Related Experiment Video
Updated: Jan 29, 2026

Modeling Chemotherapy Resistant Leukemia In Vitro
Published on: February 9, 2016
High circ_100053 predicts a poor outcome for chronic myeloid leukemia and is involved in imatinib resistance
Lei Ping1, Chen Jian-Jun2, Liao Chu-Shu1
1Laboratory of Hematology, Hunan Provincial People's Hospital, The First-Affiliated Hospital of Hunan Normal University, Changsha, Hunan Province, China, 410002.
Abstract:
Chronic myeloid leukemia (CML) is a rare clonal myeloproliferative malignancy, which is caused by a reciprocal translocation between chromosomes 9 and 22 t(9;22) (q34;q11). Altered circle RNAs (circRNAs) could contribute to leukemogenesis. In this study, we aimed to investigate the expression profiling of circRNAs in CML. We performed circRNA-sequencing to identify differentially expressed circRNAs in CML cells. Furthermore, we found that circ_100053 was significantly upregulated in peripheral blood mononuclear cells (PBMC) and serum samples from CML compared with healthy controls. CircRNA_100053 expression was associated with clinical stage (p=0.037) and BCR/ABL mutant status (p=0.008). High circ_100053 expression predicted a poor outcome of CML patients and imatinib resistance, suggesting the putative role of circ_100053 as an CML biomarker. Our findings provide insights on circRNAs expression in the development and treatment of CML.
More Related Videos
08:31Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
Published on: October 17, 2025
09:01Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
Related Concept Videos
Predicting Reaction Outcomes
Predicting Molecular Geometry
Outcomes of Glycolysis
Cellular respiration can occur aerobically (with oxygen) or anaerobically (without oxygen). In the presence of oxygen, cellular respiration starts with glycolysis and continues with pyruvate...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Resistivity
Resistance