源于TARGET、ArrayExpress和GEO数据库构建神经母细胞瘤中铜死亡相关预后模型

向彬; 陈美玲; 曹礼剑; 何雪予; 田小毛; 刘丰; 魏光辉

doi:10.16016/j.2097-0927.202207150

您当前的位置：

首页 >

文章列表页 >

源于TARGET、ArrayExpress和GEO数据库构建神经母细胞瘤中铜死亡相关预后模型

临床医学 | 更新时间：2026-01-09

- 源于TARGET、ArrayExpress和GEO数据库构建神经母细胞瘤中铜死亡相关预后模型
- Construction and validation of acuproptosis-related prognostic model in neuroblastoma based on TARGET, ArrayExpress and GEO databases
- 陆军军医大学学报 2023年45卷第4期页码：307-317
- 作者机构：
  
  400014 重庆，重庆医科大学附属儿童医院泌尿外科，儿童泌尿生殖发育与组织工程重点实验室，国家儿童健康疾病临床医学研究中心
- 作者简介：
  
  [ 田小毛，Email：doctortxm@stu.cqmu.edu.cn TIAN Xiaomao, E-mail: doctortxm@stu.cqmu.edu.cn
  [ 刘丰，Email：liufeng@hospital.cqmu.edu.cn LIU Feng, E-mail: liufeng@hospital.cqmu.edu.cn
- 基金信息：
  
  重庆市自然科学基金项目(cstc2021jcyj-msxmX0345);重庆医科大学附属儿童医院临床医学研究一般项目(NCRC-2019-GP-08)
- DOI：10.16016/j.2097-0927.202207150
  中图分类号： R319;R730.7;R739.4
- 收稿：2022-07-22，
  
  修回：2022-09-15，
  
  纸质出版：2023-02-28
- 稿件说明：
移动端阅览
向彬, 陈美玲, 曹礼剑, 等. 源于TARGET、ArrayExpress和GEO数据库构建神经母细胞瘤中铜死亡相关预后模型[J]. 陆军军医大学学报, 2023,45(4):307-317.

Bin XIANG, Meiling CHEN, Lijian CAO, et al. Construction and validation of acuproptosis-related prognostic model in neuroblastoma based on TARGET, ArrayExpress and GEO databases[J]. Journal of Army Medical University, 2023, 45(4): 307-317.
向彬, 陈美玲, 曹礼剑, 等. 源于TARGET、ArrayExpress和GEO数据库构建神经母细胞瘤中铜死亡相关预后模型[J]. 陆军军医大学学报, 2023,45(4):307-317. DOI： 10.16016/j.2097-0927.202207150.

Bin XIANG, Meiling CHEN, Lijian CAO, et al. Construction and validation of acuproptosis-related prognostic model in neuroblastoma based on TARGET, ArrayExpress and GEO databases[J]. Journal of Army Medical University, 2023, 45(4): 307-317. DOI： 10.16016/j.2097-0927.202207150.

摘要

目的

基于铜死亡相关基因(cuproptosis-related genes，CRGs)构建神经母细胞瘤(neuroblastoma，NB)预后模型，以改善神经母细胞瘤患者的个体化管理。

方法

从公开的数据库中收集具有相关完整临床信息的铜死亡基因表达数据。获得来自基因表达综合数据库(gene expression omnibus，GEO)的GSE49711队列、来自开放有效治疗方法的治疗性研究数据库(therapeutically applicable research to generate effective treatments，TARGET)的TARGET-NB队列和来自功能基因组学公共存储库ArrayExpress数据库的E-MTAB-8248队列。排除随访信息缺失的患者，最终纳入968例进行分析。选择GSE49711队列作为“训练集”构建预后模型，其余两个数据集作为“验证集”。使用Log-rank检验筛选有预后意义的变量，再使用LASSO-Cox回归分析建立最佳多基因预后模型。采用ROC曲线、列线图、校准曲线和DCA曲线评估预后模型的准确性。使用RT-qPCR验证风险基因在神经母细胞系中的表达水平，并选择关键风险基因

PDHA

1进行功能分析。

结果

通过训练集首次建立了预后模型[风险评分公式为(1.573)×

PDHA

1+(-0.561)×

GLS

+(0.320)×

LIAS

+(0.088)×

MTF

1+(0.301)×

PDHB

]

。根据风险评分中位值将患者分为高风险组和低风险组。生存分析显示高风险组中的NB患者的生存率明显低于低风险组(

0.001)。时间依赖性ROC曲线预测3、5、7年生存率的曲线下面积(area under curve，AUC)分别为0.80、0.80和0.81。生存分析表明，在TARGET-NB和E-MTAB-8248队列中，与低风险组比较，高风险组患者的预后患者更差(

=0.011，

=0.008 7)。列线图的校准曲线和DCA曲线(C指数为0.736)显示列线图具有良好的临床价值。RT-qPCR和功能丧失实验验证了风险模型中基因的表达水平和关键基因

PDHA

1的功能。

结论

基于铜死亡相关基因构建了预测神经母细胞瘤患者生存率的预后模型，并在两个外部数据集中验证了该模型的准确性。

Abstract

Objective

To construct a prognostic model for neuroblastoma (NB) based on cuproptosis-related genes (CRGs) and improve individualized management of neuroblastoma patients.

Methods

CRGs expression data with complete clinical information were collected from publicly available databases. A total of 3 independent datasets were obtained

including the GSE49711 cohort from Gene Expression Omnibus (GEO) database

the TARGET-NB cohort from Therapeutically Applicable Research to Generate Effective Treatmen

ts(TARGET) database

and the E-MTAB-8248 cohort from ArrayExpress database. 968 patients were finally included for follow-up data analysis after excluding patients with incomplete follow-up information. The GSE49711 cohort was selected as the training set to construct the prognositic model

and the other two data sets were used as the validation set to verify the accuracy. Log-rank tests were used to screen prognostically significant variables

and the best multi-gene prognostic model was contructed using LASSO-Cox regression. ROC curves

column plots

calibration curves

and DCA curves were used to assess the accuracy of the prognostic models. RT-qPCR was used to validate the expression levels of risk genes in NB cell lines

and the key risk gene

PDHA

1 was selected for functional analysis.

Results

A prognostic model was first constructed in the training cohort with a risk score formula of (1.573)×

PDHA

1+(-0.561)×

GLS

+(0.320)×

LIAS

+(0.088)×

MTF

1+(0.301)×

PDHB

. According to the risk scores calculated based on the formula

patients were classified into the high- and low-risk subgroups based on the median values. Survival analysis showed that NB patients in the high-risk subgroup had a significantly lower survival rate than that in the low-risk subgroup (

0.001). The time-dependent ROC curve predicted the area under curve (AUC) of 3-year

5-year and 7-year survival rate was 0.80

0.80

and 0.81

respectively. Survival analysis showed that in the TARGET-NB and E-MTAB-8248 cohorts

the high-risk subgroup was associated with a worse prognosis compared with the low-risk subgroup(

=0.011

=0.008 7). The calibration curve and DCA curve (C-index: 0.736) of nomagram showed the good clinical value of nomagram. The expression levels of genes in the risk model and the function of the key gene

PDHA

1 were verified by RT-qPCR and

loss-of-function experiments.

Conclusion

A prognostic model to predict the survival rate of patients with neuroblastoma is constructed based on cuproptosis-related genes and validated in two external datasets.

关键词

Keywords

references

NEWMAN E A Jr. Update on neuroblastoma[J]. J Pediatr Surg, 2019, 54(3): 383-389. DOI: 10.1016/j.jpedsurg.2018.09.004.

SIEGEL D A, KING J, TAI E, et al . Cancer incidence rates and trends among children and adolescents in the United States, 2001-2009[J ] . Pediatrics, 2014, 134(4): e945-e955. DOI: 10.1542/peds.2013-3926.

PINTO N R, APPLEBAUM M A, VOLCHENBOUM S L, et al . Advances in risk classification and treatment strategies for neuroblastoma[J ] . J Clin Oncol, 2015, 33(27): 3008-3017. DOI: 10.1200/jco.2014.59.4648.

WHITTLE S B, SMITH V, DOHERTY E, et al . Overview and recent advances in the treatment of neuroblastoma[J ] . Expert Rev Anticancer Ther, 2017, 17(4): 369-386. DOI: 10.1080/14737140.2017.1285230.

BERLANGA P, CAÑETE A, CASTEL V. Advances in emerging drugs for the treatment of neuroblastoma[J]. Expert Opin Emerg Drugs, 2017, 22(1): 63-75. DOI: 10.1080/14728214.2017.1294159.

DE BERNARDI B, NICOLAS B, BONI L C, et al . Disseminated neuroblastoma in children older than one year at diagnosis: comparable results with three consecutive high-dose protocols adopted by the Italian Co-Operative Group for Neuroblastoma[J ] . J Clin Oncol, 2003, 21(8): 1592-1601. DOI: 10.1200/JCO.2003.05.191.

BERTHOLD F, FALDUM A, ERNST A, et al . Extended induction chemotherapy does not improve the outcome for high-risk neuroblastoma patients: results of the randomized open-label GPOH trial NB2004-HR[J ] . Ann Oncol, 2020, 31(3): 422-429. DOI: 10.1016/j.annonc.2019.11.011.

MATTHAY K K, VILLABLANCA J G, SEEGER R C, et al . Treatment of high-risk neuroblastoma with intensive chemotherapy, radiotherapy, autologous bone marrow transplantation, and 13- Cis -retinoic acid. Children's Cancer Group[J ] . N Engl J Med, 1999, 341(16): 1165-1173. DOI: 10.1056/NEJM199910143411601..

TSVETKOV P, COY S, PETROVA B, et al . Copper induces cell death by targeting lipoylated TCA cycle proteins[J ] . Science, 2022, 375(6586): 1254-1261. DOI: 10.1126/science.abf0529.

ENGEBRETSEN S, BOHLIN J. Statistical predictions with glmnet[J]. Clin Epigenetics, 2019, 11(1): 123. DOI: 10.1186/s13148-019-0730-1.

MARIS J M, HOGARTY M D, BAGATELL R, et al . Neuroblastoma[J ] . Lancet, 2007, 369(9579): 2106-2120. DOI: 10.1016/s0140-6736(07)60983-0.

COHN S L, PEARSON A D, LONDON W B, et al . The International Neuroblastoma Risk Group (INRG) classification system: an INRG task force report[J ] . J Clin Oncol, 2009, 27(2): 289-297. DOI: 10.1200/jco.2008.16.6785.

MONCLAIR T, BRODEUR G M, AMBROS P F, et al . The International Neuroblastoma Risk Group (INRG) staging system: an INRG Task Force report[J ] . J Clin Oncol, 2009, 27(2): 298-303. DOI: 10.1200/jco.2008.16.6876.

MARIS J M. Recent advances in neuroblastoma[J]. N Engl J Med, 2010, 362(23): 2202-2211. DOI: 10.1056/nejmra0804577.

MATIAS A C, et al . Diethyldithiocarbamate induces apoptosis in neuroblastoma cells by raising the intracellular copper level, triggering cytochrome c release and caspase activation[J ] . Toxicol In Vitro, 2013, 27(1): 349-35 7. DOI: 10.1016/j.tiv.2012.08.017.

LOMBARDO M F, CIRIOLO M R, ROTILIO G, et al . Prolonged copper depletion induces expression of antioxidants and triggers apoptosis in SH-SY 5 Y neuroblastoma cells[J ] . Cell Mol Life Sci, 2003, 60(8): 1733-1743. DOI: 10.1007/s00018-003-3153-1..

FILOMENI G, CARDACI S, DA COSTA FERREIRA A M, et al . Metabolic oxidative stress elicited by the copper(Ⅱ) complex[Cu(isaepy) 2 ] triggers apoptosis in SH-SY 5 Y cells through the induction of the AMP-activated protein kinase/p38MAPK/p53 signalling axis: evidence for a combined use with 3-bromopyruvate in neuroblastoma treatment[J ] . Biochem J, 2011, 437(3): 443-453. DOI: 10.1042/BJ20110510..

ARCIELLO M, ROTILIO G, ROSSI L. Copper-dependent toxicity in SH-SY 5 Y neuroblastoma cells involves mitochondrial damage[J ] . Biochem Biophys Res Commun, 2005, 327(2): 454-459. DOI: 10.1016/j.bbrc.2004.12.022.

LI S R, BU L L, CAI L L. Cuproptosis: lipoylated TCA cycle proteins-mediated novel cell death pathway[J]. Signal Transduct Target Ther, 2022, 7(1): 158. DOI: 10.1038/s41392-022-01014-x.

CAI Y, HE Q C, LIU W, et al . Comprehensive analysis of the potential cuproptosis-related biomarker LIAS that regulates prognosis and immunotherapy of pan-cancers[J ] . Front Oncol, 2022, 12: 952129. DOI: 10.3389/fonc.2022.952129.

ZHU Y Z, WU G, YAN W F, et al . miR-146b-5p regulates cell growth, invasion, and metabolism by targeting PDHB in colorectal cancer[J ] . Am J Cancer Res, 2017, 7(5): 1136-1150. PMID: 28560062

XU D X, GUO J J, ZHU G Y, et al . miR-363-3p modulates cell growth and invasion in glioma by directly targeting pyruvate dehydrogenase B[J ] . Eur Rev Med Pharmacol Sci, 2018, 22(16): 5230-5239. DOI: 10.26355/eurrev_201808_15721.

WANG G Q, YE Q W, NING S J, et al . LncRNA MEG3 promotes endoplasmic Reticulum stress and suppresses proliferation and invasion of colorectal carcinoma cells through the MEG3/miR-103a-3p/PDHB CeRNA pathway[J ] . Neoplasma, 2021, 68(2): 362-374. DOI: 10.4149/neo_2020_200813N858..

MASISI B K, EL ANSARI R, ALFARSI L, et al . The role of glutaminase in cancer[J ] . Histopathology, 2020, 76(4): 498-508. DOI: 10.1111/his.14014.

JI L, ZHAO G N, ZHANG P, et al . Knockout of MTF 1 inhibits the epithelial to mesenchymal transition in ovarian cancer cells[J ] . J Cancer, 2018, 9(24): 4578-4585. DOI: 10.7150/jca.28040..

LI D, WANG C Q, MA P F, et al . PGC1α promotes cholangiocarcinoma metastasis by upregulating PDHA1 and MPC1 expression to reverse the Warburg effect[J ] . Cell Death Dis, 2018, 9(5): 466. DOI: 10.1038/s41419-018-0494-0.

CHEN J J, GUCCINI I, MITRI D D, et al . Compartmentalized activities of the pyruvate dehydrogenase complex sustain lipogenesis in prostate cancer[J ] . Nat Genet, 2018, 50(2): 219-228. DOI: 10.1038/s41588-017-0026-3.

ZHUANG L W, ZHANG B B, LIU X M, et al . Exosomal miR-21-5p derived from cisplatin-resistant SKOV 3 ovarian cancer cells promotes glycolysis and inhibits chemosensitivity of its progenitor SKOV 3 cells by targeting PDHA1[J ] . Cell Biol Int, 2021, 45(10): 2140-2149. DOI: 10.1002/cbin.11671..

SUN J H, LI J J, GUO Z X, et al . Overexpression of pyruvate dehydrogenase E1α subunit inhibits Warburg effect and induces cell apoptosis through mitochondria-mediated pathway in hepatocellular carcinoma[J ] . Oncol Res, 2 019, 27(4): 407-414. DOI: 10.3727/096504018X15180451872087.

LIU Z, YU M, FEI B Y, et al . miR-21-5p targets PDHA1 to regulate glycolysis and cancer progression in gastric cancer[J ] . Oncol Rep, 2018, 40(5): 2955-2963. DOI: 10.3892/or.2018.6695.

LIU L, CAO J, ZHAO J, et al . PDHA 1 gene knockout in human esophageal squamous cancer cells resulted in greater Warburg effect and aggressive features in vitro and in vivo [J ] . Onco Targets Ther, 2019, 12: 9899-9913. DOI: 10.2147/OTT.S226851..

KWAK C H, JIN L, HAN J H, et al . Ilimaquinone induces the apoptotic cell death of cancer cells by reducing pyruvate dehydrogenase kinase 1 activity[J ] . Int J Mol Sci, 2020, 21(17): 6021. DOI: 10.3390/ijms21176021.

DENG L M, JIANG A Q, ZENG H Q, et al . Comprehensive analyses of PDHA1 that serves as a predictive biomarker for immunotherapy response in cancer[J ] . Front Pharmacol, 2022, 13: 947372. DOI: 10.3389/fphar.2022.947372.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

多发性骨髓瘤患者基于铜死亡相关基因预后模型的构建与验证

奥沙利铂通过诱导胃癌细胞铜死亡抑制胃癌

胃腺癌继发第二原发性恶性肿瘤临床特征分析及预后模型建立

基于血清MCP-1、MCPIP1联合炎症因子的进展性脑梗死患者血管斑块稳定性列线图预测模型构建与验证

低病毒载量乙肝肝癌人群肝切除术后肝衰发生风险预测模型的建立与分析