ArticleFrontiers in genetics2025
Transcriptomic signature can distinguish chronic neutrophilic leukemia from ambiguous neutrophilic leukemias.
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Identifying uncommon neutrophilic leukemias presents a challenging task, owing to the analogous morphological characteristics and the dearth of molecular markers. The transcriptomic profile of bone marrow cells in this disease subset has been rarely explored. Material and Methods: The OHSU-CNL dataset, encompassing clinical parameters and parallel transcriptomic matrix, was downloaded from the Genomic Data Commons (GDC) database. Distinctive co-expressed gene modules and pivotal genes for chronic neutrophilic leukemia (CNL) were identified using R software. Subsequently, a diagnostic model for CNL denoted as CNL-5 was formulated employing least absolute shrinkage and selection operator (LASSO) regression analysis. The diagnostic power of the CNL-5 model was compared with conventional clinical/genetic markers via multi-ROC analysis. The divergence in overall survival between CNL-5 risk groups was delineated by Kaplan-Meier analysis, and the predictive power (AUC and Harrison's C index) was determined by time-dependent ROC. Cell signaling pathways associated with CNL-5 risk were identified by genomic set enrichment analysis (GSEA). Results: Neither clinical indicators nor genetic markers were sufficient to classify neutrophilic leukemias. Through weighted gene co-expression network analysis (WGCNA), the brown module was discerned to be CNL-specific (p = 8e-16, R Conclusion: A novel expression signature-based diagnostic assessment for CNL was developed, which showed better diagnostic utility than conventional indicators.
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