Insights From Nonsense‐Mediated mRNA Decay for Prognosis in Homologous Recombination‐Deficient Ovarian Cancer

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.