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

IF 4.5 2区 医学 Q1 ONCOLOGY
Cancer Science Pub Date : 2025-03-01 DOI:10.1111/cas.70034
Lei Han, Jialing Liu, Runjiao Zhang, Yanan Cheng, Li Dong, Lijuan Wei, Juntian Liu, Ke Wang, Jinpu Yu
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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.

Abstract Image

从无义基因介导的 mRNA 衰变洞察同源重组缺陷卵巢癌的预后
并非所有同源重组缺陷的卵巢癌患者,尤其是那些有种系BRCA突变的患者,都能从铂类和靶向治疗中获益。我们的研究旨在确定针对这些突变的无义介导的mRNA衰减的价值。我们利用两个公共队列和一个内部队列对 797 例卵巢癌患者进行了回顾性分析。我们为无义介导的 mRNA 衰变开发了一种预测算法,以区分触发状态和逃逸状态,结果发现逃逸状态预示着较好的预后。随后,我们分析了两种状态之间不同的基因表达和功能通路,筛选出 8 个与细胞周期相关的基因。然后,我们利用综合机器学习算法建立了优化的关键基因模型(平均 AUC > 0.89),与无义介导的 mRNA 衰减算法相比,该模型对具有种系 BRCA 变异或同源重组缺陷的卵巢癌具有更高的独立预后价值。此外,我们通过机器学习模型将患者分为高危和低危组,发现低危组的预后较好,药物反应和活化树突状细胞的免疫水平均高于高危对照组。我们的研究结果提供了一个基于无义介导的mRNA衰变和细胞周期途径来区分生殖系BRCA或同源重组缺陷亚型的视角。
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来源期刊
Cancer Science
Cancer Science 医学-肿瘤学
自引率
3.50%
发文量
406
审稿时长
2 months
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.
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