A novel single-cell based method for breast cancer prognosis

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dc.contributor.authorLi, X.
dc.contributor.authorLiu, L.
dc.contributor.authorGoodall, G.J.
dc.contributor.authorSchreiber, A.
dc.contributor.authorXu, T.
dc.contributor.authorLi, J.
dc.contributor.authorLe, T.D.
dc.contributor.editorIoshikhes, I.
dc.date.issued2020
dc.description.abstractBreast 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. scPrognosis uses 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 that scPrognosis outperforms 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. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.
dc.description.statementofresponsibilityXiaomei Li, Lin Liu, Gregory J. Goodall, Andreas Schreiber, Taosheng Xu, Jiuyong Li, Thuc D. Le
dc.identifier.citationPLoS Computational Biology, 2020; 16(8):e1008133-1-e1008133-20
dc.identifier.doi10.1371/journal.pcbi.1008133
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.orcidGoodall, G.J. [0000-0003-1294-0692]
dc.identifier.orcidSchreiber, A. [0000-0002-9081-3405]
dc.identifier.urihttp://hdl.handle.net/2440/128437
dc.language.isoen
dc.publisherPublic Library of Science
dc.relation.granthttp://purl.org/au-research/grants/arc/DE200100200
dc.rights© 2020 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pcbi.1008133
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectPrognosis
dc.subjectSequence Analysis, RNA
dc.subjectGene Expression
dc.subjectFemale
dc.subjectEpithelial-Mesenchymal Transition
dc.subjectSingle-Cell Analysis
dc.titleA novel single-cell based method for breast cancer prognosis
dc.typeJournal article
pubs.publication-statusPublished

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