基于《癌症基因组图谱》(The Cancer Genome Atlas,TCGA)中的 RNA-seq 表达数据,以无进展间期(Progression-Free Interval,PFI)为条件,分析作为反应的总生存期(Overall Survival,OS)的新方法

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Bo Lin, Kaipeng Wang, Yuan Yuan, Yueguo Wang, Qingyuan Liu, Yulan Wang, Jian Sun, Wenwen Wang, Huanli Wang, Shusheng Zhou, Kui Jin, Mengping Zhang, Yinglei Lai
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引用次数: 0

摘要

癌症基因组图谱(TCGA)收集了作为生存时间的总生存期(OS)和无进展间期(PFI)。考虑它们在通路检测和生存预测中的依赖关系具有生物医学意义。我们打算在参数生存模型的基础上,开发整合 PFI 作为条件的新方法,以识别与 OS 相关的通路并预测 OS。基于条件概率框架,我们为此开发了一系列基于虚弱的参数模型,以指数分布或魏布勒分布为基线。我们还考虑了以 PFI 作为协变量的两类现有方法。我们通过分析 TCGA 中肺鳞状细胞癌和肺腺癌(LUNG)、脑低级别胶质瘤和多形性胶质母细胞瘤(GBMLGG)以及皮肤黑色素瘤(SKCM)的 RNA-seq 表达数据,评估了三种方法的性能。我们的重点是 14 条与癌症相关的一般路径。我们采用了 10 倍交叉验证来评估预测准确性。对于肺癌,所有方法都检测到了 p53 信号传导和细胞周期通路。此外,与未考虑 PFI 的方法相比,考虑了 PFI 的三种方法显示出明显更好的预测性能。对于 GBMLGG,所有方法都检测到了十种途径(如 Wnt 信号转导、JAK-STAT 信号转导、ECM-受体相互作用等)。此外,与未考虑 PFI 的方法相比,考虑 PFI 的三种方法显示出更好的预测性能。对于 SKCM,只有基于 Weibull 基线的模型检测到 p53 信号通路。与未考虑 PFI 的方法相比,考虑 PFI 的三种方法的预测性能明显更好。根据我们的研究,有必要将 PFI 纳入 OS 的生存分析中。此外,PFI 是一种生存类型的时间,我们基于条件概率的方法可以改善结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA)
Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.
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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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