Cheng Tingting, Zhan Xianquan
Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People's Republic of China.
Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People's Republic of China.
EPMA J. 2017 Mar 9;8(1):51-60. doi: 10.1007/s13167-017-0083-9. eCollection 2017 Mar.
Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multi-parameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.
预测、预防和个性化医疗（PPPM）是癌症领域的热点和未来发展方向。癌症是一种复杂的全身性疾病，涉及多因素、多过程和多后果。由外源性和内源性致癌物导致的基因（基因组）、RNA（转录组）、蛋白质（蛋白质组）、肽（肽组）、代谢物（代谢组）以及影像特征（放射组）等不同层面的一系列分子改变参与肿瘤发生，并在一个网络系统中相互关联且发挥作用，从而决定了使用单一分子作为癌症个性化预测、预防、诊断和治疗生物标志物的难度。准确的PPPM实践需要一个关键的分子组合。模式识别是发现癌症关键分子组合的有效方法。现代组学、计算生物学和系统生物学技术使得识别真正可靠的癌症PPPM实践分子模式成为可能。本文综述了癌症PPPM模式识别的病理生理基础、方法及应用前景，以便将我们之前关于癌症PPPM多参数策略的观点转化为癌症PPPM或精准医疗（PM）的实际研发。
