High Expression of AHSP, EPB42, GYPC and HEMGN Predicts Favorable Prognosis in FLT3-ITD-Negative Acute Myeloid Leukemia
High Expression of AHSP, EPB42, GYPC and HEMGN Predicts Favorable Prognosis in FLT3-ITD-Negative Acute Myeloid Leukemia
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AHSP、EPB42、GYPC 和 HEMGN 的高表达预示着 FLT3-ITD 阴性急性髓系白血病的良好预后
DOI:
10.1159/000479837
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发表时间:
2017-08
影响因子:
--
通讯作者:
Chen Xiao Ping
中科院分区:
文献类型:
--
作者:
Zhu Gang Zhi;Yang Yong Long;Zhang Yan Jiao;Liu Wei;Li Mu Peng;Zeng Wen Jing;Zhao Xie Lan;Chen Xiao Ping
Background/Aims: Acute myeloid leukemia (AML) is a heterogeneous clonal disease and patients with AML who harbor an FMS-like tyrosine kinase 3 (FLT3) mutation present several dilemmas for the clinician. This study aims to identify novel targets for explaining the dilemmas. Methods: We analyzed four microarray gene expression profiles to investigate changes in whole genome expression associated with FLT3-ITD mutation. Results: We identified 22 differentially expressed genes which are commonly expressed among all four profiles. Kaplan-Meier analysis of the dataset GSE12417 revealed that low expression of AHSP, EPB42, GYPC and HEMGN predicted poor prognosis (AHSP: P=0.0317, HR=1.894; EPB42: P=0.0382, HR=1.859; GYPC: P=0.0015, HR=2.051; HEMGN: P=0.0418, HR=1.838 in GSE12417 test cohort; AHSP: P=0.0279, HR=1.548; EPB42: P=0.0398, HR=1.505; GYPC: P=0.0408, HR=1.501; HEMGN: P=0.0143, HR=1.630 in GSE12417 validation cohort). When patients were FLT3-ITD positive, the expression of FLT3 was significantly increased (all P<0.05 in four profiles), and correleation analysis of four profiles revealed that the expression of the four candidate genes negatively correlated with FLT3 expression. Conclusions: Our findings suggest that AHSP, EPB42, GYPC and HEMGN may be suitable biomarkers for diagnostic or therapeutic strategies for FLT3-ITD-positive AML patients.
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影响因子:
3
作者:
Favero ME;Costa FF
通讯作者:
Costa FF
影响因子:
--
作者:
Rong Zhang;Wei Yang;Ying Li;G. Zhang;Kun Yao;R. Hu;Bin Wu
通讯作者:
Rong Zhang;Wei Yang;Ying Li;G. Zhang;Kun Yao;R. Hu;Bin Wu
DOI:
10.1007/978-3-030-73227-1_13
发表时间:
2021
期刊:
Practical Oncologic Molecular Pathology
影响因子:
--
作者:
Guang Yang;Linsheng Zhang
通讯作者:
Guang Yang;Linsheng Zhang
DOI:
10.1016/b978-0-12-814482-4.00006-1
发表时间:
2020
期刊:
Computational Learning Approaches to Data Analytics in Biomedical Applications
影响因子:
--
作者:
Khalid Al-Jabery;Tayo Obafemi-Ajayi;G. Olbricht;D. Wunsch
通讯作者:
Khalid Al-Jabery;Tayo Obafemi-Ajayi;G. Olbricht;D. Wunsch
影响因子:
5.3
作者:
A. M. Pilon;Douglas G. Nilson;Dewang Zhou;J. Sangerman;T. Townes;D. Bodine;P. Gallagher
通讯作者:
A. M. Pilon;Douglas G. Nilson;Dewang Zhou;J. Sangerman;T. Townes;D. Bodine;P. Gallagher