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

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 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 accurately estimate patient outcomes.

Since fundamental metabolic differences between cancer and adjacent normal cells were first uncovered, metabolic reprogramming has increasingly become a hot topic in cancer biology [4]. The metabolic phenotype of cancer cells is heterogeneous in various cancer types; for example, while several malignant tumors mainly rely on glycolysis, others present a metabolic phenotype mediated by oxidative phosphorylation [5, 6]. Overall, through reprogramming tumor microenvironments, catabolic and anabolic metabolism is essential for cancer cells to sustain energy supply and biomass synthesis [7‐9]. While the underlying processes and molecular alterations of metabolic programming in various cancers have been well elucidated, the expression patterns of metabolism-related genes in endometrial cancer are still unclear.

In this study, we focused on the metabolism-related gene expression alterations of The Cancer Genome Atlas (TCGA) EC patients and obtained prognostic dysregulated MRGs. In addition, we established and validated a multiple-MRG-combined expression signature for predicting EC patient outcomes. Moreover, we integrated the clinical features of patients and the MRG model to establish a novel nomogram model that could guide comprehensive EC therapeutic strategies.

Metabolic abnormalities have recently been widely studied and shown to play an important role in tumor development in various cancer types. Metabolic dysfunction in the tumor microenvironment could lead to various outcomes in patients, and metabolism-related genes can be used as prognostic markers of tumors. In this work, we thoroughly investigated the implications of metabolism-related genes in endometrial cancer progression. By analyzing the mRNA data of TCGA EC patients, we obtained 220 dysregulated MRGs, among which 47 were associated with EC patients’ OS. Functional enrichment analysis of these prognostic MRGs showed that they were closely associated with the cellular amino acid metabolic process, glycolysis, and glycerophospholipid metabolism. In accordance with our observation, Byrne et al. also found that glycolysis and lipogenesis are highly associated with endometrial cancer phenotypes and that the suppression of GLUT6 gene expression could inhibit glycolysis and the survival of EC cells, underlying the crucial role of energy metabolism in tumor progression [16]. In addition, our results further revealed the exact dysregulated metabolic genes of these disordered metabolism-related pathways, which may provide a new perspective on the molecular mechanisms of metabolism alterations in tumor progression.

Metabolic prognostic risk signatures that combined the expression of multiple metabolism-related genes have been indicated to serve as powerful prognostic indicators in various malignant diseases, such as glioma, liver cancer, ovarian cancer, and papillary thyroid carcinoma. Zhou et al. identified a 29-energy metabolism-related gene signature, containing branched-chain amino acid transaminase 1 (BCAT1), interleukin-4 and carbohydrate sulfotransferases, to evaluate the prognosis of diffuse glioma [17]. Wang et al. enrolled 6 risks and 2 protective metabolic genes into the prognostic metabolic model which effectively predicted ovarian cancer patients’ prognosis [18]. Likewise, Ma et al. developed a metabolic gene signature as a biomarker for dedifferentiated thyroid cancer [19], and Liu et al. built a four-metabolic gene signature for liver cancer patient outcome prediction [20].

In the present study, we performed LASSO and multivariate Cox regression analyses and identified a nine-gene signature including CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2, and ACACB. Among them, HNMT was considered a protective factor while others were risk factors. The diagnostic and predictive effectiveness of these prognostic genes has already been reported in other studies. Cui et al. reported a significantly higher expression of carboxyl ester lipase (CEL) in breast cancer. The combination of CEL and other biomarkers could improve the diagnostic capability for breast cancer [21]. Likewise, Richard et al. found that over 70% of estrogen receptor (ER)-negative breast cancers exhibited elevated phosphoglycerate dehydrogenase (PHGDH) protein expression, which is crucial for promoting serine pathway flux [22]. Li reported downregulation of PHGDH caused by overexpressing LncRNA PlncRNA-1 mediated cell apoptosis rate in breast cancer [23]. In addition, Zhang et al. discovered that PHGDH could define a metabolic subtype in lung adenocarcinomas with unique metabolic dependencies [24]. In pancreatic ductal adenocarcinoma, CYP4F3, one isoform of the cytochrome P450 (CYP) superfamily, was shown to be upregulated in tumor tissues and could serve as a distinguishing marker [25]. Uridine-cytidine kinase 2(UCK2) was positively correlated with early recurrence and poor prognosis in hepatocellular carcinoma. Overexpression of UCK2 increased MMP2/9 expression and further activated Stat3 signaling, mediating the metastasis of hepatocellular carcinoma cells [26]. For ACACB, Lally et al. showed that humans with fatal HCC subtypes have increased acetyl-CoA carboxylase (ACC) expression and that the genetic activation of ACC promoted the formation of hepatic de novo lipids and induced subsequent liver carcinogenesis [27].

Here, through bioinformatic analysis and outside validation, we innovatively reported that these metabolic genes are closely related to the prognosis of EC patients. In addition, the metabolic risk signature combining these genes could accurately categorize EC patients into high- or low-risk subgroups which represented patients’ long-term outcomes. Last, our study was the first to build a comprehensive nomogram that incorporated a metabolism-related signature with clinical features including age, stage, tumor grade and lymph node status to effectively predict the survival of EC patients. This prognostic scoring system could provide a precise method to help both physicians and patients perform individualized survival evaluations and select treatment options.

In conclusion, we identified 47 prognosis-related dysregulated metabolic genes in EC. The prognostic DE-MRGs were highly associated with amino acid, glycolysis, and glycerophospholipid metabolism. The top 15 hub genes in the PPI network were also identified and analyzed. We performed LASSO and multivariate Cox regression analyses to establish and validate a robust prognostic risk signature enrolling the nine dysregulated MRGs. In addition, a comprehensive nomogram that combined clinical characteristics and the risk model was constructed, and its efficacy in predicting EC patients’ prognosis was also demonstrated. The 9-MRG model and nomogram may guide the selection of rational therapeutic strategies for doctors in clinical practice.