Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients
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RESEARCH ARTICLE
Open Access
Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients Pinping Jiang†, Wei Sun†, Ningmei Shen†, Xiaohao Huang* and Shilong Fu*
Abstract Background: Metabolic abnormalities have recently been widely studied in various cancer types. This study aims to explore the expression profiles of metabolism-related genes (MRGs) in endometrial cancer (EC). Methods: We analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated with EC patient prognosis. Functional pathway enrichment analysis of the DE-MRGs was performed. LASSO and Cox regression analyses were performed to select MRGs closely related to EC patient outcomes. A prognostic signature was developed, and the efficacy was validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients’ survival probability. Results: Forty-seven DE-MRGs were significantly correlated with EC patient prognosis. Functional enrichment analysis showed that these MRGs were highly enriched in amino acid, glycolysis, and glycerophospholipid metabolism. Nine MRGs were found to be closely related to EC patient outcomes: CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2 and ACACB. Based on these nine DE-MRGs, we developed a prognostic signature, and its efficacy in part of and the entire TCGA EC cohort was validated. The nine-MRG signature was independent of other clinical features, and could effectively distinguish high- and low-risk EC patients and predict patient OS. The nomogram showed excellent consistency between the predictions and actual survival observations. Conclusions: The MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice. Keywords: Metabolism, TCGA, Endometrial carcinoma, Prognostic model, Nomogram
Background Endometrial carcinoma (EC), one of the most common female reproductive malignancies, caused nearly 90,000 deaths worldwide each year [1]. Women with metabolic disorders, including obesity and diabetes, have a markedly increased risk of developing endometrial cancer. While early-stage endometrial cancer has a favorable * Correspondence: [email protected]; [email protected] † Pinping Jiang, Wei Sun and Ningmei Shen contributed equally to this work. Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
prognosis, nearly 30% of patients are still diagnosed at a late stage, and over 80% of these individuals die in 5 years [2]. In addition, several EC patients present a high risk of cancer progression or recurrence with insensitivity to chemotherapy, which indicates poor outcomes [3]. Therefore, it is imperative to emphasize the molecular changes that occur during endometrial cancer progression and develop novel predictive biomarkers to accuratel
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