Jinghan Wang1,2,3, Jiajia Pan2, Shujuan Huang2, Fenglin Li2, Jiansong Huang2,3, Xia Li2, Qing Ling2, Wenle Ye2, Yungui Wang2,3, Wenjuan Yu1,2,3, Jie Jin4,5,6,7. 1. Department of Hematology, The First Affiliated Hospital, Zhejiang University College of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, People's Republic of China. 2. Key Laboratory of Hematologic Malignancies, Diagnosis and Treatment, Hangzhou, Zhejiang, People's Republic of China. 3. Institute of Hematology, Zhejiang University, Hangzhou, People's Republic of China. 4. Department of Hematology, The First Affiliated Hospital, Zhejiang University College of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, People's Republic of China. jiej0503@zju.edu.cn. 5. Key Laboratory of Hematologic Malignancies, Diagnosis and Treatment, Hangzhou, Zhejiang, People's Republic of China. jiej0503@zju.edu.cn. 6. Institute of Hematology, Zhejiang University, Hangzhou, People's Republic of China. jiej0503@zju.edu.cn. 7. Zhejiang University Cancer Center Zhejiang University , Zhejiang, Hangzhou, People's Republic of China. jiej0503@zju.edu.cn.
Abstract
BACKGROUND: Although there are many clinical and molecular biomarkers in acute myeloid leukemia (AML), the novel and reliable biomarkers are still required to predict the overall survival at the time of disease diagnosis. METHODS: In order to identify independent predictors, we firstly selected 60 cytogenetically normal AML (CN-AML) patients using the propensity score analysis to balance the confounders and performed circular RNA (circRNA) sequencing. Next, one outcome related to circRNA was selected and validated in the independent cohort of 218 CN-AML patients. We then constructed circRNA-miRNA-mRNA regulated network and performed cellular metabolomic analysis to decipher the underlying biological insights. RESULTS: We identified 308 circRNAs as independent candidate predictors of overall survival. Hsa_circ_0075451 expression was validated as an independent predictor with a weak predictive ability for overall survival. The regulated network of this circular RNA indicated 84 hub genes that appear to be regulated by 10 miRNAs sponged by hsa_circ_0075451. The regulatory axis of hsa_circ_0075451 -| miR-330-5p/miR-326 -| PRDM16 was validated by the dual luciferase report assay, fluorescence in situ hybridization, and ShRNA interference assay. CONCLUSIONS: Our data demonstrates that hsa_circ_0075451 expression may independently contribute to the poor prognosis of AML and present a novel therapeutic target.
BACKGROUND: Although there are many clinical and molecular biomarkers in acute myeloid leukemia (AML), the novel and reliable biomarkers are still required to predict the overall survival at the time of disease diagnosis. METHODS: In order to identify independent predictors, we firstly selected 60 cytogenetically normal AML (CN-AML) patients using the propensity score analysis to balance the confounders and performed circular RNA (circRNA) sequencing. Next, one outcome related to circRNA was selected and validated in the independent cohort of 218 CN-AMLpatients. We then constructed circRNA-miRNA-mRNA regulated network and performed cellular metabolomic analysis to decipher the underlying biological insights. RESULTS: We identified 308 circRNAs as independent candidate predictors of overall survival. Hsa_circ_0075451 expression was validated as an independent predictor with a weak predictive ability for overall survival. The regulated network of this circular RNA indicated 84 hub genes that appear to be regulated by 10 miRNAs sponged by hsa_circ_0075451. The regulatory axis of hsa_circ_0075451 -| miR-330-5p/miR-326 -| PRDM16 was validated by the dual luciferase report assay, fluorescence in situ hybridization, and ShRNA interference assay. CONCLUSIONS: Our data demonstrates that hsa_circ_0075451 expression may independently contribute to the poor prognosis of AML and present a novel therapeutic target.
Authors: Simon Raffel; Mattia Falcone; Niclas Kneisel; Jenny Hansson; Wei Wang; Christoph Lutz; Lars Bullinger; Gernot Poschet; Yannic Nonnenmacher; Andrea Barnert; Carsten Bahr; Petra Zeisberger; Adriana Przybylla; Markus Sohn; Martje Tönjes; Ayelet Erez; Lital Adler; Patrizia Jensen; Claudia Scholl; Stefan Fröhling; Sibylle Cocciardi; Patrick Wuchter; Christian Thiede; Anne Flörcken; Jörg Westermann; Gerhard Ehninger; Peter Lichter; Karsten Hiller; Rüdiger Hell; Carl Herrmann; Anthony D Ho; Jeroen Krijgsveld; Bernhard Radlwimmer; Andreas Trumpp Journal: Nature Date: 2017-11-08 Impact factor: 49.962
Authors: Heiko Becker; Guido Marcucci; Kati Maharry; Michael D Radmacher; Krzysztof Mrózek; Dean Margeson; Susan P Whitman; Yue-Zhong Wu; Sebastian Schwind; Peter Paschka; Bayard L Powell; Thomas H Carter; Jonathan E Kolitz; Meir Wetzler; Andrew J Carroll; Maria R Baer; Michael A Caligiuri; Richard A Larson; Clara D Bloomfield Journal: J Clin Oncol Date: 2009-12-21 Impact factor: 44.544
Authors: Filipa G Pinho; Adam E Frampton; Joao Nunes; Jonathan Krell; Heba Alshaker; Jimmy Jacob; Loredana Pellegrino; Laura Roca-Alonso; Alexander de Giorgio; Victoria Harding; Jonathan Waxman; Justin Stebbing; Dmitry Pchejetski; Leandro Castellano Journal: Cancer Res Date: 2013-08-08 Impact factor: 12.701
Authors: Yuanbin Ru; Katerina J Kechris; Boris Tabakoff; Paula Hoffman; Richard A Radcliffe; Russell Bowler; Spencer Mahaffey; Simona Rossi; George A Calin; Lynne Bemis; Dan Theodorescu Journal: Nucleic Acids Res Date: 2014-07-24 Impact factor: 16.971
Authors: Safaa I Tayel; Shimaa E Soliman; Iman A Ahmedy; Mohamed Abdelhafez; Aly M Elkholy; Amira Hegazy; Nashwa M Muharram Journal: Appl Clin Genet Date: 2022-07-16