Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients

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.

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

“…However, hub genes previously identified usually contained only biomarkers or several genomic markers without clinical features, restricting the clinical applicability of these biomarkers and decreasing the specificity of the overall prognostic models. Even though a similar metabolism-related risk model including nine genes associated with prognosis of EC was constructed in one study recently ( 51 ). There were still many differences between the two studies.…”

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

“…We compared the prediction performance of the novel immune-related pseudogene signature with four recently published signatures: nine-lncRNA signature (LINC02387, FUT8-AS1, UBXN10-AS1, LINC00473, AL353194.1, FAM222A-AS1, AP002761.3, AL731566.2, and AP001021.2) derived from Xu’s research [ 9 ], nine-mRNA signature (CYP4F3, LYPLA2, CEL, PHGDH, GPAT3, HNMT, UCK2, CKM, and ACACB) derived from Jiang’s study [ 32 ], nine-mRNA signature (TP53, RAE1, RFC2, TAF10, DDB2, UMPS, TAF12, ERCC2, SEC61A1) derived from Liu’s research [ 33 ], and five-lncRNA signature (AL121906.2, BOLA3-AS1, LINC01833, AC016405.3, and RAB11B-AS1) from Jiang’s study [ 34 ] using the same TCGA EC patient cohort. As illustrated in Fig.…”

Lipid Droplet-Associated Factors, PNPLA3, TM6SF2, and HSD17B Proteins in Hepatopancreatobiliary Cancer

“…GPAT2 is highly expressed in several human cancers such as melanoma, lung, prostate, and breast cancer [12]. GPAT3 expression is associated with a shorter overall survival of endometrial cancer patients [13].…”

“…GPAT2 is highly expressed in severa cancers such as melanoma, lung, prostate, and breast cancer [12]. GPAT3 expr associated with a shorter overall survival of endometrial cancer patients [13]. LPA is converted into phosphatidic acid (PA) via the acylglycerolphosph transferase (AGPAT) family (Figure 1).…”