Construction and Validation of a Prognostic Risk Prediction Model for Lactate Metabolism-Related lncRNA in Endometrial Cancer

Endometrial cancer (EC) is a common group of malignant epithelial tumors that mainly occur in the female endometrium. Lactate is a key regulator of signal pathways in normal and malignant tissues. However, there is still no research on lactate metabolism-related lncRNA in EC. Here, we intended to establish a prognostic risk model for EC based on lactate metabolism-related lncRNA to forecast the prognosis of EC patients. First, we found that 38 lactate metabolism-associated lncRNAs were significantly overall survival through univariate Cox regression analysis. Using minimum absolute contraction and selection operator (LASSO) regression analysis and multivariate Cox regression analysis, six lactate metabolism-related lncRNAs were established as independent predictor in EC patients and were used to establish a prognostic risk signature. We next used multifactorial COX regression analysis and receiver operating characteristic (ROC) curve analysis to confirm that risk score was an independent prognostic factor of overall patient survival. The survival time of patients with EC in different high-risk populations was obviously related to clinicopathological factors. In addition, lactate metabolism-related lncRNA in high-risk population participated in multiple aspects of EC malignant progress through Gene Set Enrichment Analysis, Genomes pathway and Kyoto Encyclopedia of Genes and Gene Ontology. And risk scores were strongly associated with tumor mutation burden, immunotherapy response and microsatellite instability. Finally, we chose a lncRNA SRP14-AS1 to validate the model we have constructed. Interestingly, we observed that the expression degree of SRP14-AS1 was lower in tumor tissues of EC patients than in normal tissues, which was consistent with our findings in the TCGA database. In conclusion, our study constructed a prognostic risk model through lactate metabolism-related lncRNA and validated the model, confirming that the model can be used to predict the prognosis of EC patients and providing a molecular analysis of potential prognostic lncRNA for EC.