Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Cancer Control. 2023 Jan-Dec;30:10732748231168756. doi: 10.1177/10732748231168756.
The abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC.
We retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy.
A total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis ( < .01), and the risk score might be considered an independent predictor for predicting the prognosis. In addition, risk scores and patient clinical information were used to construct clinical nomograms, improving the prediction's precision. We also explored the relationship between the risk score and ICI, immunotherapy and drug sensitivity.
Collectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.
免疫相关基因(IRGs)的异常表达在卵巢癌(OC)的发生和发展中起着重要作用,这是妇科癌症患者死亡的主要原因。本研究旨在建立一个预后风险模型,并全面分析 OC 风险评分与预后、免疫细胞浸润(ICI)和 OC 治疗敏感性之间的关系。
我们回顾性评估了癌症基因组图谱(TCGA)数据库中连续 OC 患者的临床病理特征。首先,通过生物信息学方法构建预后风险模型。然后,我们系统地评估了模型的稳健性,以及风险评分与预后之间的相关性,以及免疫细胞浸润的相关性。ICGC 队列用于验证预后风险模型。最后,我们评估了它们在 OC 免疫治疗和化疗中的治疗价值。
共鉴定出 10 个 IRG 来构建预后风险模型。生存分析显示,低风险组患者的预后较好(<0.01),风险评分可能被认为是预测预后的独立预测因子。此外,风险评分和患者的临床信息被用来构建临床列线图,提高了预测的准确性。我们还探讨了风险评分与 ICI、免疫治疗和药物敏感性之间的关系。
总的来说,我们确定了一个新的十个 IRGs 特征,它可能被用作 OC 的预后预测因子,从而有利于临床决策和患者的个性化治疗。