Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma.
Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma.
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用于胰腺导管腺癌预后预测的癌症干细胞相关特征的开发和验证
DOI：
10.1186/s12967-020-02527-1
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发表时间：
2020-09-21
影响因子：
7.4
通讯作者：
Peng C
中科院分区：
文献类型：
--
作者：
Feng Z;Shi M;Li K;Ma Y;Jiang L;Chen H;Peng C
研究背景肿瘤干细胞(CSCs)是导致胰腺导管腺癌(PDAC)恶性行为和预后不良的重要因素。近年来，CSC生物学得到了广泛的研究，但基于CSC相关基因的实用预后标志物尚未在PDAC中建立或报道。使用MTAB-6134队列作为训练集，使用一个中国本地队列和其他五个公共队列进行外部验证。选择具有可靠预后作用的CSC相关基因组成签名，并通过Kaplan-Meier生存率、受试者操作特征(ROC)和校准曲线来评估其预测性能。结果获得了一个由DCBLD2、GSDMD、PMAIP1和PLOD2组成的稳健的标记。它将患者分为高风险组和低风险组。高危患者的总生存期(OS)和无病生存期(DFS)显著短于低危患者。校正曲线和COX回归分析显示了较强的预测能力。ROC曲线显示，该模型的生存预测效果好于其他模型。功能分析显示风险评分与CSC标记物呈正相关。这些结果具有跨数据集兼容性。这一特征可以帮助进一步改进当前的TNM分期系统，并为未来新的个性化治疗策略的开发提供数据。
BackgroundCancer stem cells (CSCs) are crucial to the malignant behaviour and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established or reported in PDAC.MethodsA signature was developed and validated in seven independent PDAC datasets. The MTAB-6134 cohort was used as the training set, while one local Chinese cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and their predictive performance was evaluated by Kaplan–Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics of the gene signature.ResultsA robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It classified patients into high-risk and low-risk groups. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) than low-risk patients. Calibration curves and Cox regression analysis demonstrated powerful predictive performance. ROC curves showed the better survival prediction by this model than other models. Functional analysis revealed a positive association between risk score and CSC markers. These results had cross-dataset compatibility.ImpactThis signature could help further improve the current TNM staging system and provide data for the development of novel personalized therapeutic strategies in the future.
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影响因子：
9
作者：
Allen PJ;Kuk D;Castillo CF;Basturk O;Wolfgang CL;Cameron JL;Lillemoe KD;Ferrone CR;Morales-Oyarvide V;He J;Weiss MJ;Hruban RH;Gönen M;Klimstra DS;Mino-Kenudson M
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影响因子：
3.7
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Heeschen C
影响因子：
3.7
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通讯作者：
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影响因子：
8
作者：
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影响因子：
2.1
作者：
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