Yangyang Dong

Identification of an immune-related risk signature and nomogram predicting the overall survival in patients with endometrial cancer

Aimed to construct an immune-related risk signature and nomogram predicting endometrial cancer (EC) prognosis. An immune-related risk signature in EC was constructed using the least absolute shrinkage and selection operator regression analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A nomogram integrating the immune-related genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve to predict the overall survival (OS) of EC patients. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) R tool was used to explore the immune and stromal scores. CCL17, CTLA4, GPI, HDGF, HFE2, ICOS, IFNG, IL21R, KAL1, NR3C1, S100A2, and S100A9 were used in developing an immune-related risk signature evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had better OS (p<0.001). The area under the ROC curve (AUC) values of this model were 0.737, 0.764, and 0.782 for the 3-, 5-, and 7-year OS, respectively. A nomogram integrating the immune-related risk model and clinical features could accurately predict the OS (AUC=0.772, 0.786, and 0.817 at 3-, 5-, and 7-year OS, respectively). The 4 immune cell scores were lower in the high-risk group. Forkhead box P3 (FOXP3) and basic leucine zipper ATF-like transcription factor (BATF) showed a potential significant role in the immune-related risk signature. Twelve immune-related genes signature and nomogram for assessing the OS of patients with EC had a good practical value.

Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients

Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC. A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram. The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from -8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan-Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram. We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.

Prognostic significance of lymphovascular space invasion in patients with endometrioid endometrial cancer: a retrospective study from a single center

This study aims to analyze factors associated with lymphovascular space invasion (LVSI) and evaluate the prognostic significance of LVSI in Chinese endometrioid endometrial cancer (EEC) patients. Five-hundred eighty-four EEC patients undergoing surgery in our center from 2006 to 2016 were selected for analysis. Univariate analysis and multivariate logistic regression were used to examine relevant factors of LVSI. To evaluate the prognostic role of LVSI, survival analyses were conducted. In survival analyses, both multivariate Cox regression and propensity score matching were used to control the confounders. The incidence of LVSI was 12.16% (71/584). Diabetes history (p=0.021), lymph node metastasis (p=0.005), deep myometrial invasion (p<0.001) and negative PR expression (p=0.007) were independently associated with LVSI. Both Kaplan-Meier method and univariate Cox regressions showed LVSI negative and positive cases had similar tumor-specific survival (TSS) and disease-free survival (DFS). After adjusting for the influence of adjuvant therapy and other clinicopathological factors with multivariate Cox regressions, LVSI still could not bring additional survival risk to the patients (p=0.280 and p=0.650 for TSS and DFS, respectively). This result was verified by Kaplan-Meier survival analyses after propensity score matching (p=0.234 and p=0.765 for TSS and DFS, respectively). LVSI does not significantly compromise the survival outcome of Chinese EEC patients.