综合多组学数据的肝细胞癌分层策略及进展  

The Strategies and Progression in The Stratification of Hepatocellular Carcinoma Using Multi-omics Data

作　　者：王猛[1] 李晓琴[1] 高斌[1] WANG Meng;LI Xiao-Qin;GAO Bin(Faculty of Environment and Life of Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学环境与生命学部,北京100124

出　　处：《生物化学与生物物理进展》2023年第7期1651-1663,共13页Progress In Biochemistry and Biophysics

基　　金：国家自然科学基金(61931013);国家重点研发计划(2017YFC011104)资助项目。

摘　　要：肝细胞癌(hepatocellular carcinoma,HCC)是最常见和致命的肝脏恶性肿瘤。这种疾病的治疗一直受到其异质性的阻碍,极大限制了其个性化治疗的进展。因此,将高度异质的HCC分成具有相似特征的分子亚类对其临床治疗有着重要意义。随着高通量技术的不断发展,多种组学数据的关联研究可以加深了解HCC发生背后的生物学机制,也为HCC分层研究打开了新的思路。本文对当前HCC多组学分层策略及其相关研究进行了综述,并总结了当前HCC亚型的多组学特征。Hepatocellular carcinoma(HCC)is the most common and lethal liver malignancy.The treatment of this disease has been hampered by its heterogeneity,which severely limits progress in its personalized therapy.Therefore,it is necessary to divide highly heterogeneous HCC into molecular subtypes with similar characteristics for its clinical treatment.With the development of high-throughput technologies,integrative multi-omics data can deepen our comprehension of the biological mechanisms behind HCC pathogenesis.And it can also open new ideas for HCC stratification studies.Cluster analysis has been the main algorithm of cancer subtypes research for many years.Based on the number of input clustering algorithm omics,we summarize the current multi-omics HCC stratification methods into two major strategies:“from single-to-multi(S To M)”and“from multi-to-multi(M To M)”.Among them,the S To M strategy is to stratify HCC using different features of single omics and then combine multi-omics data to find the differential molecules among different HCC subtypes and verify the authenticity of their differences and the association with tumor biological phenomena.Feature selection is the core of the S To M strategy.Over the years,there are 3 approaches to the selection of stratified features in the S To M strategy:data distribution-based,biological features,and multi-omics approaches.Unlike S To M strategy,M To M strategy is based on the concept of systems biology and presents a landscape of the differences and associations between different omics within different subtypes.The core step of the M To M strategy is data dimensionality reduction,which puts multi-omics data into a low-dimensional stacked matrix,providing input for the subsequent cluster analysis.Generally,M To M strategy stratification algorithms can be classified into three categories:similarity-based,integration-based,and deep learning.We believe that both S To M and M To M need to pay attention to the combination of data and the applicability and practicality of r

关 键 词：肝细胞癌 多组学 亚型识别 

分 类 号：Q31[生物学—遗传学] R735.7[医药卫生—肿瘤]