本研究旨在筛选卵巢癌（OC）的关键微小RNA（miRNA）-靶基因网络，并根据靶基因对OC分类和构建风险评估系统。我们回顾性收集了The Cancer Genome Atlas数据集以及GSE26193、GSE30161、GSE63885和GSE9891数据集中的OC样本数据。通过Pearson相关分析和miRNA与靶基因的有针对性分析，筛选关键miRNA-靶基因网络。从关键miRNA-靶基因网络中筛选与OC预后有关的靶基因，并对OC样本进行一致性聚类和最小绝对收缩和选择算子回归机器学习分析。在每个OC队列中，2651个关键miRNA-靶基因对的20个靶基因具有显著的预后相关性，OC被划分为三个聚类。在这三个分子聚类中，预后结果、生物通路、免疫细胞丰度和对免疫检查点阻滞（ICB）治疗和抗肿瘤药物的敏感性均存在差异。在这三个分子聚类中，S2表现出预后和免疫治疗反应率最低，并且免疫、缺氧、代谢和促进恶性癌症进展的通路以及浸润免疫和基质细胞种群丰度在该聚类中最高。我们创建了一个由八个靶基因组成的预后模型，使用该模型获得的风险指数不仅能显著区分样本的免疫特征，还能预测样本对ICB治疗的响应，并有助于筛选36种潜在的抗OC药物。本研究提供了一种基于关键miRNA-靶基因网络中预后靶基因对OC进行分类的策略，并创建了一个风险评估系统，用于预测OC患者的预后和对ICB治疗的响应，为OC的预后和精准治疗提供了分子基础。© 2023 John Wiley & Sons Ltd.

The present study was designed to screen key microRNA (miRNA)-target gene networks for ovarian cancer (OC) and to classify and construct a risk assessment system for OC based on the target genes.OC sample data of The Cancer Genome Atlas dataset and GSE26193, GSE30161, GSE63885 and GSE9891 datasets were retrospectively collected. Pearson correlation analysis and targeted analysis of miRNA and target gene were performed to screen key miRNA-target gene networks. Target genes associated with the prognosis of OC were screened from key miRNA-target gene networks for consensus clustering and least absolute shrinkage and selection operator-based regression machine learning analysis of OC samples.Twenty target genes of 2651 key miRNA-target gene pairs had significant prognostic correlation in each OC cohort, and OC was divided into three clusters. There were differences in prognostic outcome, biological pathways, immune cell abundance and susceptibility to immune checkpoint blockade (ICB) therapy and anti-tumor drugs among the three molecular clusters. S2 exhibited the least advantage in prognosis and immunotherapy response rate in the three molecular clusters, and the pathways regulating immunity, hypoxia, metabolism and promoting malignant progression of cancer, as well as infiltrating immune and stromal cell population abundance, were the highest in this cluster. An eight-target gene prognostic model was created, and the risk index obtained by using this model not only significantly distinguished the immune characteristics of the sample, but also predicted the response of the sample to ICB treatment, and helped to screen 36 potential anti-OC drugs.The present study provides a classification strategy for OC based on prognostic target genes in key miRNA-target gene networks, and creates a risk assessment system for predicting prognosis and response to ICB therapy in OC patients, providing molecular basis for prognosis and precise treatment of OC.© 2023 John Wiley & Sons Ltd.