Frontiers in genetics · 2025
Transcriptomic signature can distinguish chronic neutrophilic leukemia from ambiguous neutrophilic leukemias.
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Guo Chao, Li Zhen-Ling
Аннотация
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, R2 = 0.5). Using LASSO analysis, the CNL-5 model, with risk scores based on the weighted expression value of five genes (PDCD7/CR2/ZSCAN20/TRIM68/LILRA6) dichotomized patients into CNL-like and Atypical-CNL groups. Compared to the Atypical-CNL group, the CNL-like group demonstrated a clinical phenotype more consistent with CNL and had a significantly higher prevalence of CSF3R mutations (p < 0.05). Additionally, the AUC of the CNL-5 risk model surpassed that of conventional clinical/genetic markers, as validated by the GSE42731 dataset. Poorer survival was revealed in the high-risk group than in the low-risk group defined by the CNL-5 model. GSEA identified CNL-5-associated pathways, such as the inhibition of oxidative phosphorylation and the activation of IL6-JAK-STAT3 signaling.
CONCLUSION: A novel expression signature-based diagnostic assessment for CNL was developed, which showed better diagnostic utility than conventional indicators.
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, R2 = 0.5). Using LASSO analysis, the CNL-5 model, with risk scores based on the weighted expression value of five genes (PDCD7/CR2/ZSCAN20/TRIM68/LILRA6) dichotomized patients into CNL-like and Atypical-CNL groups. Compared to the Atypical-CNL group, the CNL-like group demonstrated a clinical phenotype more consistent with CNL and had a significantly higher prevalence of CSF3R mutations (p < 0.05). Additionally, the AUC of the CNL-5 risk model surpassed that of conventional clinical/genetic markers, as validated by the GSE42731 dataset. Poorer survival was revealed in the high-risk group than in the low-risk group defined by the CNL-5 model. GSEA identified CNL-5-associated pathways, such as the inhibition of oxidative phosphorylation and the activation of IL6-JAK-STAT3 signaling.
CONCLUSION: A novel expression signature-based diagnostic assessment for CNL was developed, which showed better diagnostic utility than conventional indicators.