Prognostic Value of the miR-17~92 Cluster in Chronic Lymphocytic Leukemia.
Sylwia ChocholskaMichał Konrad ZarobkiewiczAgata SzymańskaNatalia LehmanJustyna WośAgnieszka A Bojarska-JunakPublished in: International journal of molecular sciences (2023)
The aim of this study was to investigate the expression of miR-17∼92 cluster members in chronic lymphocytic leukemia (CLL) patients. Six microRNAs (miRNAs)-miR-17, miR-18a, miR-19a, miR-19b-1, miR-20a, and miR-92a-1-very poorly characterized in CLL patients, were chosen for the study to consider their possible role as cancer biomarkers. It is currently unclear to which extent miR-17~92 expression is related to other routinely measured CLL markers, and whether the findings can be of any clinical significance. To achieve this goal, we report the expression levels of these miRNAs detected by RT-qPCR in purified CD19+ B lymphocytes of 107 CLL patients and correlate them with existing clinical data. The study provides new evidence regarding the heterogeneity of miR-17~92 cluster members' expression in CLL patients. Higher miR-17-5p expression was associated with unfavorable prognostic factors (i.e., 17p and 11q deletions, CD38 and ZAP-70 expression). On the other hand, miR-19a, miR-20a, and miR-92a-1 negatively correlated with these adverse factors. The presence of del(13q) as a sole aberration was associated with a significantly lower miR-17-5p as well as higher miR-19a-3p and miR-92a-1-5p expression compared to patients carrying unfavorable genetic aberrations. Particularly, miR-20a could be considered an independent favorable prognostic factor. In a multivariate analysis, high miR-20a expression remained an independent marker predicting long TTT (time to treatment) for CLL patients.
Keyphrases
- prognostic factors
- long non coding rna
- cell proliferation
- poor prognosis
- end stage renal disease
- long noncoding rna
- newly diagnosed
- ejection fraction
- chronic lymphocytic leukemia
- chronic kidney disease
- squamous cell carcinoma
- gene expression
- machine learning
- binding protein
- emergency department
- single cell
- deep learning
- dna methylation
- artificial intelligence
- combination therapy