Literature DB >> 28164500

High BCL11A Expression in Adult Acute Myeloid Leukemia Patients Predicts a Worse Clinical Outcome.

Huijuan Dong, Pengcheng Shi, Yong Zhou, Yong Yu, Xutao Guo, Yao Yao, Pengtao Liu, Bing Xu.   

Abstract

BACKGROUND: Recent reports showed BCL11A may be causatively involved in myeloid leukemia. This study investigated the relationship between BCL11A expression levels and adult acute myeloid leukemia patient characteristics as well as clinical outcomes.
METHODS: RT-PCR was employed to detect BCL11A gene expression levels in 80 patients with acute myeloid leukemia.
RESULTS: Median BCL11A expression levels of 80 AML bone marrow samples were found to be higher than the control group (0.039 vs. 0.014, p < 0.005). Patients with low BCL11A expression levels had a significantly higher CR (complete remission) rate compared with patients with high BCL11A expression levels (90% vs. 53%, p < 0.005). Moreover, the median OS (overall survival) in patients with low BCL11A expression (268 d) was also longer than that in patients with high BCL11A expression (101.5 d) (p < 0.05). No significant difference was observed between the high and low BCL11A groups with respect to white blood cells, haemoglobin, platelet count, French-American-Britain (FAB) subtypes, percentage of blasts in bone marrow, peripheral blood, cytogenetic risk groups, and CD34 expression.
CONCLUSIONS: Adult acute myeloid leukemia had a higher BCL11A expression level. High BCL11A expression level was correlated with lower CR rate and shorter OS, suggesting that BCL11A expression could potentially be used as a prognosis indicator.

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Year:  2017        PMID: 28164500     DOI: 10.7754/Clin.Lab.2016.160614

Source DB:  PubMed          Journal:  Clin Lab        ISSN: 1433-6510            Impact factor:   1.138


  3 in total

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Journal:  NAR Cancer       Date:  2022-09-28

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Authors:  Haley E Ramsey; Kristy Stengel; James C Pino; Gretchen Johnston; Merrida Childress; Agnieszka E Gorska; Pia M Arrate; Londa Fuller; Matthew Villaume; Melissa A Fischer; P Brent Ferrell; Caroline E Roe; Jing Zou; Alexander L R Lubbock; Matthew Stubbs; Sandra Zinkel; Jonathan M Irish; Carlos F Lopez; Scott Hiebert; Michael R Savona
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3.  TopicNet: a framework for measuring transcriptional regulatory network change.

Authors:  Shaoke Lou; Tianxiao Li; Xiangmeng Kong; Jing Zhang; Jason Liu; Donghoon Lee; Mark Gerstein
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

  3 in total

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