Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis

Abstract

Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNAseq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.(c) 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creative