Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

中文翻译：

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基因组选择的扩展应用筛选多组学数据以获取前列腺癌的预后特征。

使用几十个基因表达谱的预后测试有助于为前列腺癌 (PCa) 提供治疗选择。然而，这些测试需要改进以满足解决过度治疗的临床需求，这仍然是 PCa 管理中的普遍问题。本研究采用基因组选择 (GS) 方法，利用全基因组标记来预测农艺性状，用于 PCa 预后。我们利用癌症基因组图谱 (TCGA) 数据库来评估六种 GS 方法和七种组学数据组合的预测性能，结果表明最佳线性无偏预测 (BLUP) 模型在可预测性和计算效率方面优于其他方法。利用 BLUP-HAT 方法，BLUP 的加速版本，我们证明，与由少量基因组成的面板相比，使用大量疾病相关基因的表达数据并整合其他组学数据（即 miRNA）显着提高了结果的可预测性。最后，我们开发了一种新的逐步前向选择 BLUP-HAT 方法，以方便搜索多组学数据以寻找具有预后潜力的预测变量。将新方法应用于 TCGA 数据以推导出 mRNA 和 miRNA 表达特征，用于预测 PCa 的无复发存活率，这些特征在六个独立队列中得到验证。这是对高效 BLUP-HAT 方法及其派生算法的跨学科采用，用于分析 PCa 预后的多组学数据。