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神经母细胞瘤的 3 蛋白表达特征用于预后预测。

A 3-Protein Expression Signature of Neuroblastoma for Outcome Prediction.

机构信息

Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, China.

Ophthalmology.

出版信息

Am J Surg Pathol. 2018 Aug;42(8):1027-1035. doi: 10.1097/PAS.0000000000001082.

Abstract

Neuroblastoma (NB) is the most common extracranial solid tumor in children with contrasting outcomes. Precise risk assessment contributes to prognosis prediction, which is critical for treatment strategy decisions. In this study, we developed a 3-protein predictor model, including the neural stem cell marker Msi1, neural differentiation marker ID1, and proliferation marker proliferating cell nuclear antigen (PCNA), to improve clinical risk assessment of patients with NB. Kaplan-Meier analysis in the microarray data (GSE16476) revealed that low expression of ID1 and high expression of Msi1 and PCNA were associated with poor prognosis in NB patients. Combined application of these 3 markers to constitute a signature further stratified NB patients into different risk subgroups can help obtain more accurate prediction performance. Survival prognostic power of age and Msi1_ID1_PCNA signature by receiver operating characteristics analysis showed that this signature predicted more effectively and sensitively compared with classic risk stratification system, compensating for the deficiency of the prediction function of the age. Furthermore, we validated the expressions of these 3 proteins in neuroblastic tumor spectrum tissues by immunohistochemistry revealed that Msi1 and PCNA exhibited increased expression in NB compared with intermedial ganglioneuroblastoma and benign ganglioneuroma, whereas ID1 levels were reduced in NB. In conclusion, we established a robust risk assessment predictor model based on simple immunohistochemistry for therapeutic decisions of NB patients.

摘要

神经母细胞瘤(NB)是儿童中最常见的颅外实体瘤,其预后差异较大。精确的风险评估有助于预测预后,这对治疗策略的决策至关重要。在本研究中,我们开发了一个由 3 种蛋白组成的预测模型,包括神经干细胞标志物 Msi1、神经分化标志物 ID1 和增殖标志物增殖细胞核抗原(PCNA),以改善 NB 患者的临床风险评估。微阵列数据(GSE16476)中的 Kaplan-Meier 分析显示,ID1 表达低和 Msi1 和 PCNA 表达高与 NB 患者的预后不良相关。这 3 种标志物的联合应用进一步将 NB 患者分层为不同的风险亚组,可以帮助获得更准确的预测性能。受试者工作特征分析显示,年龄和 Msi1_ID1_PCNA 标志物对生存预后的预测能力优于经典风险分层系统,弥补了年龄预测功能的不足。此外,我们通过免疫组织化学验证了这些 3 种蛋白在神经母细胞瘤谱组织中的表达,结果显示,与中间型神经节母细胞瘤和良性神经节细胞瘤相比,NB 中 Msi1 和 PCNA 的表达增加,而 ID1 的水平降低。总之,我们建立了一个基于简单免疫组织化学的稳健风险评估预测模型,用于 NB 患者的治疗决策。

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