Integrative analysis of scRNA-seq, spatial transcriptomics, and machine learning constructs a prognostic model for Pyrotinib resistance in breast cancer

Pyrotinib resistance remains a major challenge in the treatment of breast cancer (BRCA), highlighting the need for reliable biomarkers and prognostic models. This study aimed to identify Pyrotinib resistance-related biomarkers and explore their regulatory mechanisms and therapeutic potential. We integrated single-cell RNA sequencing (scRNA-seq) of Pyrotinib-resistant SKBR3 cells, spatial transcriptomics (GSE243275), and bulk RNA-seq (TCGA-BRCA, GSE20685, GSE86374). Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and 10 machine learning algorithms were used to screen candidate genes and construct a prognostic model. Functional enrichment, regulatory network analysis, molecular docking, cell communication, pseudotime trajectory analyses and in vitro experiment were performed for validation. A total of 740 Pyrotinib resistance-related genes and 39 candidate genes were identified. The StepCox [forward] + Ridge model, consisting of HMGB3, TFPI, ACTG2, and JCHAIN, exhibited robust prognostic performance (C-index: 0.61–0.65; AUC ≥ 0.6 across datasets), with high-risk patients showing poorer survival. These genes were validated at the mRNA and protein levels, participated in immune-related pathways, and had distinct chromosomal/subcellular localizations. ABT-737 was identified as a potential targeted drug via molecular docking. Spatial transcriptomics revealed fibroblast-centered cell communication and dynamic biomarker expression during malignant progression. Lastly, JCHAIN, HMGB3, ACTG2, and TFPI were significantly upregulated at both mRNA and protein levels in Pyrotinib-resistant BRCA cells compared with parental control cells. The four-gene model serves as a reliable prognostic tool for Pyrotinib response and BRCA outcomes, providing novel insights into resistance mechanisms and precision therapy strategies.