Objective: To investigate the expression and prognostic value of neural-related genes (NRGs) in endometrial cancer (EC). Design: Bioinformatics Analysis. Setting: Bioinformatics Database. Population or sample: Sample with endometrial cancer. Methods: We classified endometrial cancer cases into two subgroups based on NRGs expression and evaluated the differences between the two subtypes. A prognostic prediction model was established by LASSO-Cox analysis to screen for prognosis-associated genes. Main outcome measures: overall survival (OS), enriched pathways, correlation analysis of clinical features, immune cell infiltration, immune response, tumor mutation burden (TMB). Results: EC was classified into two subtypes based on the expression of NRGs, and significant variations in clinical staging, pathological grading, and immune regulation were found between the two subtypes. The prognostic model revealed that increased NRGs expression was linked to a poor prognosis in endometrial cancer patients. Conclusions: In endometrial cancer, the genes CHRM2, GRIN1, L1CAM, and SEMA4F were found to be strongly linked to clinical stage, immune infiltration, immune response, and key signaling pathways. The genes CHRM2, GRIN1, L1CAM, and SEMA4F may serve as potential biomarker for endometrial cancer prognosis. Funding: This work was in part supported by Innovation and Entrepreneurship Talent Project of Lanzhou (2020-RC-52).