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The study presents the development of a predictive machine learning model called CSOARG, which utilizes single-cell and multi-omics data to forecast prognosis and drug responses in ovarian cancer. The model, based on an 8-gene signature, demonstrated a correlation with genomic instability and was validated to predict five-year overall survival rates, showing that high-risk patients had poorer responses to chemotherapy and immunotherapy. The findings suggest that the CSOARG model could enhance clinical decis

Author
Charmi Oswal
Language
EN