PDF(Pubmed)
Abstract：
Ovarian cancer is a heterogeneous disease with different molecular phenotypes. We performed molecular typing of ovarian cancer using cell differentiation trajectory analysis and proposed a prognostic risk scoring model. Using the copy number variation provided by inferCNV, we identified malignant tumor cells. Then, ovarian cancer samples were divided into four subtypes based on differentiation-related genes (DRGs). There were significant differences in survival rates, clinical features, tumor microenvironment scores, and the expression levels of ICGs among the subtypes. Based on nine DRGs, a prognostic risk score model was generated (AUC at 1 year: 0.749; 3 years: 0.651). Then we obtained a nomogram of the prognostic variable combination, including risk scores and clinicopathological characteristics, and predicted the 1-, 3- and 5-year overall survival. Finally, we explored some issues of immune escape using the established risk model. Our study demonstrates the significant influence of cell differentiation on predicting prognosis in OV patients and provides new insights for OV treatment and potential immunotherapeutic strategies.
摘要：
卵巢癌是一种具有不同分子表型的异质性疾病。我们使用细胞分化轨迹分析对卵巢癌进行分子分型，并提出预后风险评分模型。使用由inereCNV提供的拷贝数变异，我们确定了恶性肿瘤细胞。然后，根据分化相关基因(DRGs)将卵巢癌样本分为4种亚型.生存率有显著差异，临床特征,肿瘤微环境评分,和ICGs在亚型之间的表达水平。基于九个DRG，产生预后风险评分模型(1年AUC:0.749;3年AUC:0.651).然后我们获得了预后变量组合的列线图，包括风险评分和临床病理特征，并预测了1-，3年和5年总生存期。最后,我们利用建立的风险模型探讨了免疫逃逸的一些问题。我们的研究证明了细胞分化对预测OV患者预后的重要影响，并为OV治疗和潜在的免疫治疗策略提供了新的见解。
