Cross-cohort integrative multi-omics analysis of cancer essential gene ATP6V0B to dissect functional characteristics and clinical implications in breast cancer

Breast cancer remains a leading cause of cancer-related mortality in women, with recurrence, metastasis, and therapeutic resistance limiting outcomes. The role of vacuolar ATPase subunit ATP6V0B in breast cancer pathogenesis and immune regulation is poorly understood. We integrated CRISPR screening, machine learning (LASSO/Random Forest), and multi-omics data (> 10,000 samples) to identify ATP6V0B as a cancer essential gene. Single-cell transcriptomics and ligand-receptor network analysis dissected its TME regulatory role. Prognostic modeling (Cox regression/nomogram) was validated across cohorts. ATP6V0B was overexpressed in tumors, correlating with larger size, lymph node metastasis, aggressive subtypes (basal-like/HER2-enriched), and poor prognosis (OS/RFS/DMFS, p < 0.001). ATP6V0B+ tumor subsets are associated with immunosuppression via VEGFA-VEGFR2/THBS1-CD47, correlating with MDSC infiltration and immunotherapy resistance. A nomogram integrating ATP6V0B-related signature, stage, and age improved prognostic accuracy (1/3/5-year OS AUC 0.78/0.74/0.72). ATP6V0B is a prognostic biomarker and therapeutic target in breast cancer, driving immune evasion. The nomogram enables individualized prognostic assessment, supporting precision oncology. 1. ATP6V0B is identified as a cancer essential gene in breast cancer via integrative analysis of CRISPR screening, machine learning, and multi-omics data (> 10,000 samples). 2. High ATP6V0B activity correlates with aggressive clinicopathological features (large tumor size, lymph node metastasis, basal-like/HER2-enriched subtypes) and poor prognosis (OS, RFS, DMFS; p < 0.001). 3. ATP6V0B⁺ tumor subsets are associated with immunosuppressive microenvironment via ligand-receptor axes (VEGFA-VEGFR2, THBS1-CD47), correlating with MDSC infiltration and immune checkpoint inhibitor resistance. 4. A nomogram integrating ATP6V0B-related prognostic signature, clinical stage, and age improves prognostic stratification accuracy (1/3/5-year OS AUC: 0.78/0.74/0.72). 5. ATP6V0B holds promise as a prognostic biomarker and therapeutic target for precision oncology in breast cancer.