Computational Analysis of Exosome-Derived Signature in TNBC: Integrating Single-Cell and Bulk Transcriptomics for Prognosis Prediction

Abstract

Triple-negative breast cancer (TNBC) is a particularly aggressive subtype of breast cancer with limited targeted therapeutic options. Exosomes, small membrane vesicles secreted by cells, play a crucial role in intercellular communication and material exchange. However, the role of exosome-related genes (ERGs) in TNBC remains unclear. In here, we analyzed single-cell RNA sequencing (scRNA-seq) from 10 TNBC samples and bulk RNA-seq from TCGA and METABRIC cohorts. Starting with 121 EDPS curated from the breast cancer-specific ExoBCD database, we identified exosome-active cell populations and derived an Exosome-Derived Prognostic Signature (EDPS) through integrative machine learning. Our analysis identified 31,140 cells from TNBC samples, categorized into nine cell types, with epithelial cells exhibiting the highest exosome-related scores. A total of 232 differentially expressed genes (DEGs) related to exosome-related scores were identified, with 19 prognostic genes selected through univariate Cox regression, leading to the construction of an EDPS. Low EDPS scores correlated with poorer clinical outcomes, higher immune infiltrates, and immune-related pathways. Furthermore, we identified notable differences in biological functions and mutation profiles between the two EDPS groups. Additionally, the low EDPS score group exhibited lower tumor immune dysfunction and exclusion (TIDE) scores, immunophenoscore (IPS), and higher immune checkpoint expression, suggesting better immunotherapy outcomes. In conclusion, while derived from exosome-related genes, the EDPS primarily reflects immune-active tumor microenvironments. This signature may help identify TNBC patients likely to benefit from immunotherapy, though further validation of its relationship to exosome biology is needed.