高级别浆液性卵巢癌（HGSOC）是一种高度致命的妇科癌症，需要精确的预后模型和个性化治疗策略。肿瘤微环境（TME）对疾病进展和治疗至关重要。基于机器学习的整合是识别预测性生物标志物和开发预后模型的强大工具。因此，使用基于机器学习的整合开发的免疫相关风险模型可以改进预后预测并指导HGSOC的个性化治疗。在HGSOC的生物信息学研究中，我们进行了（i）共识聚类，以基于免疫和基质细胞的标记识别免疫亚型，（ii）差异表达基因和单因素Cox回归分析，以得出TME和预后相关基因，（iii）基于十个独立机器学习算法构建的机器学习程序，用于筛选和构建TME相关风险评分（TMErisk），以及（iv）评估TMErisk在TME解构中的效果、基因组不稳定性的指示以及免疫疗法和化疗的指导作用。我们鉴定了两种不同的免疫微环境表型和一个强大而临床实用的预后评分系统。 TMErisk在跨队列中预测HGSOC预后上的表现优于大多数临床特征和其他已发表的标志物。低TMErisk组被明确定义为有BRCA1突变、免疫激活和更好的免疫反应，具有显著有利的预后。相反，高TMErisk组则与C-X-C motif化学因子配体的缺失和致癌激活途径显着相关。此外，低TMErisk组患者对十一种候选药物的反应更强。我们的研究开发了一种新的基于免疫相关风险模型，使用基于机器学习的整合预测卵巢癌患者的预后。此外，该研究不仅描述了HGSOC的TME中细胞组分的多样性，还指导了潜在的治疗技术的发展，以应对肿瘤免疫抑制并增强对癌症治疗的反应。 版权所有 © 2023 Wu，Tian，He，Liu，Ou，Li和Fu。

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecological cancer that requires accurate prognostic models and personalized treatment strategies. The tumor microenvironment (TME) is crucial for disease progression and treatment. Machine learning-based integration is a powerful tool for identifying predictive biomarkers and developing prognostic models. Hence, an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for HGSOC.During the bioinformatic study in HGSOC, we performed (i) consensus clustering to identify immune subtypes based on signatures of immune and stromal cells, (ii) differentially expressed genes and univariate Cox regression analysis to derive TME- and prognosis-related genes, (iii) machine learning-based procedures constructed by ten independent machine learning algorithms to screen and construct a TME-related risk score (TMErisk), and (iv) evaluation of the effect of TMErisk on the deconstruction of TME, indication of genomic instability, and guidance of immunotherapy and chemotherapy.We identified two different immune microenvironment phenotypes and a robust and clinically practicable prognostic scoring system. TMErisk demonstrated superior performance over most clinical features and other published signatures in predicting HGSOC prognosis across cohorts. The low TMErisk group with a notably favorable prognosis was characterized by BRCA1 mutation, activation of immunity, and a better immune response. Conversely, the high TMErisk group was significantly associated with C-X-C motif chemokine ligands deletion and carcinogenic activation pathways. Additionally, low TMErisk group patients were more responsive to eleven candidate agents.Our study developed a novel immune-related risk model that predicts the prognosis of ovarian cancer patients using machine learning-based integration. Additionally, the study not only depicts the diversity of cell components in the TME of HGSOC but also guides the development of potential therapeutic techniques for addressing tumor immunosuppression and enhancing the response to cancer therapy.Copyright © 2023 Wu, Tian, He, Liu, Ou, Li and Fu.