The heterogeneity of breast cancer metastasis: a bioinformatics analysis utilizing single-cell RNA sequencing data.

Abstract

Purpose: Breast cancer is a common malignancy in women, and its metastasis is a leading cause of cancer-related deaths. Single-cell RNA sequencing (scRNA-seq) can distinguish the molecular characteristics of metastasis and identify predictor genes for patient prognosis. This article explores gene expression in primary breast cancer tumor tissue against metastatic cells in the lymph node and liver using scRNA-seq.

Methods: Breast cancer scRNA-seq data from the Gene Expression Omnibus were used. The data were processed using R and the Seurat package. The cells were clustered and identified using Metascape. InferCNV is used to analyze the variation in copy number. Differential expression analysis was conducted for the cancer cells using Seurat and was enriched using Metascape.

Results: We identified 18 distinct cell clusters, 6 of which were epithelial. CNV analysis identified significant alterations with duplication of chromosomes 1, 8, and 19. Differential gene analysis resulted in 17 upregulated and 171 downregulated genes for the primary tumor in the primary tumor vs. liver metastasis comparison and 43 upregulated and 4 downregulated genes in the primary tumor in the primary tumor vs lymph node metastasis comparison. Several enriched terms include Ribosome biogenesis, NTP synthesis, Epithelial dedifferentiation, Autophagy, and genes associated with epithelial-to-mesenchymal transitions.

Conclusion: No single gene or pathway can clearly explain the mechanisms behind tumor metastasis. Several mechanisms contribute to lymph node and liver metastasis, such as the loss of differentiation, epithelial-to-mesenchymal transition, and autophagy. These findings necessitate further study of metastatic tissue for effective drug development.