摘要
中医肿瘤学是一门古老而又年轻的中医临床学科,具有“整体观念、辨证论治”的中医学基本属性,还具有“以人为本、道法自然、顺势思维”的特色与优势,但其同样也面临发展的局限性。系统生物学整体研究、多变量整合、动态分析的思想体系和方法策略与中医肿瘤学的思维方式具有趋同性,可以成为中医肿瘤学发展中的借鉴和补充。充分借鉴系统生物学的核心研究平台,即基因组学、转录组学、蛋白质组学和代谢组学可以实现中医肿瘤学在理论、方法、技术上的全面发展,阐释中医肿瘤病因学、发病学、诊断辨证、治疗靶点、作用机制等的科学内涵,发现新的中医药疗法和药物。多组学研究策略可补充单组学研究的不足,整合单组学数据,更加符合中医肿瘤学整体性研究的特征。
Traditional Chinese medicine(TCM)oncology is an ancient while young clinical discipline of traditional Chinese medicine,which has the basic attribute of“holistic concept,syndrome differentiation and treatment”in the field of TCM,and has the characteristics and advantages of“being human-oriented,Taoism following nature,homeopathic thinking”,but also faces the limitations of development.The system biology includes the thought system and method strategy of integrated research,multivariable integration and dynamic analysis,which are similar to the thinking modes of TCM oncology and can be used as the reference and supplement in the development of TCM oncology.In the future research,the core research platform of system biology about genomics,transcriptomics,proteomics and metabonomics can be fully used for reference to realize the all-round development of TCM oncology in the theoretical,methodological and technological levels,can be adopted for explaining the scientific connotation of TCM oncology etiology,pathogenesis,diagnosis and differentiation,treatment target,and therapeutic mechanism,and will be helpful for the development of new Chinese medicine therapies and drugs.Multi-omics analysis strategy can supplement the deficiency of single omics analysis by the integration of the data of single omics,and presents more consistence with the holistic characteristics of the TCM oncology research.
作者
王院春
吴勉华
惠建荣
王希胜
朱叶萍
WANG Yuan-chun;WU Mian-hua;HUI Jian-rong;WANG Xi-sheng;ZHU Ye-ping(Nanjing University of Chinese Medicine,Nanjing 200032 Jiangsu,China;Affiliated Hospital of Shaanxi University of Chinese Medicine,Xianyang 712000 Shaanxi,China)
出处
《中医肿瘤学杂志》
2021年第3期11-17,共7页
Journal of Oncology in Chinese Medicine
基金
国家自然科学基金资助项目(编号:81774266)。
