Identification and experimental validation of core genes associated with breast cancer brain metastasis via machine learning

Breast cancer (BC) is the most common malignancy among women, with approximately 2.3 million new cases diagnosed annually, accounting for approximately 11.6% of all cancer cases worldwide [1].Distant metastasis is the primary cause of mortality in BC patients, with nearly 50% of patients ultimately developing metastatic disease.The predominant metastatic sites of BC include the lung, liver, brain, and bone, each exhibiting distinct biological characteristics that drive the organ-specific tropism of cancer cells [2,3].Among these, brain metastasis represents a significant cause of mortality in BC patients and is particularly prevalent in those with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer (TNBC) subtypes [4].Breast cancer brain metastasis (BCBM) can manifest in three forms: choroid plexus metastasis (rare), leptomeningeal metastasis (approximately 8%), and parenchymal metastasis, the most common presentation, with multiple lesions in 78% of cases and solitary lesions in 14%.Distinct anatomical regions of the brain provide different microenvironments, which in turn shape epidemiological patterns, biological behaviors, and therapeutic vulnerabilities of metastatic cancer [5].With the continuous advancement of systemic therapies and imaging surveillance, brain metastases from BC have become increasingly prevalent, accounting for approximately 10%-30% of all metastatic breast cancer (MBC) cases.The continuous progression of BCBM often compromises patients' cognitive and sensory functions, leading to neurological impairment and severely limiting quality of life (QOL).Notably, the mortality rate within one year after diagnosis remains at 80% [6].Current therapeutic strategies for BCBM primarily include surgery, whole-brain radiotherapy (WBRT), stereotactic radiosurgery (SRS), chemotherapy, or combinations thereof [7].Although these approaches