青岛大学学报（医学版）2023，Vol.59Issue(6)：826-831,6.DOI:10.11712/jms.2096-5532.2023.59.190
AML铜死亡相关lncRNA预后风险模型的构建
CONSTRUCTION OF A PROGNOSTIC RISK MODEL BASED ON CUPROPTOSIS-RELATED LNCRNAS FOR ACUTE MYELOID LEUKEMIA
摘要
Abstract
Objective To establish and validate a prognostic risk model based on cuproptosis-related long non-coding RNAs(lncRNAs)for acute myeloid leukemia(AML)using AML data in The Cancer Genome Atlas database through bioinforma-tic analysis.Methods Cuproptosis-related lncRNAs associated with AML prognosis were determined by co-expression and uni-variable Cox regression analyses.Lasso regression and multivariable Cox regression analyses were performed to identify the optimal cuproptosis-related lncRNAs for constructing the prognostic risk model.Patients with AML were divided into high-and low-risk groups according to the risk model.The prediction model was evaluated by using the calibration curve,C index,receiver operating characteristic(ROC)curve,and decision curve.Results Four optimal cuproptosis-related lncRNAs(LINC01547,LINC02356,NORAD,AC000120.1)associated with the prognosis of patients with AML were obtained.The nomogram model based on the four lncRNAs showed high accuracy when predicting the 1,3,and 5 year outcomes of patients with AML patients.The C index was 0.686.The areas under the ROC curves for 1,3,and 5 year outcome prediction in the training set were 0.758,0.717,and 0.804,respectively;and those in the test set were 0.704,0.682,and 0.927,respectively.In both the training and test sets,the survival rate of the high-risk group was significantly lower than that of the low-risk group.Conclusion The risk model score based on cuproptosis-related lncRNAs was an independent prognostic factor,which could effectively predict the prognosis of patients with AML.关键词
白血病,髓样,急性/铜死亡/RNA,长链非编码/预后/比例危险度模型Key words
leukemia,myeloid,acute/cuproptosis/RNA,long noncoding/prognosis/proportional hazards models分类
医药卫生引用本文复制引用
谢文杰,王智超,郭小芳,管洪在..AML铜死亡相关lncRNA预后风险模型的构建[J].青岛大学学报（医学版）,2023,59(6):826-831,6.基金项目
山东省自然科学基金项目(ZR2020MH311) （ZR2020MH311）
