Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Pathology, The People's Hospital of Honghu, Honghu, Hubei, China.
Front Immunol. 2022 Jun 24;13:894718. doi: 10.3389/fimmu.2022.894718. eCollection 2022.
Ovarian cancer is the most lethal heterogeneous disease among gynecological tumors with a poor prognosis. Necroptosis, the most studied way of death in recent years, is different from apoptosis and pyroptosis. It is a kind of regulated programmed cell death and has been shown to be closely related to a variety of tumors. However, the expression and prognosis of necroptosis-related genes in ovarian cancer are still unclear. Our study therefore firstly identified the expression profiles of necroptosis-related genes in normal and ovarian cancer tissues. Next, based on differentially expressed necroptosis-related genes, we clustered ovarian cancer patients into two subtypes and performed survival analysis. Subsequently, we constructed a risk model consisting of 5 genes by LASSO regression analysis based on the differentially expressed genes in the two subtypes, and confirmed the strong prognostic ability of the model and its potential as an independent risk factor survival analysis and independent risk factor analysis. Based on this risk model, patients were divided into high and low risk groups. By exploring differentially expressed genes, enrichment functions and tumor immune microenvironment in patients in high and low risk groups, the results showed that patients in the low risk group were significantly enriched in immune signaling pathways. Besides, immune cells content, immune function activity was significantly better than the high-risk group. Eventually, we also investigated the sensitivity of patients with different risk groups to ICB immunotherapy and chemotherapy drugs. In conclusion, the risk model could effectively predict the survival and prognosis of patients, and explore the tumor microenvironment status of ovarian cancer patients to a certain extent, and provide promising and novel molecular markers for clinical diagnosis, individualized treatment and immunotherapy of patients.
卵巢癌是妇科肿瘤中预后最差的异质性最强的疾病。细胞程序性坏死是近年来研究最多的死亡方式，它不同于细胞凋亡和细胞焦亡，是一种受调控的细胞程序性死亡，与多种肿瘤密切相关。然而，卵巢癌中细胞程序性坏死相关基因的表达和预后尚不清楚。因此，我们首先鉴定了正常和卵巢癌组织中细胞程序性坏死相关基因的表达谱。接下来，基于差异表达的细胞程序性坏死相关基因，我们将卵巢癌患者聚类为两个亚型，并进行生存分析。随后，我们基于两个亚型中差异表达基因，通过 LASSO 回归分析构建了一个由 5 个基因组成的风险模型，并验证了该模型的强大预后能力及其作为独立预后因素的潜力。基于该风险模型，我们将患者分为高风险组和低风险组。通过探索高风险组和低风险组患者的差异表达基因、富集功能和肿瘤免疫微环境，结果表明低风险组患者在免疫信号通路中显著富集。此外，低风险组患者的免疫细胞含量和免疫功能活性明显优于高风险组。最后，我们还研究了不同风险组患者对 ICB 免疫治疗和化疗药物的敏感性。总之，该风险模型能够有效地预测患者的生存和预后，并在一定程度上探索卵巢癌患者的肿瘤微环境状态，为患者的临床诊断、个体化治疗和免疫治疗提供了有前景的新分子标志物。
