卵巢癌（OC）是一种与预后不良相关的恶性肿瘤，并与调节性T细胞（Tregs）在免疫微环境中有关。然而，Tregs相关基因（TRGs）与OC预后之间的关联尚不完全了解。本研究使用xCell算法分析了多个队列中的Tregs得分。采用加权基因共表达网络分析（WGCNA）确定潜在的TRGs和分子亚型。此外，我们采用九种机器学习算法建立了预后指标的风险模型。采用逆转录定量聚合酶链反应和免疫荧光染色对临床样本中的Tregs的免疫抑制能力和关键TRGs的表达进行了验证。研究发现较高的Tregs得分与较差的总生存率显著相关。复发患者表现出增加的Tregs浸润和减少的CD8+ T细胞。此外，使用七个关键TRGs进行的分子分型表明，B型亚型多个致癌通路富集且预后较差。值得注意的是，B型亚型显示出高Tregs水平，表明免疫抑制。此外，我们验证了基于机器学习的预后模型在多个平台队列中的预后模型以更好地区分患者生存和预测免疫治疗效果。最后，通过临床样本验证了关键TRGs的差异表达。本研究为我们对OC免疫微环境中Tregs的作用提供了新的洞察。我们确定了从Tregs衍生的潜在治疗靶点（CD24、FHL2、GPM6A、HOXD8、NAP1L5、REN和TOX3），以供个体化治疗，并建立了基于机器学习的OC患者预后模型，这在临床实践中可能很有用。© 2023 Wiley Periodicals LLC.

Ovarian cancer (OC) is a malignancy associated with poor prognosis and has been linked to regulatory T cells (Tregs) in the immune microenvironment. Nevertheless, the association between Tregs-related genes (TRGs) and OC prognosis remains incompletely understood. The xCell algorithm was used to analyze Tregs scores across multiple cohorts. Weighted gene co-expression network analysis (WGCNA) was utilized to identify potential TRGs and molecular subtypes. Furthermore, we used nine machine learning algorithms to create risk models with prognostic indicators for patients. Reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to demonstrate the immunosuppressive ability of Tregs and the expression of key TRGs in clinical samples. Our study found that higher Tregs scores were significantly correlated with poorer overall survival. Recurrent patients exhibited increased Tregs infiltration and reduced CD8+ T cell. Moreover, molecular subtyping using seven key TRGs revealed that subtype B exhibited higher enrichment of multiple oncogenic pathways and had a worse prognosis. Notably, subtype B exhibited high Tregs levels, suggesting immune suppression. In addition, we validated machine learning-derived prognostic models across multiple platform cohorts to better distinguish patient survival and predict immunotherapy efficacy. Finally, the differential expression of key TRGs was validated using clinical samples. Our study provides novel insights into the role of Tregs in the immune microenvironment of OC. We identified potential therapeutic targets derived from Tregs (CD24, FHL2, GPM6A, HOXD8, NAP1L5, REN, and TOX3) for personalized treatment and created a machining learning-based prognostic model for OC patients, which could be useful in clinical practice.© 2023 Wiley Periodicals LLC.