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Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation.

基于转录组失调的胰腺癌四基因预后模型的开发。

  • 影响因子:5.5150
  • DOI:10.18632/aging.102844
  • 作者列表:"Yan J","Wu L","Jia C","Yu S","Lu Z","Sun Y","Chen J
  • 发表时间:2020-02-20
Abstract

:We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.

摘要

: 我们系统地开发了一个胰腺癌预后模型,该模型在不同的转录组学平台和患者队列中是兼容的。在进行质量控制措施后,我们使用了 7 个微阵列数据集和 2 个 RNA 测序数据集来鉴定胰腺癌患者中持续失调的基因。进行加权基因共表达网络分析,探讨基因表达模式与临床特征之间的关联。使用最小绝对收缩和选择算子 (LASSO) 和 Cox 回归构建预后模型。我们通过确定时间依赖性生存的风险评分曲线下面积来检验模型的预测能力。胰腺癌中大部分差异表达基因富集了与肿瘤免疫微环境相关的功能。发现转录组谱与总生存期相关,4 个基因被确定为独立的预后因素。然后提出了预后风险评分,在训练和自我验证队列中显示中等的准确性。此外,两个独立的微阵列队列中的患者成功地分为高、低风险预后组。因此,我们构建了一个可靠的胰腺癌预后模型,这将有利于临床治疗决策。

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影响因子:2.69
发表时间:2020-01-18
DOI:10.1016/j.bbrc.2020.01.048
作者列表:["Li Y","Wang Z","Jin J","Zhu SX","He GQ","Li SH","Wang J","Cai Y"]

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影响因子:2.46
发表时间:2020-01-01
DOI:10.1097/COC.0000000000000609
作者列表:["Appelt AL","Andersen RF","Lindebjerg J","Jakobsen A"]

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