subpopulations; box plots demonstrated marked expression heterogeneity across 26 molecular subtypes. Perturbation of these hubs generated a 500-gene set whose KEGG enrichment highlighted three interconnected layers: (i) Polycomb repression and ATP-dependent chromatin remodeling (epigenetic gatekeepers), (ii) FoxO signaling, cell cycle, and senescence (core oncogenic pathways), and (iii) broad cancer hallmarks including endocrine resistance and diverse solid tumor pathways.
CONCLUSION: We propose a hierarchical pathomechanism: synergistic dysfunction in chromatin remodeling and Polycomb-mediated repression establishes a permissive epigenomic landscape, enabling activation of an oncogenic transcriptional network (centered on AFF1, TAL1, IKZF1, GATA1). This network then hijacks TP53/FoxO signaling to drive cell cycle escape, apoptosis resistance, and metabolic adaptation. Our findings unify disparate molecular lesions into a coherent axis and suggest new therapeutic nodes.
METHODS: We employed an integrative bioinformatics strategy. Transcriptomic data from GSE84881 (AML stromal cells) and GSE9476 (AML blasts) identified differentially expressed genes, refined via GeneCards and CellMarker to a 32-gene AML signature. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses followed. Core hubs were validated for spatial (single-cell t-SNE) and subtype-specific expression using the Hematologic Malignancy database. Perturbation analysis (GPSAdb2.0 BioTrigger) expanded the network, with pathway enrichment on responsive genes.
RESULTS: The 32-gene signature enriched strongly in hematopoietic differentiation and unexpectedly in cross-lineage developmental pathways (e.g., gland, epithelial development). PPI topology revealed nine hubs: AFF1, TAL1, IKZF1, GATA1, NOTCH1, BCL2, IL1B, IRF4, ZAP70. Single-cell t-SNE showed distinct, non-overlapping localization patterns among AML subpopulations; box plots demonstrated marked expression heterogeneity across 26 molecular subtypes. Perturbation of these hubs generated a 500-gene set whose KEGG enrichment highlighted three interconnected layers: (i) Polycomb repression and ATP-dependent chromatin remodeling (epigenetic gatekeepers), (ii) FoxO signaling, cell cycle, and senescence (core oncogenic pathways), and (iii) broad cancer hallmarks including endocrine resistance and diverse solid tumor pathways.
CONCLUSION: We propose a hierarchical pathomechanism: synergistic dysfunction in chromatin remodeling and Polycomb-mediated repression establishes a permissive epigenomic landscape, enabling activation of an oncogenic transcriptional network (centered on AFF1, TAL1, IKZF1, GATA1). This network then hijacks TP53/FoxO signaling to drive cell cycle escape, apoptosis resistance, and metabolic adaptation. Our findings unify disparate molecular lesions into a coherent axis and suggest new therapeutic nodes.