Establishing a Prognostic Signature Based on Epithelial–Mesenchymal Transition-Related Genes for Endometrial Cancer Patients - Liu, Jinhui; Cui, Guoliang; Shen, Shuning; Gao, Feng; Zhu, Hongjun; Xu, Yinghua (Frontiers in Immunology, 2022)
Backgrounds: Epithelial-mesenchymal transition (EMT) is a sequential process that tumor cells develop from epithelial state to mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential and resistance to therapy, thus could affect the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC). Methods: EMT-related genes (ERGs) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazard regression analysis were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs were confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/ 3-/ 5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high and low risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways and response to drugs. Results: 220 genes were screened out from 1316 ERGs for their different expression in tumor versus normal. Next, 10 genes were found associated to OS in EC and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/ low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. GSEA discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher TMB and higher CTAL4/PD1 expression, which was in line with better response to immune checkpoint inhibitor. Similarly, response to chemotherapeutic drugs also turned out better in low-risk group. The high-risk group had higher m6A RNA expression, microsatellite instability level and stemness indices. Conclusion: We constructed the ERG-related signature model to predict the prognosis of EC patients. What’s more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.