铁死亡相关基因在预测神经母细胞瘤患者预后价值中的意义

段金江; 刘星宇; 赵旭; 周凯; 沈启阳

doi:10.16766/j.cnki.issn.1674-4152.003913

铁死亡相关基因在预测神经母细胞瘤患者预后价值中的意义

doi: 10.16766/j.cnki.issn.1674-4152.003913

1.
蚌埠医科大学第一附属医院普外四科，安徽蚌埠 233004
2.
南京医科大学附属儿童医院肿瘤病区，江苏南京 210000

基金项目:

安徽省高等学校自然科学研究项目 KJ2021A0740

蚌埠医学院2021年度研究生科研创新计划 Byycx22091

详细信息

通讯作者:
周凯，E-mail：zhoukai0552@163.com

中图分类号: R739.4 R730.43
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出版历程
- 收稿日期: 2024-01-06
- 网络出版日期: 2025-05-14

Comprehensive analysis between ferroptosis-related gene signatures and neuroblastoma prognosis

1.
^*Department of General Surgery, First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China

摘要

摘要: 目的神经母细胞瘤(NB)是中枢神经系统常见的颅外恶性肿瘤之一，它是一种来源于胚胎神经嵴细胞的儿童实体肿瘤，主要影响肾上腺或交感神经节的发育。因其异质性较大，临床表现可从无症状的偶发肿瘤到全身表现的广泛转移。虽然临床稳定的NB患儿可以完全治愈，但高危NB患儿的预后较差，需要联合治疗。鉴于此，探索高危NB患儿的预后生物标志物显得尤为重要。方法从TARGET和GEO数据库中获得原始数据126例和498例基因测序和临床病理原始数据。预测铁死亡相关基因(FRGs)作为NB患者预后生物标志物的作用, 并采用Lasso-Cox回归分析筛选与预后相关的关键FRGs。实验验证敲低神经母细胞瘤的多巴胺受体D4(DRD4)基因，观察肿瘤细胞的生存、迁徙和增殖能力。结果在TARGET队列中使用回归分析得到关键5个FRGs构建预后预测模型。基于5个FRGs计算风险评分, 并根据风险评分的中位值将患者分为低危组和高危组。高危组在免疫细胞中的丰度相对较高。高危组患者免疫肿瘤学指标表达上调。此外，高危组与氧化磷酸化和MTORC1信号有关。敲低DRD4基因，NB的生存细胞数减少、迁徙和增殖能力明显受抑制。结论预后预测模型中纳入的5个FRGs可作为预测NB患者预后的敏感生物标志物，可用于临床免疫治疗和靶向治疗。
- 神经母细胞瘤 /
- 免疫 /
- 铁死亡相关基因 /
- 生物标志物 /
- 预后
Abstract: Objective Neuroblastoma (NB) is a neoplasm that originates from the neural crest cells of the developing embryo. It is one of the most prevalent extracranial malignancies affecting the central nervous system, manifesting as a solid tumor in the pediatric population these tumors typically develop in the adrenal glands or the sympathetic ganglia. The clinical presentation of NB can vary significantly ranging from asymptomatic incidental tumors to widespread metastases accompanied by systemic manifestations. While children diagnosed with clinically stable NB can be successfully treated, those with high-risk NB often have a poor prognosis, even with combination therapy strategies. In order to assess the prognosis of patients with neuroblastoma (NB), there is a need for in-depth study of sensitive biomarkers. Methods Gene sequencing and clinicopathological raw data of 126 and 498 cases were obtained from the TARGET and GEO database. We engaged in a discourse on the role of ferroptosis death-related genes (FRGs) as a prognostic biomarker for NB patients. Lasso-Cox regression analysis was utilized to select FRGs related to prognosis. The experiment involved DRD4 gene knockdown in neuroblastoma, and detected the migration and proliferation ability of tumor cells after DRD4 gene knockdown. Results In the TARGET cohort, a prognostic prediction model was constructed using a selection of five genes. The risk score was calculated based on the five selected FRGs. Patients were divided into low-risk and high-risk groups according to the median risk score. The high-risk group exhibited a higher relative abundance of immune cells, and the expression of immuno-oncology targets was found to be up-regulated in patients with high-risk scores. Furthermore, the high-risk group exhibited a heightened association with oxidative phosphorylation and MTORC1 signaling. Knockdown of DRD4 significantly reduced the number of surviving cells and inhibited the migration and proliferation of NB. Conclusion FRGs have been identified as sensitive biomarkers with the potential to predict the prognosis of patients with NB. These findings underscore the significance of FRGs in the fields of clinical immunotherapy and targeted therapy.
- Neuroblastoma /
- Immune /
- Ferroptosis-related genes /
- Biomarkers /
- Prognosis

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图 1 筛选基因及构建模型

注：A为交叉验证；B为λ系数增大，系数为0的基因被剔除。

Figure 1. Screening genes and constructing models

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图 2 模型的免疫评分分析

注：A为免疫高危组和低危组之间的分布热图。B为Lasso-Cox模型高危组与低危组免疫浸润差异的箱形图，^aP < 0.01，^bP < 0.001，^cP < 0.05。

Figure 2. Immune score analysis of the model

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图 3 各项临床病理因素和风险评分的ROC曲线

Figure 3. ROC curves for each clinicopathological factor and risk score

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图 4 基于5个FRGs构建的NB患者预后预测模型的验证

注：A为5个FRGs的临床相关热图，红色为高表达，蓝色为低表达。B为风险分组和各临床因素的列线图。C为GSE49711预后特征的Kaplan-Meier生存曲线。

Figure 4. Validation of prognostic prediction model for NB patients based on five FRGs

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图 5 DRD4对NB细胞迁移的影响

注：Transwell实验200倍镜下观察NB细胞的迁移能力。

Figure 5. Effect of DRD4 on NB cell migration

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表 1 NB患者的单因素和多因素分析

Table 1. Univariate and multivariate analyses of NB patients

基因	单因素分析		多因素分析
基因	P值	HR(95% CI)	P值	HR(95% CI)
DRD4	< 0.001	1.105(1.060~1.153)	0.005	1.080(1.024~1.139)
MIOX	< 0.001	3.784(1.744~8.214)	0.109	2.264(0.833~6.156)
KEAP1	0.005	1.078(1.023~1.136)	0.401	1.026(0.966~1.090)
TRIB3	0.008	1.099(1.025~1.178)	0.674	1.021(0.928~1.123)
ATG5	0.012	0.806(0.680~0.954)	0.852	0.982(0.809~1.191)
TUBE1	0.028	0.870(0.768~0.985)	0.150	0.885(0.750~1.045)
SLC7A5	0.030	1.017(1.002~1.034)	0.557	1.006(0.987~1.025)
MAPK1	0.032	0.916(0.846~0.992)	0.536	0.969(0.877~1.070)
TFRC	0.045	1.034(1.001~1.069)	0.184	1.028(0.987~1.072)
AURKA	0.050	1.047(1.000~1.097)	0.462	1.022(0.964~1.083)

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表 2 SK-N-BE2和SH-SY5Y细胞qPCR结果(x±s)

Table 2. qPCR results of SK-N-BE2 and SH-SY5Y cells (x±s)

组别	n	SK-N-BE2	SH-SY5
NC	3	1.00±0.15	1.00±0.15
Si-DRD4	3	0.25±0.06	0.31±0.09
t值		14.430	8.023
P值		＜0.001	＜0.001

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表 3 SK-N-BE2细胞CCK8结果(x±s)

Table 3. CCK8 results of SK-N-BE2 cells (x±s)

组别	n	0 h	24 h	48 h	72 h
NC	3	0.35±0.00	0.53±0.01^a	1.57±0.07^ab	2.15±0.09^abc
Si-DRD4	3	0.35±0.01	0.49±0.01^a	0.88±0.60^ab	1.42±0.10^abc
F值		0.843	20.343	168.453	90.976
P值		0.410	0.011	＜0.001	＜0.001
注：F_交互=73.040，P_交互＜0.001；F_组间=243.067，P_组间＜0.001；F_时间=802.857，P_时间＜0.001。与同组0 h比较，^aP < 0.05；与同组24 h比较，^bP < 0.05；与同组48 h比较, ^cP < 0.05。

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表 4 SH-SY5Y细胞CCK8结果(x±s)

Table 4. CCK8 results of SH-SY5Y cells (x±s)

组别	n	0 h	24 h	48 h	72 h
NC	3	0.25±0.00	0.46±0.01^a	1.26±0.05^ab	1.75±0.07^abc
Si-DRD4	3	0.25±0.01	0.38±0.00^a	1.05±0.08^ab	1.55±0.09^abc
F值		< 0.001	261.902	13.173	8.470
P值		0.999	＜0.001	0.022	0.044
注：F_交互=5.036，P_交互=0.012；F_组间=29.241，P_组间＜0.001；F_时间=891.455，P_时间＜0.001。与同组0 h比较，^aP < 0.05；与同组24 h比较，^bP < 0.05；与同组48 h比较, ^cP < 0.05。

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