Transcriptomic and miRNA-Target Pathway Analysis of the DLK1-DIO3 Imprinted microRNA Cluster in Chronic Lymphocytic...

Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell malignancy in which B-cell receptor signaling, microenvironmental interactions, genomic lesions, and epigenetic deregulation cooperate to shape disease behavior. The imprinted DLK1-DIO3 locus at chromosome 14q32 contains the largest human miRNA cluster and has been implicated in cancer-related regulatory networks; however, its contribution to CLL remains incompletely defined.

In the present study, we investigated the potential involvement of DLK1-DIO3 miRNAs in CLL biology by integrating public transcriptomic datasets with miRNA-centered pathway analysis. GSE70830 was used as a discovery dataset and GSE66117 as a supportive validation cohort to identify genes consistently downregulated in CLL compared with normal B cells.

The analyses identified 1236 and 2145 downregulated genes, respectively, and their intersection yielded a 345-gene consensus set. The overlap was significantly greater than expected by chance (odds ratio 2.33; p = 1.55 × 10^-31). This set was used as gene-filter input for DIANA-miRPath v3.0 analysis of the DLK1-DIO3 miRNA cluster, identifying eight KEGG pathways mainly involving B-cell receptor/NF-κB signaling, cell adhesion, leukocyte transendothelial migration, and glycan-related processes. DIANA-miRPath v4.0 provided pathway-centered refinement. GSE216258 miRNA analysis did not show generalized locus-wide upregulation, while a GSE12366 sensitivity analysis showed that 32 of the 345 genes overlapped the strongest naïve-memory B-cell differentiation signatures. The absence of uniform locus-wide upregulation suggests that these data do not establish a direct link between DLK1-DIO3 activation and generalized repression of the 345-gene set. Future studies are needed to identify gene- and pathway-specific effects.

Overall, our findings support an association between DLK1-DIO3 miRNA target/pathway annotations and CLL-relevant transcriptional programs, providing a hypothesis-generating framework that warrants further experimental validation.