Zhang Beilei, Li Zhanghang, Wang Kunqin, Duan Mingke, Yin Yidan, Zhan Qirui, Wang Fu, An Ruifang
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
Comput Biol Med. 2023 Sep;164:107343. doi: 10.1016/j.compbiomed.2023.107343. Epub 2023 Aug 9.
Ovarian cancer (OC), is a tumor that poses a serious threat to women's health due to its high mortality rate and bleak prognosis. Pyroptosis, a type of programmed cell death, is important for determining the prognosis of a patient's prognosis for cancer and may represent a novel target for treatment. However, research into how prognosis is impacted by pyroptosis-related genes (PRGs) is poorly understood. In this study, a prognostic model was created using bioinformatic analysis of PRGs in OC. In OC, we discovered 18 pyroptosis regulators that were either up- or down-regulated. By analyzing prognoses, we developed a 9-genes based prognostic model. Each OC patient received a risk score that could be used to categorize them into two subgroups: those with high risk and/or low chance of survival and those with low risk and/or high chance of survival. Functional enrichment and immunoinfiltration analysis indicated that low expression of immune pathways in high-risk group may account for the decrease of survival possibility. In Multivariable cox regression studies, age, clinical stage and the prognostic model were discovered to be independent factors impacting the prognosis for OC. To forecast OC patient survival, a predictive nomogram was developed. Furthermore, we found a correlation between predictive PRGs and clinical stage, indicating that AIM2, CASP3, ZBP1 and CASP8 may play a role in the growth of tumor in OC. After detailed and complete bioinformatics analysis, the lncRNA RP11-186B7.4/hsa-miR-449a/CASP8/AIM2/ZBP1 regulatory axis was identified in OC. Our study may provide a novel approach for prognostic biomarkers and therapeutic targets of OC.
卵巢癌（OC）是一种由于高死亡率和预后不佳而对女性健康构成严重威胁的肿瘤。细胞焦亡是一种程序性细胞死亡，对于确定癌症患者的预后很重要，可能代表一种新的治疗靶点。然而，关于细胞焦亡相关基因（PRGs）如何影响预后的研究了解甚少。在本研究中，通过对OC中PRGs的生物信息学分析创建了一个预后模型。在OC中，我们发现了18个上调或下调的细胞焦亡调节因子。通过分析预后，我们开发了一个基于9个基因的预后模型。每个OC患者都获得了一个风险评分，可用于将他们分为两个亚组：高风险和/或低生存机会组以及低风险和/或高生存机会组。功能富集和免疫浸润分析表明，高风险组免疫途径的低表达可能是生存可能性降低的原因。在多变量cox回归研究中，发现年龄、临床分期和预后模型是影响OC预后的独立因素。为了预测OC患者的生存情况，开发了一个预测列线图。此外，我们发现预测性PRGs与临床分期之间存在相关性，表明AIM2、CASP3、ZBP1和CASP8可能在OC肿瘤生长中起作用。经过详细和完整的生物信息学分析，在OC中鉴定出lncRNA RP11 - 186B7.4/hsa - miR - 449a/CASP8/AIM2/ZBP1调控轴。我们的研究可能为OC的预后生物标志物和治疗靶点提供一种新方法。
