Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

中文翻译：

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数据驱动的翻译性前列腺癌研究：从生物标记物发现到临床决策。

前列腺癌（PCa）是一种常见的恶性肿瘤，在全球男性中发病率不断上升且异质性很高。在大数据和人工智能时代，生物标志物发现范式正在从传统的实验和基于小数据的识别向大数据驱动和系统级筛选转变。遗传因素和环境影响之间的复杂相互作用为PCa的发生和演化的系统建模提供了机会。我们在此回顾PCa临床翻译信息学的当前研究前沿。首先，介绍了PCa开发和临床治疗学的异质性和复杂性，引起了PCa系统生物学研究的关注。然后，对分子标记，临床表型和生活方式改变等生物标志物和危险因素进行了阐述，以进行PCa个性化管理。讨论了多维数据集成和计算建模的方法和应用。最后提供了PCa系统医学和整体保健的未来前景和挑战。