Comprehensive genomic analysis of microenvironment phenotypes in ovarian cancer.

Comprehensive genomic analysis of microenvironment phenotypes in ovarian cancer.
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卵巢癌微环境表型的综合基因组分析

DOI:
10.7717/peerj.10255
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发表时间:
2020
期刊:
影响因子:
2.7
通讯作者:
Yang G
Yang G
中科院分区:
生物学3区
文献类型:
--
作者:
Wang J;Zhang T;Yang L;Yang G

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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。过去十年,卵巢癌的生存率并未显着提高。最近,免疫检查点抑制剂如靶向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。方法在本研究中,我们使用来自癌症基因组图谱(TCGA)数据库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集群:A和B。这些肿瘤微环境(TME)亚型与基因组和临床病理特征相关。建立了随机森林分类模型。差异基因组特征,功能富集,和DNA甲基化之间的两个集群进行了分析。分析免疫细胞浸润的特点及免疫相关细胞因子或标志物的表达。还在簇A和B之间进行体细胞突变分析。采用多因素考克斯分析影响预后的独立因素。结果卵巢癌TME簇A免疫细胞浸润少,免疫调节剂分布稀疏,表达低。相反,细胞毒性T细胞和免疫抑制细胞在卵巢癌TME簇B中显著增加。此外,免疫相关的细胞因子或标志物,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(DMEGs)。功能富集分析显示,簇B中的DMEGs参与重要的生物学过程和免疫相关途径。聚类B中的突变负荷不显著高于聚类A(p = 0.076)。多因素考克斯分析显示,TME是卵巢癌的独立预后因素(风险比:1.33,95%可信区间: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了描述和分类,并整合了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。
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.
DOI: 10.1093/nar/gkv007
发表时间: 2015-04-20
影响因子: 14.9
作者:
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