miR-582-5p serves as an antioncogenic biomarker in intermediate risk AML with normal cytogenetics and could inhibit proliferation and induce apoptosis of leukemia cells.
Xiaman WangYuandong FengPeihua ZhangHongli ChenJu BaiFangxia WangAili HePublished in: Cell biology international (2020)
Numerous studies confirmed that aberrant microRNA (miRNA) expression contributes to cancer development and progression. We carried out this study to explore the expression profile of miRNAs in intermediate risk acute myeloid leukemia (AML) and locate certain miRNAs as biomarkers. We profiled differentially expressed miRNAs by performing miRNA sequencing analysis in the patients' samples. Bioinformatic analysis showed the most significantly expressed genes mostly involved in cellular component organization, cell differentiation, and cell development. Reverse-transcription polymerase chain reaction validated the expression of miR-582-5p in different groups of AML samples. It was confirmed that miR-582-5p was downregulated in newly diagnosed AML and relapse/refractory AML compared with CR AML or controls. Among intermediate risk AML patients with normal cytogenetics, a lower level of miR-582-5p is correlated with an unfavorable outcome, and a shorter overall survival. Gain- and loss-of-function experiments revealed that miR-582-5p could inhibit proliferation, suppress migration, and invasion ability and induce apoptosis of leukemia cells. Furthermore, overexpression of miR-582-5p can increase sensitivity of cells to Ara-C. In conclusion, miR-582-5p can serve as an antioncogenic biomarker in intermediate risk AML with normal cytogenetics for risk classification and outcome prediction. These results showed a novel role for miR-582-5p in predicting the prognosis and promoting the tumor growth of AML.
Keyphrases
- acute myeloid leukemia
- cell cycle arrest
- allogeneic hematopoietic stem cell transplantation
- newly diagnosed
- induced apoptosis
- cell death
- oxidative stress
- poor prognosis
- single cell
- machine learning
- bone marrow
- deep learning
- gene expression
- squamous cell carcinoma
- stem cells
- young adults
- ejection fraction
- genome wide
- chronic kidney disease
- long non coding rna