Abstract
Not all ovarian cancer patients with homologous recombination deficiency, especially those with germline BRCA mutations, can benefit from platinum-based and targeted therapy. Our study aimed to determine the value of nonsense-mediated mRNA decay, which targeted these mutations. The retrospective analysis of 797 ovarian cancer patients was performed using two public cohorts and one in-house cohort. We developed a prediction algorithm for nonsense-mediated mRNA decay to discriminate between trigger and escape status, finding that escape status indicated a better prognosis. Subsequently, we analyzed differential gene expression and functional pathways between the two statuses and filtered 8 genes associated with the cell cycle. Then the optimized key gene model was built using integrated machine learning algorithms (mean AUC > 0.89), which had a higher independent prognostic value for ovarian cancer with germline BRCA variants or homologous recombination deficiency than the nonsense-mediated mRNA decay algorithm. Furthermore, we classified patients into high- and low-risk groups by the machine learning model and found that the low-risk group had a better prognosis with higher drug response and immune levels of activated dendritic cells than the high-risk controls. Our findings provide a perspective based on nonsense-mediated mRNA decay and cell cycle pathways to distinguish subtypes of germline BRCA or homologous recombination deficiency.