Lin Yuxin, Zhao Xiaojun, Miao Zhijun, Ling Zhixin, Wei Xuedong, Pu Jinxian, Hou Jianquan, Shen Bairong
Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Department of Urology, Suzhou Dushuhu Public Hospital, Suzhou, 215123, China.
J Transl Med. 2020 Mar 7;18(1):119. doi: 10.1186/s12967-020-02281-4.
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.
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具有高度异质性。在大数据和人工智能时代，生物标志物发现的模式正从传统的基于实验和小数据的识别转向大数据驱动和系统层面的筛选。遗传因素与环境效应之间的复杂相互作用为前列腺癌发生和发展的系统建模提供了机会。我们在此回顾前列腺癌临床转化信息学的当前研究前沿。首先，介绍前列腺癌发展及临床诊疗中的异质性和复杂性，以引发对前列腺癌系统生物学研究的关注。然后阐述从分子改变到临床表型及生活方式变化等方面的生物标志物和风险因素，用于前列腺癌的个性化管理。讨论了多维度数据整合和计算建模的方法及应用。最后给出了前列腺癌系统医学和整体医疗的未来前景与挑战。
