Background Ovarian cancer is one of the leading causes of cancer-related death in women. The incidence of ovarian cancer is insidious, and the recurrence rate is high. The survival rate of ovarian cancer has not significantly improved over the past decade. Recently, immune checkpoint inhibitors such as those targeting CTLA-4, PD-1, or PD-L1 have been used to treat ovarian cancer. Therefore, a full analysis of the immune biomarkers associated with this malignancy is necessary. Methods In this study, we used data from The Cancer Genome Atlas (TCGA) database to analyze the infiltration patterns of specific immune cell types in tumor samples. Data from the Gene Expression Omnibus (GEO) database was used for external validation. According to the invasion patterns of immune cells, we divided the ovarian cancer microenvironment into two clusters: A and B. These tumor microenvironment (TME) subtypes were associated with genomic and clinicopathological characteristics. Subsequently, a random forest classification model was established. Differential genomic features, functional enrichment, and DNA methylation were analyzed between the two clusters. The characteristics of immune cell infiltration and the expression of immune-related cytokines or markers were analyzed. Somatic mutation analysis was also performed between clusters A and B. Finally, multivariate Cox analysis was used to analyze independent prognostic factors. Results The ovarian cancer TME cluster A was characterized by less infiltration of immune cells and sparse distribution and low expression of immunomodulators. In contrast, cytotoxic T cells and immunosuppressive cells were significantly increased in the ovarian cancer TME cluster B. Additionally, immune-related cytokines or markers, including IFN-γ and TNF-β, were also expressed in large quantities. In total, 35 differentially methylated and expressed genes (DMEGs) were identified. Functional enrichment analyses revealed that the DMEGs in cluster B participated in important biological processes and immune-related pathways. The mutation load in cluster B was insignificantly higher than that of cluster A (p = 0.076). Multivariate Cox analysis showed that TME was an independent prognostic factor for ovarian cancer (hazard ratio: 1.33, 95% confidence interval: 1.01–1.75, p = 0.041). Conclusion This study described and classified basic information about the immune invasion pattern of ovarian cancer and integrated biomarkers related to different immunophenotypes to reveal interactions between ovarian cancer and the immune system.

中文翻译：

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卵巢癌微环境表型的综合基因组分析

背景 卵巢癌是女性癌症相关死亡的主要原因之一。卵巢癌发病隐匿，复发率高。在过去十年中，卵巢癌的存活率并没有显着提高。最近，针对 CTLA-4、PD-1 或​​ PD-L1 的免疫检查点抑制剂已被用于治疗卵巢癌。因此，有必要对与这种恶性肿瘤相关的免疫生物标志物进行全面分析。方法 在这项研究中，我们使用癌症基因组图谱 (TCGA) 数据库中的数据来分析肿瘤样本中特定免疫细胞类型的浸润模式。来自基因表达综合 (GEO) 数据库的数据用于外部验证。根据免疫细胞的侵袭模式，我们将卵巢癌微环境分为两个簇：A 和 B。这些肿瘤微环境 (TME) 亚型与基因组和临床病理学特征相关。随后，建立了随机森林分类模型。分析了两个簇之间的差异基因组特征、功能富集和 DNA 甲基化。分析免疫细胞浸润特征和免疫相关细胞因子或标志物的表达情况。还在聚类 A 和 B 之间进行体细胞突变分析。最后，使用多变量 Cox 分析来分析独立的预后因素。结果卵巢癌TME簇A具有免疫细胞浸润少、免疫调节剂分布稀疏、低表达的特点。相比之下，卵巢癌 TME 簇 B 中的细胞毒性 T 细胞和免疫抑制细胞显着增加。此外，免疫相关细胞因子或标志物，包括 IFN-γ 和 TNF-β，也大量表达。总共鉴定了 35 个差异甲基化和表达基因 (DMEG)。功能富集分析表明，簇 B 中的 DMEGs 参与了重要的生物学过程和免疫相关途径。集群 B 中的突变负荷显着高于集群 A (p = 0.076)。多变量 Cox 分析显示，TME 是卵巢癌的独立预后因素（风险比：1.33，95% 置信区间：1.01-1.75，p = 0.041）。结论 本研究对卵巢癌免疫侵袭模式的基本信息进行描述和分类，并整合与不同免疫表型相关的生物标志物，以揭示卵巢癌与免疫系统之间的相互作用。