Identification of differentially expressed genes at the single-cell level and prognosis prediction through bulk RNA sequencing data in breast cancer - Chen, Hanghang; Tian, Tian; Luo, Haihua; Jiang, Yong (Frontiers in Genetics, 2022)
Background: The invention and development of single-cell technologies contributed a lot to the understanding of tumor heterogeneity. The objective of this research was to investigate the differentially expressed genes (DEGs) between normal and tumor cells at the single-cell level and explore the clinical application of these genes with bulk RNA-sequencing data in breast cancer. Methods: We collected single-cell, bulk RNA-sequencing (RNA-seq) and microarray data from two public databases. Through single-cell analysis of 23909 mammary gland cells from seven healthy donors and 33138 tumor cells from seven breast cancer patients, cell type-specific DEGs between normal and tumor cells were identified. With these genes and bulk RNA-seq data, we developed a prognostic signature and validated the efficacy in two independent cohorts. We also explored the differences of immune infiltration and tumor mutational burden (TMB) between different risk groups. Results: A total of 6175 cell type-specific DEGs were obtained through single-cell analysis between normal and tumor cells in breast cancer, of which 1768 genes intersected with bulk RNA-seq data. An 18-gene signature was constructed to assess the outcomes in breast cancer patients. The efficacy of the signature was notably prominent in two independent cohorts. The low-risk group showed higher immune infiltration and lower TMB. Among the 18 genes in the signature, 16 were also differentially expressed in bulk RNA-seq dataset. Conclusion: Cell type-specific DEGs between normal and tumor cells were identified through single-cell transcriptome data. The signature constructed with these DEGs could stratify patients efficiently. The signature was also closely correlated with immune infiltration and TMB. Nearly all the genes in the signature were also differentially expressed at the bulk RNA-seq level.