Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China.
BMC Genom Data. 2022 Oct 7;23(1):74. doi: 10.1186/s12863-022-01088-0.
Little is known about the prognostic risk factors of endometrial cancer. Therefore, finding effective prognostic factors of endometrial cancer is the vital for clinical theranostic. In this study, we constructed an inflammatory-related risk assessment model based on TCGA database to predict prognosis of endometrial cancer. We screened inflammatory genes by differential expression and prognostic correlation, and constructed a prognostic model using LASSO regression analysis. We fully utilized bioinformatics tools, including ROC curve, Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and in vitro experiments to verify the accuracy of the prognostic model. Finally, we further analyzed the characteristics of tumor microenvironment and drug sensitivity of these inflammatory genes. The higher the score of the endometrial cancer risk model we constructed, the worse the prognosis, which can effectively provide decision-making help for clinical endometrial diagnosis and treatment.
关于子宫内膜癌的预后危险因素知之甚少。因此，寻找有效的子宫内膜癌预后危险因素对于临床治疗至关重要。本研究基于 TCGA 数据库构建了一个炎症相关的风险评估模型，以预测子宫内膜癌的预后。我们通过差异表达和预后相关性筛选炎症基因，并使用 LASSO 回归分析构建了一个预后模型。我们充分利用了生物信息学工具，包括 ROC 曲线、Kaplan-Meier 分析、单因素和多因素 Cox 回归分析以及体外实验来验证预后模型的准确性。最后，我们进一步分析了这些炎症基因的肿瘤微环境特征和药物敏感性。我们构建的子宫内膜癌风险模型的评分越高，预后越差，这可以为临床子宫内膜诊断和治疗提供有效的决策帮助。
