Construction and Validation of a Prognostic Model Based on 11 Lymph Node Metastasis Related Genes for Overall Survival in Endometrial Cancer

Abstract

Background: Endometrial cancer (EC) is one of the most common malignant tumor in the female reproductive system. The incidence of lymph node metastasis (LNM) is only about 10% in clinically suspected early-stage EC patients. Discovering prognostic model and effective biomarkers for early diagnosis is important to reduce the mortality rate. Methods: We downloaded the RNA-sequencing data and clinical information from the TCGA database. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to analyze the differentially expressed genes (DEGs). A least absolute shrinkage and selection operator (LASSO) regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related LNM related genes signature. Subsequently, a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the risk model and key gene was validated in vitro. Results: In total, 89 LRGs were identified. Based on the LASSO Cox regression, 11 genes were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 3.583e−08). The area under the curve (AUC) of this model was 0.718 at 5 years of OS. Then, we developed an OS-associated nomogram that included the risk score and clinicopathological features. The concordance index of the nomogram was 0.769. The survival verification performed in three subgroups from the nomogram demonstrated the validity of the model. The AUC of the nomogram was 0.787 at 5 years OS. Proliferation and metastasis of HMGB3 were explored in EC cell line. Finally, we revealed that the most frequently mutated genes in the low-risk and high-risk groups are PTEN and TP53, respectively. Conclusions: Our results suggest that LNM plays an important role in the prognosis, and HMGB3 was potential as a biomarker for EC patients. By detecting the mutation of the risk signature, clinicians can accurately treat patients with targeted therapy, thereby improving their survival rate.