Machine learning multiomics Deciphers GRHL2 associated tumor microenvironment evolution guiding precision therapeutics in breast cancer

Grainyhead-like 2 (GRHL2) has been implicated in various cancers, but its role in breast cancer (BRCA) remains poorly understood. This study aims to comprehensively characterize the prognostic and immunological significance of GRHL2 in BRCA using multi-omics data. We analyzed single-cell RNA sequencing (scRNA-seq) data and multiple bulk RNA-seq datasets. Tumor cells were identified using InferCNV, and prognostic genes were refined via CoxBoost regression. GRHL2 was identified as a risk factor associated with poor survival in multiple cohorts. Functional enrichment analysis revealed its involvement in key oncogenic pathways. Furthermore, GRHL2 expression correlated with immune infiltration patterns, immunomodulator expression, and somatic mutation profiles. Drug sensitivity analysis suggested potential therapeutic agents targeting GRHL2-related pathways. Collectively, our findings highlight GRHL2 as a promising prognostic biomarker for risk stratification and a candidate therapeutic target, with specific implications for guiding immunotherapy and targeted treatment strategies in BRCA.