Multidimensional single-cell analysis of the molecular characteristics and functional pathways of Regulatory T cells in the microenvironment of HR+ breast cancer

The HR+/HER2- subtype represents the most prevalent form of breast cancer. T-cell heterogeneity and functional status within the breast cancer microenvironment significantly influence tumor progression and the efficacy of immunotherapy. Single-cell RNA sequencing(scRNA-seq) is a powerful tool that enables an in-depth analysis of diverse cell types and their molecular characteristics within tumor tissues. This study analyzed scRNA-seq data from HR+/HER2- and ER+ breast cancer samples (GSE228499 and GSE176078) sourced from the GEO database. Initially, the Seurat package was employed for quality control and normalization of the data, followed by dimensionality reduction and clustering. Cell type identification was conducted using the SingleR and Garnett tools, with a focus on the extraction and annotation of T cells and their subsets. Subsequently, FindAllMarkers was utilized to screen for differentially expressed genes, in conjunction with Gene Set Enrichment Analysis (GSEA) pathway analysis using the GSEABase package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) Enrichment Analysis using the Sangerbox platform. CellChat was employed to analyze intercellular communication, and Monocle was used for pseudo-time analysis to visualize T cell differentiation trajectories. This study presents a multilayer analysis of scRNA-seq data derived from 29,540 HR+/HER2- cells and 41,103 ER+ cells, with a specific focus on T cells and Regulatory T (Treg) cells. By employing advanced single-cell sequencing techniques, we elucidate the distinct phenotypic and functional profiles of Treg cells, revealing their pivotal roles in tumor immune evasion and progression. This approach systematically elucidates the heterogeneity and functional characteristics of T cells and Treg cells within the HR+ breast cancer microenvironment, thereby providing a solid data foundation for advancing our understanding of the tumor immune microenvironment. Breast cancer, scRNA-seq, Tumor immune microenvironment, Treg