Correlation between mRNA expression levels related to iron death and prognosis in patients with acute myeloid leukemia and analysis of decision curve
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摘要:
目的 耐药性和复发是急性髓系白血病(AML)患者预后不佳的主要原因，除遗传、表观遗传和蛋白质组学的改变导致恶性克隆存在的抗死亡作用外，铁死亡也参与细胞的多种生物过程，本研究探讨铁死亡相关mRNA表达水平与急性髓系白血病(AML)患者预后的相关性。 方法 选取2020年1月—2021年3月华北石油管理局总医院收治的AML患者88例为研究对象，根据24个月预后情况分为预后良好组和预后不良组。比较2组铁死亡相关mRNA表达水平，采用LASSO-logistic回归分析筛选与AML预后高度相关的铁死亡相关mRNA，绘制决策曲线对比分析ELN风险分类和铁死亡相关mRNA表达水平对AML患者的预后价值。 结果 随访观察24个月，35例患者出现死亡、复发、疾病进展终点事件定义为预后不良组，其余53例为预后良好组。LASSO-logistic回归分析构建了PHKG2、STEAP3、ARNTL和DPP4共4个mRNA标志物的预后模型；ELN风险分类评价49例预后良好，其余39例为预后中等及预后不良。决策曲线显示，在阈值范围内0~1.0，PHKG2、STEAP3、ARNTL和DPP4 mRNA预测模型预测AML患者预后的净收益率较ELN风险分类高。 结论 铁死亡相关mRNA表达水平与AML患者预后有关，基于决策曲线分析铁死亡相关mRNA表达水平对AML患者预后的预测具有一定的价值。 Abstract:Objective Drug resistance and disease recurrence are primary factors contributing to poor prognosis in patients with acute myeloid leukemia (AML). In addition to the anti-death effects of malignant clones caused by genetic, epigenetic, and proteomic changes, iron death also plays a role in various cellular biological processes. The purpose of this study is to investigate the correlation between iron death related mRNA expression and prognosis in patients with AML. Methods A total of 88 AML patients admitted to the General Hospital of North China Petroleum Administration between January 2020 and March 2021 were included in this study. The patients were divided into two groups based on their prognosis at 24 months: a good prognosis group and a poor prognosis group. The expression levels of mRNA related to iron-death were compared between the two groups. LASSO-logistic regression analysis was used to identify iron-death related mRNA highly correlated with AML prognosis. Furthermore, a decision curve was drawn to compare the prognostic value of ELN risk classification with that of iron-death related mRNA expression levels in AML patients. Results After 24-month follow-up, 35 patients experienced death, recurrence, and disease progression were categorized as the poor prognosis group, while the remaining 53 patients were classed as the good prognosis group. Prognostic models were constructed using LASS-logistic regression analysis for the mRNA markers PHKG2, STEAP3, ARNTL, and DPP4. According to the ELN risk classification, 49 cases had a good prognosis, while the remaining 39 cases had a moderate or poor prognosis. The decision curve showed that the predictive models based on PHKG2, STEAP3, ARNTL, and DPP4 mRNA markers exhibited a higher net benefit rate for predicting AML outcomes compared to the ELN risk classification within the threshold range of 0-1.0. Conclusion The expression level of iron-death related mRNA is related to the prognosis of AML patients, and analyzing this expression level using a decision curve holds significant value in predicting AML prognosis. -
Key words:
- Acute myeloid leukemia /
- Ferroptosis /
- Prognosis /
- Decision curve
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图 1 LASSO回归模型中通筛选最合适λ的过程
Figure 1. Process of selecting the most suitable λ in LASSO regression model
图 3 铁死亡相关mRNA表达水平与AML患者预后的决策曲线
Figure 3. Decision curve of mRNA expression level related to iron death ferroptosis and prognosis of AML patients
表 1 2组AML患者一般资料比较
Table 1. Comparison of general data of AML patients between the two groups
组别 例数 年龄
(x ± s，岁)性别
(男性/女性，例)髓外病变
[例(%)]骨髓原始细胞比率(x ± s，%) 血红蛋白
(x ± s，g/L)白细胞计数
(x ± s，109/L)血小板计数
(x ± s，109/L)预后良好组 53 46.26±5.25 32/21 14(26.42) 50.26±10.36 76.26±6.38 40.23±9.36 22.65±4.23 预后不良组 35 46.01±6.03 24/11 10(28.57) 51.37±9.64 77.02±6.49 41.02±10.67 21.98±4.52 统计量 0.200a 0.612b 0.049b 0.513a 0.541a 0.357a 0.598a P值 0.842 0.434 0.824 0.609 0.590 0.722 0.488 注：a为t值，b为χ2值。 
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表 2 2组AML患者铁死亡相关mRNA表达水平比较(x ± s)
Table 2. Comparison of mRNA expression levels related to iron death ferroptosis in AML patients between the two groups(x ± s)
mRNA 预后良好组
(n=53)预后不良组
(n=35)t值 P值 PHKG2 6.22±1.40 4.28±1.19 6.971 < 0.001 HSD17B11 5.69±1.35 4.11±1.05 6.155 < 0.001 STEAP3 6.40±1.43 4.46±1.57 5.786 < 0.001 HRAS 6.58±1.41 8.01±1.26 4.967 < 0.001 ARNTL 8.17±1.74 6.42±1.77 4.570 < 0.001 CXCL2 6.56±1.42 7.98±1.38 4.670 < 0.001 SLC38A1 5.16±1.35 7.26±1.35 7.142 < 0.001 PGD 4.26±1.11 5.23±1.41 3.428 0.001 ENPP2 5.24±1.03 6.85±1.29 6.194 < 0.001 ACSL3 1.67±0.11 1.91±0.14 8.548 < 0.001 DDIT4 5.32±1.32 6.55±1.09 4.758 < 0.001 PSAT1 6.12±1.35 7.05±1.44 3.039 0.003 CHAC1 5.26±1.34 7.26±1.65 5.985 < 0.001 CISD1 3.26±1.02 4.23±1.02 4.366 < 0.001 DPP4 3.58±0.81 4.57±0.94 5.104 < 0.001 GPX4 8.16±2.01 10.15±2.06 4.478 < 0.001 AIFM2 6.33±1.27 7.64±1.65 3.982 < 0.001 SQLE 4.26±1.02 4.97±1.04 3.158 0.002 ACSF2 6.26±1.35 7.65±1.42 4.583 < 0.001
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表 3 AML患者预后影响因素的logistic回归分析各变量赋值
Table 3. Logistic regression values of prognostic factors in AML patients
变量 赋值方法 PHKG2 连续变量，以实际值赋值 STEAP3 连续变量，以实际值赋值 ARNTL 连续变量，以实际值赋值 DPP4 连续变量，以实际值赋值 预后 预后不良=1，预后良好=0
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表 4 AML患者预后影响因素的logistic回归模型分析
Table 4. Logistic regression model analysis of prognostic factors in AML patients
变量 B SE Waldχ2 P值 OR值 95% CI PHKG2 -1.356 0.409 11.005 0.001 0.258 0.116~0.574 STEAP3 -1.305 0.441 8.778 0.003 0.271 0.114~0.643 ARNTL -0.658 0.290 5.143 0.023 0.518 0.293~0.914 DPP4 2.328 0.770 9.134 0.003 10.252 2.266~46.384
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