Identification of inflammatory-related gene signatures to predict prognosis of endometrial carcinoma

Abstract

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.