Lei Han, Jialing Liu, Runjiao Zhang, Yanan Cheng, Li Dong, Lijuan Wei, Juntian Liu, Ke Wang, Jinpu Yu
{"title":"Insights From Nonsense-Mediated mRNA Decay for Prognosis in Homologous Recombination-Deficient Ovarian Cancer","authors":"Lei Han, Jialing Liu, Runjiao Zhang, Yanan Cheng, Li Dong, Lijuan Wei, Juntian Liu, Ke Wang, Jinpu Yu","doi":"10.1111/cas.70034","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":9580,"journal":{"name":"Cancer Science","volume":"116 5","pages":"1449-1463"},"PeriodicalIF":4.5000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cas.70034","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Science","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cas.70034","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Cancer Science (formerly Japanese Journal of Cancer Research) is a monthly publication of the Japanese Cancer Association. First published in 1907, the Journal continues to publish original articles, editorials, and letters to the editor, describing original research in the fields of basic, translational and clinical cancer research. The Journal also accepts reports and case reports.
Cancer Science aims to present highly significant and timely findings that have a significant clinical impact on oncologists or that may alter the disease concept of a tumor. The Journal will not publish case reports that describe a rare tumor or condition without new findings to be added to previous reports; combination of different tumors without new suggestive findings for oncological research; remarkable effect of already known treatments without suggestive data to explain the exceptional result. Review articles may also be published.