Abstract

Background This study aimed to analyze the breast cancer (BC) microenvironment using single-cell RNA sequencing (scRNA-seq) data, develop a prognostic stratification model, and identify potential therapeutic targets and drugs.Methods scRNA-seq, bulk transcriptomic data, and clinical information were obtained from EMBL-EBI (single cell data from 17 tumor samples), TCGA (1039 tumor samples), and GEO databases [GSE20685 (324 tumor samples), GSE42568 (104 tumor samples), and GSE88770 (117 tumor samples)]. scRNA-seq data were used to analyze the tumor microenvironment landscape of BC and retrieve subcluster-specific markers (SSMs). Subtypes of BC patients were defined based on SSMs and bulk transcriptomic data, and a prognostic risk model was constructed using machine learning. The characteristic therapeutic targets of high-risk patients were further identified and potential drugs were evaluated by molecular docking and cellular thermal shift assay. In vitro and in vivo experiments were conducted to explore the functions of the core target, 3-hydroxybutyrate dehydrogenase 1 (BDH1) and tretinoin, which modulated the malignant biological behaviors of BC.Results A total of 2004 SSMs were identified. Six prognostic cell sub-clusters were identified, and 16 prognostic genes were identified from the SSMs of these six cell sub-clusters. Least absolute shrinkage and selection operator (LASSO)-Cox algorithm further identified four core genes, and a risk model was constructed. The overall survival time of BC patients in the high-risk group was shorter than that of the low-risk group, and the risk score was a predictor of prognosis in BC patients in both training dataset and validation datasets (P<0.0001 in all datasets). Six potential drug targets were identified in high-risk patients, five of which were significantly highly expressed in BC, and BDH1 was associated with the overall survival of BC patients. Tretinoin showed good binding activity for all five targets. Depletion of