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A novel single-cell based method for breast cancer prognosis

文献类型：期刊论文

作者Li, Xiaomei1; Liu, Lin1; Goodall, Gregory J.2,3,4; Schreiber, Andreas2,3; Xu, Taosheng5; Li, Jiuyong1; Le, Thuc D.1
刊名PLOS COMPUTATIONAL BIOLOGY
出版日期2020-08-01
卷号16
ISSN号1553-734X
DOI10.1371/journal.pcbi.1008133
通讯作者Le, Thuc D.(Thuc.Le@unisa.edu.au)
英文摘要Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method,scPrognosis, to improve breast cancer prognosis with scRNA-seq data.scPrognosisuses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show thatscPrognosisoutperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer.scPrognosiswill also be useful when applied to scRNA-seq datasets of different biological processes other than EMT. Author summary Various computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.
WOS关键词EXPRESSION ; SURVIVAL ; PROGNOSTICATION ; METASTASIS ; MODELS
资助项目ARC DECRA Grant[DE200100200] ; National Natural Science Foundation of China[61902372] ; Natural Science Foundation of Anhui Province, China[2008085QF292] ; Presidential Foundation of Hefei Institutes of Physical Science, Chinese Academy of Sciences[YZJJ2018QN24] ; Cancer Research UK ; British Columbia Cancer Agency Branch
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
出版者PUBLIC LIBRARY SCIENCE
WOS记录号WOS:000565610800002
资助机构ARC DECRA Grant ; National Natural Science Foundation of China ; Natural Science Foundation of Anhui Province, China ; Presidential Foundation of Hefei Institutes of Physical Science, Chinese Academy of Sciences ; Cancer Research UK ; British Columbia Cancer Agency Branch
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/70442]  
专题中国科学院合肥物质科学研究院
通讯作者Le, Thuc D.
作者单位1.Univ South Australia, UniSA STEM, Mawson Lakes, SA, Australia
2.SA Pathol, Ctr Canc Biol, Adelaide, SA, Australia
3.Univ South Australia, Adelaide, SA, Australia
4.Univ Adelaide, Sch Med, Discipline Med, Adelaide, SA, Australia
5.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Li, Xiaomei,Liu, Lin,Goodall, Gregory J.,et al. A novel single-cell based method for breast cancer prognosis[J]. PLOS COMPUTATIONAL BIOLOGY,2020,16.
APA Li, Xiaomei.,Liu, Lin.,Goodall, Gregory J..,Schreiber, Andreas.,Xu, Taosheng.,...&Le, Thuc D..(2020).A novel single-cell based method for breast cancer prognosis.PLOS COMPUTATIONAL BIOLOGY,16.
MLA Li, Xiaomei,et al."A novel single-cell based method for breast cancer prognosis".PLOS COMPUTATIONAL BIOLOGY 16(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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