Introduction
Endometrial carcinoma (EC) is the second most common gynecological cancer in women worldwide [
1]. Its prevalence and mortality are both rising annually, which poses a severe threat to women’s health [
1]. The increasing incidence of EC is closely related to the increasing incidence of obesity and diabetes worldwide [
2]. Studies in epidemiology have demonstrated that obesity constitutes an independent risk factor for EC, with a positive correlation observed between body mass index (BMI) and the incidence of this malignancy [
3]. Moreover, metabolic disorders such as diabetes mellitus are related to the incidence and adverse pathological features of EC [
4]. The investigation of EC development, particularly the tumor metabolism mechanism, has emerged as a prominent research focus in molecular targeted therapy for EC [
5]. Further elucidation of the molecular mechanisms underlying EC in metabolism holds significant clinical therapeutic implications.
The nutrients glucose (carbohydrate) and lipids play crucial roles in the body, closely intertwined with energy storage and supply, and are integral components of cellular metabolic processes [
6]. The process of tumor development involves the reprogramming of glycometabolism (carbohydrate metabolism) and lipid metabolism, which is intricately linked to tumor progression, invasion, metastasis, and immune modulation [
7,
8]. Glycolysis is the predominant pathway of glycometabolism in the human body. Tumor cells exhibit heightened glucose consumption to rapidly generate sufficient ATP for energy via glycolysis. Even under conditions of adequate oxygen availability, there is a propensity for glucose conversion into lactic acid, known as the Warburg effect [
9]. Consequently, the augmented glycolytic activity of malignant tumor cells can induce an acidic microenvironment surrounding them, fostering normal cell death while facilitating tumor cell angiogenesis and invasion [
10]. The metabolic characteristics of the glycometabolism pathway and mitochondrial function exhibited significant alterations in EC [
11]. Lipids play crucial roles in the composition of cellular membranes, energy metabolism, and synthesis of endocrine hormones [
12]. The preservation of cell membrane structure and facilitation of cell signal transduction are critically dependent on cholesterol and phospholipids [
13]. Lipids and their metabolic intermediates play a pivotal role in diverse cellular signal transduction pathways implicated in cancer [
14]. The dysregulation of lipid metabolism represents a pivotal metabolic alteration in the context of cancer. Lipid metabolism disorder can result in dysregulated expression of various genes and proteins, as well as perturbed cytokine profiles and disrupted signaling pathways [
15]. Despite the correlation between the incidence and development of EC with glycometabolism and lipid metabolism [
16‐
18], there remains uncertainty regarding the molecular processes involved and the impact of associated genes on EC patient prognosis. Additionally, current research on abnormal glycometabolism and lipid metabolism in EC patients mainly consists of single-gene laboratory studies, with little exploration into gene clusters associated with these metabolic processes.
The implementation of comprehensive treatment strategies has significantly enhanced the overall prognosis of patients with EC; however, individuals experiencing recurrent and metastatic EC exhibit a dismal survival outlook under the current therapeutic regimen [
19]. Moreover, currently there is a lack of robust biomarkers or predictive models that can accurately forecast the survival rate of patients with EC [
20]. The conventional methods for tumor histology and morphology classification fail to comprehensively capture the heterogeneity among EC cells and patients, leading to inadequate repeatability [
21‐
23] and significant variations in prognosis even within the same stage of EC [
24,
25]. Reclassification of tumors based on various criteria enables the classification of patients, thereby facilitating their management, treatment, and follow-up. The conventional classification of EC is important for diagnosing and treating patients. However, as disease research progresses, the limitations of traditional methods become more evident. Conventional classification heavily depend on clinical or histological features without fully considering genomic characteristics, which can lead to varying outcomes among patients with the identical EC classifications [
26‐
29]. Moreover, there is often an overlap between tissue type and FIGO grade, making it challenging to address tumor heterogeneity and resulting in subjective diagnoses that complicate clinical decision-making. Multiple factors, encompassing genomic and clinical aspects, play a pivotal role in the development and prognosis of endometrial cancer; however, the existing classification system inadequately predicts the survival outcome of patients affected by this malignancy [
30].
Due to the limitations of conventional classification and basic experiments, numerous bioinformatics analysis techniques have been extensively employed for the identification and characterization of genes associated with the progression of diverse cancer types. Our previous studies have focused on the domain of biomarker screening and bioinformatics analysis employing high-throughput sequencing technology from public databases [
31,
32]. The emergence of pathogenic abnormalities stems from intricate network relationships among genes [
33]. Therefore, in our study, we identified ten glycometabolism and lipid metabolism-related genes (GLRGs) from the Genecards database to construct a reliable prognostic signature for predicting overall survival (OS) and treatment strategies. Our data demonstrated a significant correlation between the GLRG-related prognostic signature and immune features, tumor mutational burden (TMB), as well as chemosensitivity. Furthermore, we successfully validated the impact of this representative gene model on EC cells in vitro.
Material and methods
Data collection for EC
We downloaded the processed data containing RNA sequences and clinical information (such as prognostic information) from The Cancer Genome Atlas (TCGA) (
https://portal.gdc.cancer.gov/, accessed on 5 July 2023) [
34] and the Genotype-Tissue Expression (GTEx) project (
https://www.gtexportal.org/home/, accessed on 5 July 2023) [
35]. The primary EC patients with insufficient clinical data and follow-up information were excluded, resulting in the inclusion of 545 primary EC patients and 78 samples from healthy individuals. According to gene annotation information in GENCODE, the Ensemble Gene was transformed into a Gene Symbol [
36]. The Genecards database (
https://www.genecards.org/, accessed on 6 July 2023) was utilized to retrieve a total of 714 GLRGs (Supplementary Table S
1) [
37]. Using the Maftools package, the somatic mutations of mRNAs were created using the Mutation Annotation Format (MAF) [
38]. We obtained EC, or normal control endometrial tissues, following the approval of the ethics committee at Fujian Cancer Hospital.
Differential and prognostic analysis of GLRGs
We screened differentially expressed GLRGs using limma package (
P<0.05 and |logFC|>1) [
39]. The volcano plot for differentially expressed GLRGs was generated using the ggplot2 package. The Survminer package [
40] was utilized to identify the optimal cut-point and select prognostic GLRGs based on the expression level of differential GLRGs, survival time, and state.
Establishment of the GLRG-related cluster and signature
Based on the differentially expressed GLRGs with prognostic value, EC patients were grouped using the non-negative matrix factorization (NMF) clustering algorithm [
41]. By leveraging expression levels of individual GLRGs and employing Least Absolute Shrinkage and Selection Operator (LASSO) regression prognostic coefficient [
42], we have developed a risk-score (RS) model as follows: Risk Score =∑β
gene×Exp
gene. In the RS formula, β
gene is the LASSO regression coefficient of the GLRG, and Exp
gene signifies the expression level of the GLRG. The RS of each EC patient was calculated, and the median RS was used as the critical value to further divide the EC patients into high-risk group and low-risk group (high-risk group: RS≥median; low-risk group: RS < median). Considering the sample size and referring to previous literature [
43,
44], the total samples (Total Set,
n=545) were randomly divided into a Train Set (
n=273) and a Test Set (
n=272) in a one-to-one ratio using the random sampling function in the R programming language to minimize information leakage and enhance model performance evaluation accuracy. Initially, the risk scores for patients in the Training Set were computed using the aforementioned formula. Subsequently, we employed the same methodology to calculate the risk scores of both the Testing Set and Total Set for validation purposes. The Kaplan-Meier curves were utilized to evaluate the survival outcomes of different risk groups. To assess the GLRG-related signature’s prediction power, we created ROC curves using the timeROC program. The autonomous prognostic relevance of the linked components, such as RS and clinical characteristics, was confirmed by the Univariate and Multivariate Cox regression models. The calibration curve was used to confirm the construction of the visual nomogram and assess the risk model’s accuracy for use as a stand-alone prognostic factor [
45]. Besides, principal component analysis (PCA) [
46] and t-distributed stochastic neighbor embedding (t-SNE) [
47] were applied to test the effectiveness of the risk scores in differentiating EC patients.
Enrichment analysis of candidate genes
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out utilizing the ClusterProfler package [
48]. Gene Set Variation Analysis (GSVA) was conducted utilizing the GSVA package [
49]. The Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis was conducted to compare the two different groups [
50].
Evaluation of immune activity and therapy for the two GLRG-related risk groups
The immunological microenvironment has a significant role in the origin and evolution of EC. An evaluation of the immune-infiltrating cell abundance was conducted using the IOBR algorithm [
51]. We utilized the TIDE algorithm to predict potential responses to immune checkpoint blockade (ICB) [
52]. We employed the web platforms GenePattern and Submap to conduct a comparative analysis of immunotherapy disparities between the two risk groups [
53]. The oncoPredict package [
54] was utilized to evaluate the IC50 levels of chemotherapy drugs for patients with EC.
Screening significant GLRGs in EC using machine learning-dependent integrative approaches
The most significant GLRGs among the GLRGs in the GLRG-related signature were further screened out by integrating 12 machine learning (ML) algorithms and combining 113 algorithm combinations [
55] to authenticate GLRGs with high accuracy and stability between EC and normal samples. As previously mentioned, considering the sample size and referring to previous literature [
43,
44], the total samples (All Set) were randomly and equally partitioned into a Train Set and a Test Set. The same Train Set was utilized to construct signatures among the 113 algorithms, and subsequently, the validation of these signatures was performed based on the calculation results obtained from both the same Test Set and All Set. The frequency of GLRGs observed in the 113 algorithm combinations was computed, and subsequently, the GLRG with the highest occurrence rate was chosen for subsequent wet experiments.
Cell lines and cell culture
The endometrial epithelial cells (EECs) were preserved in our laboratory. The extensively employed EC cells, HEC-1A and Ishikawa, were obtained from the Meisen Chinese Tissue Culture Collections (Zhejiang, China) and preserved in our laboratory. McCoy’s 5A medium (Shanghai BasalMedia Technologies Co., Ltd., Shanghai, China) mixed with 10% fetal bovine serum (FBS) was used to cultivate HEC-1A cells. RPMI 1640 medium (Shanghai BasalMedia Technologies Co., Ltd.) containing 10% FBS was used to cultivate Ishikawa cells and EECs. The cell culture medium was supplemented with streptomycin at a concentration of 100 g/mL and penicillin at a concentration of 100 U/mL (Beyotime, Beijing, China). Cells were grown at 37°C in a 5% CO2 environment.
Quantitative real-time PCR
The extraction of total RNA was performed using TRNzol Universal Reagent (Tiangen Biotech, Beijing, China). Reverse transcription was performed using FastKing gDNA Dispelling RT SuperMix (Tiangen Biotech, Beijing, China). The reaction was conducted at a temperature of 42°C for a duration of 15 minutes to facilitate genome removal and reverse transcription, followed by an enzyme inactivation step at 95°C for 3 minutes. SuperReal PreMix Plus (Tiangen Biotech, Beijing, China) was utilized for quantitative real-time PCR (qRT-PCR). The initial denaturation step was performed at 95°C for 15 minutes, followed by a total of 40 cycles consisting of denaturation at 95°C for 10 seconds and annealing/extension at 60°C for 32 seconds in the RT-qPCR cycling conditions. The melt curve stage was programmed as follows: 95°C for 15 seconds, 60°C for 60 seconds, 95°C for 15 seconds, and 60°C for 10 seconds in accordance with the instructions provided in the kit. The primer sequences utilized in this study can be found in Supplementary Table S
2. The β-actin PCR product, with the size of 248 bp, exhibited a homogeneous melting temperature of approximately 87.5 °C. The HK2 PCR product, with the size of 87 bp, displayed the homogeneous melting temperature around 83 °C. The expression levels were calculated using the 2
-ΔΔCt formula.
Cell transfection for EC cells
Observing the guidelines provided by the manufacturer, small interfering RNAs (siRNA) targeting HK2 and negative control RNAs (GenePharma, Shanghai, China) were employed for silencing HK2 expression using a GP-transfect-mate (GenePharma, Shanghai, China) transfection reagent. The specific sequences for siRNA can be found in Supplementary Table S
3.
Western blot assays
The total proteins were extracted using RIPA buffer supplemented with a proteinase inhibitor cocktail (Beyotime, Beijing, China). Utilizing the BCA assay kit (Beyotime, Beijing, China), the protein content was determined. Polyacrylamide gel electrophoresis (SDS-PAGE) with the addition of sodium dodecyl sulfate was employed to separate the proteins, followed by their transfer onto the polyvinylidene fluoride membranes made from PVDF. After an hour of room temperature blocking with blocking solution, the membranes were incubated for an entire night at 4°C with primary antibodies targeting HK2 (dilution 1:5000, Cat No. 66974-1-Ig, Proteintech, China) and β-Actin (dilution 1:5000, Cat No. 81115-1-RR, Proteintech, China). Following a wash, the membrane was left at room-temperature for an hour to be incubated with the secondary antibody (dilution 1:10000, ZB-2306, ZSGB-BIO, China). An improved chemiluminescent substrate was used to identify protein bands.
CCK-8 assays for EC cells
After transfection, the transfected cells were cultured for four days in 96-well plates with a seeding density of 2000 cells/well and 100 μl of complete growth medium. The Counting Kit-8 (APExBIO, Houston, TX, USA) was utilized to measure the optical density (OD) at 450 nm using a computerized microplate reader at time points of 0-hour, 24-hour, 48-hour, 72-hour, and 96-hour in accordance with instructions. The groups were equipped with three multiple wells each and each trial was repeated in triplicate.
Scratch wound-healing assays for EC cells
Upon reaching approximately 100% confluency on the 6-well plates, the EC cells were gently scraped with a 200µl pipette tip, and the suspended cells were subsequently removed with PBS. Afterwards, the EC cells were cultured in a serum-free medium at 37°C with 5% CO2. Subsequently, three non-overlapping views of each well were randomly captured at both the 0-hour and 48-hour time points. The pictures were imported into Image J, an image processing program, and examined. The following formula calculates the rate of wound healing: wound healing rate = (A0h–A48h)/A0h×100%, where A0h is the initial wound area and A48h is the wound area left at the end of the healing process.
Transwell assays for EC cells
The Transwell assays were conducted using chambers (pore size, 8 µm) equipped with polycarbonate filters. In the upper compartment, 2×105 EC cells were put in 300 μL of serum-free medium, while the lower chamber contained 500 μL of McCoy’s 5A media supplemented with 30% FBS. For the invasion experiment, Matrigel was applied to the chamber. The EC cells were cultured for 48 hours at a temperature of 37°C in an environment containing 5% CO2. The cells that were unable to pass through the transwell were removed using cotton swabs. Following a ten-minute fixation in 4% paraformaldehyde, the cells that migrated to the lower chamber were subsequently stained with 0.1% crystal violet for five minutes. Finally, cell quantification on the lower surface was calculated using a microscope.
In 6-well culture plates, 500 cells were added to each well for a 14-day inoculation period. When discernible clones appeared, the supernatant was removed. After being cleaned twice with PBS, it was fixed for 15-20 minutes by adding 1 mL of 4% paraformaldehyde fixative. After removing the supernatant, the sample was subjected to two rounds of PBS washing. Subsequently, 1 mL of crystal violet staining solution was added and incubated for a duration of 15 to 20 minutes. The culture plates were carefully rinsed with tap water, allowed to dry naturally, and then photographed, with the number of clones that could be seen being tallied.
Apoptosis assays for EC cells
The apoptosis assays were carried out according to the instructions of the manufacturer (MedChemExpress, NJ, USA). Following transfection, 195 μl of binding buffer was used to resuspend the cells after they had been cleaned with PBS and digested with trypsin without EDTA. Subsequently, the cells were stained with 10 μl of Annexin V-FITC and 5 μl of propidium iodide (PI), followed by a dark incubation at room temperature for 15 minutes. The apoptosis of cells was quantified using a flow cytometer (BD Biosciences, New York, USA).
EdU cell proliferation assays for EC cells
HK2 knock-down and the matching control EC cells were seeded at a density of 2×105 cells/well in 24-well plates and cultivated for 24 hours. The assessment of cell proliferation was conducted using the EdU analysis kit (APExBIO, Houston, TX, USA) in accordance with the provided instructions.
Statistical analysis of this study
The statistical analysis was conducted using the R package (v 4.0.2) or GraphPad Prism (v 9.0). The assay was performed in triplicate and the results were presented as Mean ± SD. The normality of data distribution was analyzed by normality tests (D’Agostino-Pearson omnibus, Anderson-Darling, Shapiro-Wilk and Kolmogorov-Smirnov) provided by GraphPad Prism. The survival outcome between two subgroups was compared using Kaplan-Meier curves and the log-rank test. The Spearman correlation coefficient was employed to ascertain the association between the two variables. The two-tailed Student’s t-test was employed for comparing data between two groups, while the one-way ANOVA test was utilized for analyzing data from more than two groups. Additionally, specialized analyses were detailed in the corresponding section. The threshold for determining a statistically significant difference was set at P<0.05, adhering to the conventional criterion.
Discussion
Aberrant glycometabolism and lipid metabolism can exert diverse influences on the development and progression of EC. For instance, elevated blood glucose levels can elicit insulin resistance, a condition characterized by reduced sensitivity of insulin-responsive tissues to insulin, leading to elevated concentrations of both insulin and glucose in the bloodstream. Given that insulin serves as the primary anabolic hormone in the body and exerts its influence on cell proliferation [
56], thereby initiating a range of physiological processes associated with carcinogenesis, numerous studies have established a correlation between insulin resistance, incidence of EC, and diverse biological mechanisms [
7,
57,
58]. The development of insulin resistance can be induced not only by hyperglycemia but also by obesity, potentially through the promotion of chronic inflammation in adipose tissue and an increase in systemic insulin secretion [
59]. Obese individuals exhibit a substantial augmentation in adipose tissue, which actively secretes hormones and adipokines such as leptin and adiponectin. Reduced levels of leptin, elevated circulating adiponectin, and an increased ratio of adiponectin to leptin have been associated with a decreased risk of EC [
60]. Another study demonstrated a positive correlation between elevated leptin levels and the progression of EC [
61], and cisplatin may potentially exert its therapeutic effects on EC through modulation of the leptin pathway [
62]. As previously mentioned, numerous contemporary studies are currently focusing on investigating the impact of aberrant glycometabolism and lipid metabolism on EC. Numerous physiological and pathological processes in vivo exhibit intricate associations with gene expression and its regulation. However, there is currently no established and accurate predictive signature associated with glycometabolism and lipid metabolism-related genes (GLRGs) for prognosticating the outcomes of patients with EC and guiding treatment decisions.
A comprehensive consensus on the risk factors influencing the prognosis of EC patients is yet to be established. In this study, we conducted a comprehensive analysis of the expression profiles of 714 GLRGs in EC using data obtained from the Genecards and TCGA databases. Consequently, we identified differentially expressed GLRGs exhibiting significant prognostic value. The prognostic signature of GLRGs was further established using LASSO Cox regression analysis and subsequently validated for its independent prognostic value. The conventional tumor histology and morphology classification methods fail to capture the full extent of heterogeneity among tumor cells and patients, resulting in poor repeatability and significant variations in prognosis for the same type of tumor. Recent advancements in molecular research on EC have unveiled the genomic alterations associated with its presence, thereby offering valuable insights into the pathogenesis of the disease. Molecular testing holds significant potential in the early detection of EC or precursor lesions and in guiding individualized treatment strategies for EC [
63]. The novelty of our study lies in its integration of genes associated with glycometabolism and lipid metabolism, thereby enhancing the precision and generalizability of the predictive model. Based on the GLRG-related signature, patients with EC were stratified into high-risk and low-risk groups. The overall survival (OS) of high-risk EC patients was significantly inferior to that of the low-risk group, even after accounting for clinical factors, as demonstrated across the Train, Test, and Total sets. The accuracy of the risk model in predicting survival status was further validated by consistent findings pertaining to progression-free survival (PFS). The time-varying ROC curve exhibited the resilience of the prognostic GLRG-related signature in accurately predicting survival rates at intervals of 1 year, 3 years, 5 years, 7 years, and 9 years for EC patients across all three sets - Train Set, Test Set, and Total Set. In addition, our study revealed a strong association between higher risk scores, calculated based on the GLRG-related signature, and advanced age, higher stages and grades, as well as an elevated rate of lymph node metastasis overall in patients with EC. The risk score’s C-index and AUC exhibited superior performance in comparison to other clinical features (Age, Grade, Stage, Cluster, and LNM), thereby indicating a stronger predictive power and higher confidence in the GLRG-related signature. Furthermore, the Multivariate Cox regression analysis revealed that the GLRG-related signature exerts an independent influence on the prognosis of patients with EC. Therefore, the GLRG-related signature established in our study facilitates risk assessment, risk stratification, and prognostication for EC patients based on their individual characteristics, thereby equipping clinicians with a valuable tool to evaluate the prognosis of EC and develop appropriate follow-up strategies.
In clinical practice, the implementation of tailored treatment approaches for stratified patients is crucial in enhancing patient prognosis. Tumor immunotherapy represents a therapeutic approach aimed at inducing immune system activation and preventing tumor cells from evading immune surveillance [
64]. The challenges of immunotherapy for EC persist in terms of limited response rates, incidence of adverse reactions, and the inability to predict individual efficacy [
65]. The immune microenvironment exhibited notable disparities between the two risk groups distinguished by the GLRG-related signature overall. However, no discernible disparity in immunotherapy responses was observed between the two risk groups based on GLRG. Interestingly, the IC50 levels of six commonly employed chemotherapeutic agents were found to be significantly lower in EC patients belonging to the low-risk group, suggesting a heightened sensitivity towards these drugs within this particular subgroup. Among them, Cisplatin exerts inhibitory effects on the DNA replication of EC cells, leading to detrimental consequences on the structural and functional integrity of DNA [
62,
66]. Paclitaxel exerts its effects by promoting the inhibition of tubulin polymerization and maintaining tubulin stability, thereby further suppressing tumor cell proliferation [
67]. Tamoxifen, a nonsteroidal anti-estrogen drug, exerts its inhibitory effect on the proliferation of EC cells by competitively binding to estrogen receptors and antagonizing estrogen signaling [
68]. Regardless of the stage of endometrial cancer, monotherapy has limited efficacy, necessitating adjuvant therapy and combination chemotherapy regimens to enhance treatment outcomes and prognosis. Therefore, our study also holds significant implications for guiding clinical drug utilization.
In order to promote the application of our established risk model, we conducted in vitro experiments on the optimal gene (HK2) within the GLRG-related signature. The rate of glycolysis is primarily regulated by hexokinase, which serves as the initial limiting enzyme in this metabolic pathway. This family of exoglucose-phosphorylase enzymes exhibits a wide distribution across various organisms. The most active isoenzyme within this enzyme family, hexokinase 2 (HK2), plays a pivotal role in glucose metabolism [
69]. According to previous studies, HK2 exhibits high expression levels in various malignancies [
70], including breast [
71], liver [
72], colorectal [
73], pancreatic [
74], and cervical cancers [
75]. The heightened glycolytic rate of tumor cells can be attributed to their elevated expression of HK2 [
76]. The inhibition of HK2 knockdown not only impeded the tumorigenic growth of glioblastoma, medulloblastoma, and renal cell carcinoma [
77‐
79], but also demonstrated anti-angiogenic effects in pancreatic cancer cells [
80]. Numerous inhibitors of HK2 have been developed, including the competitive inhibitor 2-deoxyglucose (2-DG) and the catalytic inhibitors 3-bromopyruvate (3-BrPyr) and clonidamine. In various in vitro and in vivo tumor models, multiple compounds effectively target HK2, inducing its dissociation from mitochondria and subsequently initiating apoptosis in tumor cells [
81]. The treatment of type 2 diabetes involves metformin binding to the HK2 and G-6-P sites, resulting in the dissociation of HK2 from mitochondria and facilitating cellular apoptosis. The utilization of metformin as a pivotal preoperative intervention for EC has been extensively employed in the field, effectively restoring atypical endometrial hyperplasia to normal endometrial tissue [
82], mitigating the risk of EC, and enhancing the prognosis of patients with EC [
83]. Moreover, mitochondrial HK2 governs glycolysis and regulates levels of reactive oxygen species (ROS), while also participating in Ca
2+ signaling and homeostasis to effectively regulate the energetic survival of cellular organisms [
84]. However, the comprehension of HK2 in EC remains limited. Herein, we employed qRT-PCR and WB techniques to validate the robust expression of HK2 in EC tissues. The suppression of HK2 effectively attenuated the proliferation, migration, and invasion of EC cells. The prognostic analysis revealed a significant positive correlation between elevated HK2 expression levels and unfavorable patient outcomes. Our study suggests that HK2 can serve as a reliable biomarker for the diagnosis and prognostic prediction of EC, while targeted regulation of HK2 holds promising potential as a therapeutic intervention for managing EC.
Although there have been published articles specifically examining the prognostic characteristics of EC [
85], their focus has been exclusively limited to one aspect of metabolism. The present study constitutes the initial endeavor to integrate genes linked with glycometabolism and lipid metabolism. Nevertheless, it is necessary to delineate the existing limitations within this study. The accuracy of the GLRG-related risk model in predicting prognosis has been validated using the TCGA database; however, further collection of EC samples, external independent datasets, and extensive prospective clinical analysis are necessary to validate the effectiveness and utility of the GLRG-related signature and biomarkers in clinical applications. The correlation between the GLRG-signature and drug susceptibility necessitates further validation through clinical trials and molecular biology experiments. Other clinical factors, such as lymphovascular space invasion (LVSI), have been identified as significant prognostic indicators in EC. However, due to limited data availability, the inclusion of LVSI in this study is currently unfeasible. Additionally, the underlying mechanism through which HK2 facilitates EC progression remains elusive, and our study has solely undergone experimental validation in vitro. A comprehensive understanding of the potential mechanism of HK2 necessitates extensive exploration and experimentation in vivo.
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