Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Journal of Translational Medicine

Zhijun Miao¹  Xiaojun Zhao²  Xuedong Wei²  Zhixin Ling²  Jinxian Pu²  Yuxin Lin²  Jianquan Hou²  Bairong Shen³ 

[1] Department of Urology, Suzhou Dushuhu Public Hospital;Department of Urology, The First Affiliated Hospital of Soochow University;Institutes for Systems Genetics, West China Hospital, Sichuan University;

关键词: Prostate cancer;    Translational informatics;    Biomarker discovery;    Systems medicine;    Clinical application;   

DOI  :  10.1186/s12967-020-02281-4

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown