A Novel Prognostic Model of Endometrial Cancer Based on Inflammation and Lipid Metabolism Genes

Background: Endometrial cancer (EC) is a malignancy of the inner epithelial lining of the uterus, with an increasing incidence and disease-associated mortality worldwide. Inflammation and lipid metabolism contribute to EC risk. Materials and Methods: Differential expression genes (DEGs) in EC and normal samples were analyzed based on the TCGA-UCEC database, and DEGs associated with inflammation and lipid metabolism were screened out to be candidate genes. Prognosis-related genes were analyzed using Cox regression and LASSO regression, and a prognostic model was established. Receiver operating characteristic curves and Kaplan-Meier survival analysis were performed to assess the predictive performance of the prognostic model. Gene Set enrichment analysis, immune infiltration analysis, and gene set variation analysis were performed. Expression of prognostic genes in local tissues was examined by Reverse Transcription Quantitative PCR (RT-qPCR) and immunohistochemistry. Methylthiazolyldiphenyl-tetrazolium bromide assay, migration assay, and wound-healing assay were applied to examine the role of CKMT1B on cell proliferation and migration in EC cell lines. Results: A prognostic model based on six prognosis-related genes (CKMT1B, NTS, NSG2, H3C1, MAL, ELOA2) was established in EC, and this model had a favorable predictive performance. Respective different pathways and immune cell infiltration were associated with prognostic genes. 5/6 prognostic genes were highly expressed in local EC tissues compared with normal tissues. Knockdown of CKMT1B significantly suppressed cell proliferation and metastasis in EC cell lines. Conclusion: CKMT1B, NTS, NSG2, H3C1, MAL, and ELOA2 (especially CKMT1B) were important factors in human EC and could be potentially used for risk stratification and prognosis prediction in EC.