The Effect of Atorvastatin on Oncogenic miRNAs in Hematological Malignancies: A Central Study

The efficacy of statins as anti-cancer drugs has been demonstrated in several malignancies but has been poorly investigated in hematological malignancies (HM). By studying its effect on oncogenic miRNAs, we investigated the effect of statin therapy on HM patients. The data were used to identify enriched pathways that were altered due to statin treatment. The main aim of this study was to identify significantly differentially expressed miRNAs and involved regulatory pathways post-atorvastatin treatment in HM patients. A panel of 95 plasma circulating miRNAs involved in tumorigenesis, apoptosis, and differentiation were relatively quantified using qPCR for blood samples obtained from 12 HM patients, 4 with Chronic Myeloid Leukemia (CML), 4 with Non-Hodgkin Lymphoma (NHL), and 4 with Essential Thrombocythemia. Pre- and post-administration of a 6-week atorvastatin course miRNA expression levels were measured. Significantly differentially expressed miRNAs were those with a fold change >2 or <0.5 using the Livak method with a two-sided <i>p</i>-value < 0.05. To further understand the underlying mechanism of statin regulated miRNA, GO and KEGG pathway enrichment analyses were conducted for identified target genes using the DAVID 6.8 bioinformatics tool. Out of 95 miRNAs, 14 exhibited significant fold changes post-treatment with statins including miR-198, miR-29a+b+c, miR-204, miR-222, miR-224, miR-155, miR-128b, miR-296, miR-199a+b, miR-194, miR-125a, miR-200a, and the let-7-family that were upregulated and miR-150 that was downregulated post-statin treatment. Higher mir-222, mir-194, mir-128b, and mir-199b expressions were significantly associated with better overall survival using the Cancer Genomic Atlas leukemia and lymphoma patient cohorts. Enrichment analysis identified the PI3k-Akt pathway as well as other pathways involved in the epithelial-mesenchymal transition. Atorvastatin upregulated several tumor suppressor genes involved in mediating better prognosis. The data can be used to enhance personalized treatments for patients with hematological malignancies by helping to predict the different pathways that may be targeted and, therefore, result in better survival outcomes in these patients.