Abstract：
BACKGROUND: Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.
METHODS: We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
RESULTS: Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
CONCLUSIONS: Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
METHODS: We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
RESULTS: Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
CONCLUSIONS: Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
摘要：
背景：卵巢癌（OV）是女性生殖系统常见的恶性肿瘤，5年生存率约为30％。无效的早期诊断和预后导致大多数患者的生存不良。G蛋白偶联受体(GPCRs，人类细胞表面受体的最大家族）与OV相关。我们旨在鉴定GPCR相关基因（GPCRRG）特征，并开发一种新的模型来预测OV预后。
方法：我们从癌症基因组图谱（TCGA）和基因表达综合（GEO）数据库下载了数据。使用最小绝对收缩和选择算子（LASSO）Cox回归分析筛选预后GPCRRGs，并建立了预后模型。通过Kaplan-Meier(K-M)生存分析评估模型的预测能力。使用定量逆转录聚合酶链反应检查正常和OV细胞系中GPCRRG的水平。使用单样本基因集富集分析（ssGSEA）和CIBERSORT分析了高危和低危人群的免疫学特征。
结果：根据风险评分，17个GPCRRGs与OV预后相关。CXCR4，GPR34，LGR6，LPAR3和RGS2在三个OV数据集中显着表达，并且能够进行准确的OV诊断。预后模型的K-M分析表明，它可以区分高危和低危患者，对应于较差和更好的预测，分别。GPCRRG表达与免疫浸润率相关。
结论：我们的预后模型阐述了GPCRRGs在OV中的作用，为OV患者的预后和免疫反应预测提供了新的工具。
方法：我们从癌症基因组图谱（TCGA）和基因表达综合（GEO）数据库下载了数据。使用最小绝对收缩和选择算子（LASSO）Cox回归分析筛选预后GPCRRGs，并建立了预后模型。通过Kaplan-Meier(K-M)生存分析评估模型的预测能力。使用定量逆转录聚合酶链反应检查正常和OV细胞系中GPCRRG的水平。使用单样本基因集富集分析（ssGSEA）和CIBERSORT分析了高危和低危人群的免疫学特征。
结果：根据风险评分，17个GPCRRGs与OV预后相关。CXCR4，GPR34，LGR6，LPAR3和RGS2在三个OV数据集中显着表达，并且能够进行准确的OV诊断。预后模型的K-M分析表明，它可以区分高危和低危患者，对应于较差和更好的预测，分别。GPCRRG表达与免疫浸润率相关。
结论：我们的预后模型阐述了GPCRRGs在OV中的作用，为OV患者的预后和免疫反应预测提供了新的工具。
