| Literature DB >> 31969992 |
Hua Jiang1, Zhiying Ou1, Yingyi He1, Meixing Yu1, Shaoqing Wu1, Gen Li1, Jie Zhu1, Ru Zhang1, Jiayi Wang1, Lianghong Zheng2, Xiaohong Zhang1, Wenge Hao1, Liya He1, Xiaoqiong Gu1, Qingli Quan1, Edward Zhang1, Huiyan Luo3, Wei Wei3, Zhihuan Li2, Guangxi Zang2, Charlotte Zhang1, Tina Poon1, Daniel Zhang1, Ian Ziyar2, Run-Ze Zhang2, Oulan Li2, Linhai Cheng2, Taylor Shimizu2, Xinping Cui4, Jian-Kang Zhu5, Xin Sun1, Kang Zhang1,2,6.
Abstract
The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis, treatment selection, and prognosis prediction of patients. Using genome-wide methylation profiling and machine learning methods, we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia (ALL) or acute myelogenous leukemia (AML) from normal blood. We established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and specificity. We then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups, with significant differences in clinical outcome in each leukemia type. Together, these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML, with implications for the prediction of prognosis and treatment selection.Keywords: Haematological cancer; Prognostic markers
Year: 2020 PMID： 31969992 PMCID： PMC6959291 DOI： 10.1038/s41392-019-0090-5
Source DB: PubMed Journal: Signal Transduct Target Ther ISSN： 2059-3635