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 and characterization of metabolic subtypes of endometrial cancer using systems-level approach

“…This intersection of metabolic pathways may facilitate the balance between the production of nucleotides and antioxidant species, which support a high proliferation rate and provide defense against oxidative stress [44]. Accordingly, we also observed glycolytic genes to be upregulated in EC, which is consistent with previous studies [17,42]. The candidate genes of PPP include TKTL1, which is upregulated in cluster-2 samples.…”

M2‐phenotype tumour‐associated macrophages upregulate the expression of prognostic predictors MMP14 and INHBA in pancreatic cancer

“…Therefore, there is an urgent need to find new markers to provide new ideas for the classification and treatment of pancreatic cancer and improve patient prognosis. Currently, several research teams have constructed prognostic risk models based on different molecular characteristics such as metabolism, 21 cell cycle 22 and chemokines receptors 23 in order to predict the efficacy of treatment and the prognosis of cancer patients. Since tumour evolution is a complex process, such multigene‐based prognostic risk models are more accurate in prediction the prognosis of cancer patients than single gene prediction.…”

The Pyroptosis-Related Signature Predicts Prognosis in Uterine Corpus Endometrial Carcinoma

“…The establishment of a prognostic evaluation model, involving gene expression levels and clinical characteristics, is a promising and valuable research direction [35][36][37][38] . For UCEC, studies have shown that the prognostic evaluation models based on metabolism-related, immune-related, and variable splicing-related genes can be used to predict the prognosis of UCEC [39][40][41][42] . The prognostic evaluation models based on PRGs have been used to predict the prognosis of postoperative patients with head and neck squamous cell carcinoma, lung adenocarcinoma, gastric cancer, and melanoma, thereby demonstrating their clinical value [43][44][45][46] .…”