Construction of a novel signature based on CRISPR-Cas9 screening for prognostic prediction in breast cancer.

Breast cancer (BC) is a highly heterogeneous malignancy and remains the leading cause of cancer-related mortality among women worldwide. Although advances in molecular classification and targeted therapies have improved outcomes for certain subtypes, robust prognostic biomarkers applicable across clinical contexts are still lacking. The CRISPR-Cas9 system offers a powerful platform for identifying cancer cell vulnerabilities and may facilitate the development of clinically relevant prognostic models. We integrated genome-wide CRISPR-Cas9 screening data from the DepMap database with transcriptomic and clinical data from TCGA and GEO datasets to identify BC cell survival-dependent genes (CSDGs). CSDGs prognostic signature was constructed using univariate Cox regression, LASSO, and stepwise multivariate Cox regression analyses. The model was validated in internal and external cohorts. Functional enrichment analyses, including GO, KEGG, WGCNA, and GSEA, were performed to explore the biological mechanisms underlying the signature. Random forest analysis and functional experiments were conducted to investigate the role of key gene in CSDGs signature. A total of 1,622 CSDGs were identified, and a nine-gene prognostic CSDGs signature (BRD4, CHORDC1, COPZ1, HNRNPC, NUP43, RAD1, RBBP8, TUBA1B, and VPS28) was developed. This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival, and its robustness was confirmed across multiple internal and external cohorts. High-risk patients exhibited a significant association with multiple adverse clinical features. A nomogram that combined the risk score with clinical variables showed robust predictive performance, and its C-index surpassed those of individual predictors, underscoring the enhanced accuracy of the integrated model. Functional analyses revealed enrichment of oncogenic pathways (e.g., MYC targets, G2/M checkpoint, mTORC1 signaling) in high-risk patients, while low-risk patients exhibited immune and hormone response signatures. CHORDC1 was identified as the most critical gene in the model. Knockdown of CHORDC1 significantly inhibited proliferation, migration, and invasion of BC cells. Transcriptomic profiling further linked CHORDC1 to oncogenic pathways, including EMT, mTORC1 signaling, and TNF-α/NF-κB signaling activation. We developed a CRISPR-Cas9 screening-based prognostic signature for BC that effectively stratifies patient risk and demonstrates robust predictive performance across cohorts. CHORDC1 was identified as a key oncogenic driver, promoting tumor progression via pathways such as EMT and mTORC1 signaling, highlighting its potential as a therapeutic target. These findings may contribute to the development of personalized prognostic tools and therapeutic strategies in BC.