Integrated Analysis of Bulk and Single-Cell Transcriptomics Identifies Prognostic Biomarkers in Breast Cancer Metastasis.

Breast cancer (BC) remains one of the leading causes of cancer-related mortality among women worldwide, with distant metastasis being the primary contributor to poor prognosis. However, the molecular mechanisms driving BC metastasis are not yet fully understood. We integrated three public microarray datasets (GSE14776, GSE103357, and GSE32489) to identify the differentially expressed genes (DEGs) associated with breast cancer metastasis. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene identification were performed using bioinformatics tools including DAVID, STRING, Cytoscape, and R. The prognostic significance of hub genes was assessed using Kaplan-Meier plotter and GEPIA. Expression validation was conducted through UALCAN, immunohistochemistry (IHC), and single-cell RNA sequencing (scRNA-seq) analysis from the GSE180286 dataset. A total of 295 co-DEGs were identified across the three datasets, enriched in pathways such as MAPK signaling, Rap1 signaling, and cell adhesion molecules. Twenty hub genes were identified from the PPI network, with eight showing strong prognostic value. Among them, PRC1 and POLR3H emerged as potential novel biomarkers. IHC confirmed the differential protein expression of PRC1, CDCA8, KIF14, and POLR3H. scRNA-seq analysis revealed that these hub genes were predominantly expressed in malignant epithelial and EMT (epithelial-mesenchymal transition) cells, particularly those from metastatic lymph node sites. This integrative analysis combining bulk and single-cell transcriptomic data identified key metastasis-associated genes in breast cancer. PRC1 and POLR3H, in particular, may serve as novel prognostic biomarkers and potential therapeutic targets.