Novel radiation-derived gene blueprint stratifying patients with breast cancer

Breast cancer (BC) is a prevalent global malignancy with a high recurrence rate. The effectiveness of predictive, preventive, and personalized treatment strategies is limited by a lack of reliable prognostic biomarkers. Radiotherapy significantly reduces breast cancer recurrence risk and prolongs patients' lives. However, the role of radiation-related genes in breast cancer remains unclear. Differentially expressed radiation-related genes were identified through analysis of the BRCA gene expression matrix between radiation and non-radiation groups. Multi-omics investigation, including bulk and single-cell RNA sequencing, was conducted to explore these genes in breast cancer. A risk model was developed using random forest, stepAIC, and LASSO Cox regression analyses to predict prognosis, immune cell infiltration, immunotherapy response, and targeted drug sensitivity based on radiation-related gene expression profiles. Functional differences were assessed via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses. We identified 133 radiation-related differentially expressed genes (DEGs), with 26 hub genes selected via LASSO and random forest models. Single-cell analysis revealed enrichment of radiation-related scores primarily in malignant cells. The radiation-related risk model, validated in the METABRIC dataset and an independent prognostic indicator in the TCGA-BRCA cohort, showed that low-risk patients had higher overall survival rates than high-risk patients. Risk scores correlated with immune infiltration, and low-risk patients exhibited greater immunotherapy response based on immune checkpoint gene expression. Drug sensitivity to gemcitabine, lapatinib, methotrexate, and doxorubicin varied across risk groups. To put it briefly, a strong efficient risk model was created to forecast prognosis, TME features, reactions to immunotherapy targeted medications in BRCA. This might lead to new understandings of individualized accurate treatment approaches. To facilitate clinical application, we have developed an R package and Excel-based calculator tool that enables clinicians to easily calculate patient risk scores using the 8-gene signature. These tools, along with detailed usage instructions, are freely available in the supplementary materials and GitHub repository.