Identification of a Novel miR-122-5p/CDC25A Axis and Potential Therapeutic Targets for Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by uncontrolled proliferation of myeloid cells. MicroRNAs (miRNAs), small noncoding RNAs, regulate post-transcriptional gene expression by degrading target mRNAs or repressing translation. Dysregulated miRNA expression has been implicated in various malignancies, including CML, where they can function as oncogenes or tumor suppressors. This study aimed to investigate the relationship between miR-122-5p and cell division cycle 25A (CDC25A) in CML and to elucidate the regulatory mechanisms of miR-122-5p. This study integrates bioinformatics analysis with in vitro RT-qPCR validation in K562 chronic myeloid leukemia cells to explore the potential regulatory relationship between miR-122-5p and CDC25A. mRNA expression profiles were retrieved from the GSE100026 dataset in the Gene Expression Omnibus (GEO), and differentially expressed genes were identified using GEO2R. Quantitative real-time PCR (RT-qPCR) was performed to measure miR-122-5p, CDC25A, and cyclin-dependent kinase 4 (CDK4) expression levels. Bioinformatics analyses (miRNeT, miRDIP, TargetScan, BioGPS, GeneMANIA, STRING) were applied to predict molecular interactions and functional pathways. Public RNA-seq datasets and in silico tools were used to prioritize candidates; RT-qPCR in a single CML cell line (K562) provided in vitro expression validation. In K562 cells, miR-122-5p expression was significantly reduced, while CDC25A and CDK4 were markedly upregulated. Bioinformatics tools confirmed CDC25A as a potential miR-122-5p target. Functional enrichment indicated CDC25A involvement in cell cycle regulation and apoptosis. These findings suggest that miR-122-5p functions as a tumor suppressor in CML by targeting CDC25A. Modulating the miR-122-5p/CDC25A axis may provide potential molecular targets for inhibiting CML progression through regulation of cell cycle pathways. Findings are exploratory and based on bioinformatics with limited in vitro expression confirmation; functional studies are required to establish causality.