Department of Mathematics, University of Maryland, College Park, MD, 20742, USA.
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, 20740, USA.
Sci Rep. 2021 Feb 5;11(1):3203. doi: 10.1038/s41598-021-82332-y.
Detection of prognostic factors associated with patients' survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called "Cox-TOTEM" to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers.
检测与患者生存结果相关的预后因素有助于深入了解疾病并指导治疗决策。高通量技术的快速发展产生了大量的基因组生物标志物作为候选预后因素，但大多数在临床应用中作用有限。随着技术价格的降低，许多基因组研究在不同的队列中进行，以探索一个共同的科学问题，从而确定更具可重复性和可信度的生物标志物。然而，当联合分析多个研究时，来自研究人群和设计的异质性带来了新的挑战。例如，来自不同队列的患者表现出不同的人口统计学特征和风险状况。然而，现有的用于生存分析的高维变量选择方法仅限于单研究分析。我们提出了一种新的基于 Cox 模型的两阶段变量选择方法，称为“Cox-TOTEM”，用于检测多个基因组研究中共同存在的与生存相关的生物标志物。模拟结果表明，与将现有方法分别应用于每个研究相比，我们的方法大大提高了变量选择的灵敏度，尤其是在信号较弱或研究存在异质性时。我们的方法在 TCGA 转录组数据中的应用确定了与五种妇科癌症共同疾病机制相关的重要生存相关基因。
