Unveiling the Role of SLC2A1 and MPST in Uterine Corpus Endometrial Carcinoma: Diagnostic and Prognostic Insights

Unveiling the Role of SLC2A1 and MPST in Uterine Corpus Endometrial Carcinoma: Diagnostic and Prognostic Insights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling the Role of SLC2A1 and MPST in Uterine Corpus Endometrial Carcinoma: Diagnostic and Prognostic Insights Xiaoyu Xi, Xinxin Gong, Yixi Liu, Boran Cui, Chenchen Xia, Jiexian Du, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3876179/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Uterine corpus endometrial carcinoma (UCEC) represents the prevailing neoplasm affecting the female reproductive system. The early diagnosis of UCEC is crucial for improving the survival rate of patients. In this study, we study the two specific genes: SLC2A1, which encodes the facilitated glucose transporter, and MPST, which encodes 3-mercaptopyruvate sulfurtransferase. SLC2A1 and MPST have been identified as important regulators in cancer. Nevertheless, it is still unknown how SLC2A1 and MPST function and operate within endometrial cancer. The objective of this study is to investigate the potential significance of SLC2A1 and MPST in terms of diagnosis and prognosis for UCEC. Methods Using data from the TCGA database, we analyzed the levels of expression for SLC2A1 and MPST in 33 various cancer types. Then we created a protein-protein interaction (PPI) network that incorporated SLC2A1, MPST, and relevant genes.Furthermore, we performed KEGG/GO pathway enrichment analysis on these genes. We utilized Spearman correlation analysis to examine the correlation between SLC2A1 and MPST expression and the infiltration of immune cells, as well as the association between immune checkpoint genes and TP53. We analyzed DNA methylation changes in the SLC2A1 and MPST genes and their impact on survival outcomes. We investigated the correlation between SLC2A1 and MPST expression and clinicopathological features of patients with endometrial cancer Additionally, we evaluated the diagnostic and prognostic predictive capabilities of SLC2A1 and MPST. Results In the tumor tissues, MPST and SLC2A1 expression levels increased significantly. Our research revealed a noteworthy association between the levels of expression of SLC2A1 and MPST, and the infiltration of immune cells, the presence of immune checkpoint genes, and TP53 in UCEC tissues. Furthermore, there was a remarkable association between the expression levels of SLC2A1 and MPST and the clinical stage, histological type, and histological grade in UCEC tissues. Our analysis using Kaplan-Meier survival curves and diagnostic subject operating characteristics (ROC) curves revealed that both SLC2A1 and MPST exhibit robust diagnostic and prognostic significance. Conclusions The study we conducted emphasizes the diagnostic and prognostic potential of SLC2A1 and MPST as biomarkers for UCEC. These findings offer encouraging prospects for targeted therapies. SLC2A1 MPST UCEC clinical outcome immune cell infiltration immune checkpoint methylation gene mutation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction The occurrence of uterine corpus endometrial carcinoma (UCEC), a common type of gynecological cancer, is steadily rising worldwide(1). In recent years, the rising prevalence of obesity, lifestyle changes, and increased use of estrogen replacement therapy have contributed to the significant increase in UCEC cases, posing a substantial health risk to women(2). Currently, surgery is the primary treatment option for UCEC. However, it is crucial to select the appropriate adjuvant treatment based on the tumor’s pathology and clinical stage. Therefore, identifying suitable gene targets for UCEC treatment and finding reliable diagnostic and prognostic indicators are essential. These advancements present an opportunity to explore novel immunotherapy strategies. After nitric oxide and carbon monoxide, hydrogen sulfide (H2S) has been recognized as the third gas signaling molecule. It is found abundantly in mammals. Endogenous H2S is primarily produced by enzymes such as cystathionine-synthase (CBS), cystathionine-lyase (CSE), and 3-mercaptopyruvate sulfurtransferase (3-MST)(3). The enzyme CSE is encoded by the SLC2A1 gene, while 3-MST is encoded by the MPST gene. CBS, on the other hand, is encoded by the cystathionine beta-synthase gene. The gene SLC2A1 is responsible for encoding a protein that is essential for the functioning of cellular energy metabolism pathways(4–6). It is predominantly expressed in endothelial and trophoblastic cells, making it highly significant in these cellular contexts. As a facilitative glucose transporter, SLC2A1 is responsible for the continuous, or basal, uptake and transport of glucose(7). The glucose transporter proteins (GLUTs) are crucial membrane proteins responsible for mediating the transmembrane transport of glucose, maintaining cell energy supply, and supporting normal cellular functions. The overexpression of SLC2A1 in different types of cancer, including breast, lung, liver, endometrial, oral, and gastric cancer(4, 8–12), is particularly intriguing. Nevertheless, the role of SLC2A1 in UCEC is still not fully understood. MPST is a crucial enzyme that regulates the biosynthesis of endogenous hydrogen sulfide (H 2 S) and is expressed in various human tissues. Activation of MPST is involved in important processes such as tRNA sulfuration, protein aminoacylation, and cyanide detoxification(13). The experimental results indicate that MPST has an important function in providing protection against oxidative stress, overseeing the functioning of mitochondria in respiration, and managing the metabolism of fatty acids(13). Furthermore, research has shown that MPST is present in endometrial tumors, indicating a direct connection with the survival rate of patients diagnosed with uterine corpus endometrial carcinoma. However, there remains a gap in understanding the specific functions of MPST in this context, including its clinical correlation analysis and functional pathway enrichment analysis, which have not been extensively investigated. Fluctuations in CBS expression are associated with alterations in H 2 S levels, contributing to the development of pathological conditions in different biological systems such as the brain, heart tissue, immune system, and liver tissue. In physiological conditions, the liver and brain are the primary sites of CBS expression, while our investigation of endometrial cancer revealed minimal expression in this context. Extensive research has highlighted the significance of H 2 S in obstetrical and gynecological diseases, particularly in conditions like endometriosis(3) and gestational hypertension(14, 15). These studies have emphasized the crucial role of H 2 S in mitigating inflammation, thereby contributing to the management of such gynecological ailments. In this study, we probe the expression of SLC2A1 and MPST in multitudinous types of cancer. However, the specific roles of SLC2A1 and MPST in tumor immune cell infiltration, abnormal DNA methylation, and prognosis in UCEC have not yet been elucidated. Therefore, using comprehensive bioinformatics analysis of the TCGA database, our study aims to provide a visual representation of the diagnostic and prognostic significance of SLC2A1 and MPST in UCEC. Furthermore, we carried out an interrelated analysis between SLC2A1 and MPST methylation, and examined whether gene alterations have an impact on disease outcomes in patients. Additionally, we confirmed the beneficial effects of SLC2A1 and MPST in diagnosing and treating UCEC patients, suggesting their potential as therapeutic targets for the evolution of novel immunotherapy strategies. Results 2.1.1The expression levels of SLC2A1 and MPST genes were assessed in both normal tissues and tumor The expression levels of SLC2A1 and MPST genes were analyzed in cancer datasets from the TCGA database. SLC2A1 was found to be observably upregulated in 22 out of 33 tumor tissues, including adrenocortical cancer (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical and endocervical cancer (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney clear cell carcinoma (KIRC), brain lower grade glioma (LGG), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), testicular germ cell tumor (TGCT), thyroid carcinoma (THCA), uterine corpus endometrioid carcinoma (UCEC), and uterine carcinosarcoma (UCS) (Fig. 1A). MPST was also found to be upregulated in 13 of the 33 tumor tissues, namely BLCA, COAD, DLBC, GBM, LGG, LIHC, PRAD, PAAD, READ, STAD, THYM, UCEC, and UCS (Fig. 1B). These results were confirmed using the TIMER database (Fig. 1C, D). Notably, both SLC2A1 and MPST were significantly upregulated in UCEC tissues (Fig. 1E,F). 2.1.2PPI network and enrichment analysis in cancer In the research, we structured a protein-protein interaction (PPI) network for SLC2A1 and MPST genes, along with their associated genes, using the STRING database. The SLC2A1-related genes identified in the network were GIPC1, STOM, HIF1A, SERPINH1, LDHA, TP53, SEMA3A, MDK, PTPRS, and RAB28 (Fig. 2A). Similarly, the genes associated with MPST included CBSL, SUOX, ENSP00000381234, TRMU, ETHE1, GOT1, GOT2, MOCS3, CTH, and NFS1(Fig. 2B). Enrichment analysis revealed significant findings in the bubble map, which showcased GO-BP (biological process), GO-CC (cellular component), GO-MF (molecular function), and KEGG pathways. The top biological processes identified were cell upgrowth adjust, nervous system upgrowth adjust, neurogenesis adjust, and lactate metabolic process. The most enriched cellular components included membrane microdomain, membrane raft, and pigment granule. The most abundant molecular functions were chondroitin sulfate binding, protein self-association, p53 binding, and histone deacetylase binding. The KEGG pathway enrichment analysis revealed significant correlation with multipe pathways, including Renal cell carcinoma, Mitophagy - animal, HIF-1 signaling pathway, Thyroid hormone signaling pathway, and Central carbon metabolism in cancer (Fig. 2C, E, G). Regarding MPST, the key biological processes observed were alpha-amino acid biosynthetic process, cellular amino acid biosynthetic process, alpha-amino acid metabolic process, and sulfur compound metabolic process. The most enriched cell constituent were mitochondrial intermembrane gap, organelle capsule cavity, and mitochondrial matrix. The most abundant molecular functions included sulfurtransferase activity, pyridoxal phosphate binding, vitamin B6 binding, and transfer activity involved in transferring sulfur-containing groups. The KEGG pathway enrichment analysis revealed a significant association with various way, including Phenylalanine metabolism, 2-Oxalic acid metabolism, Sulfur metabolism, amino acids biosynthesis, and Cysteine and methionine metabolism (Fig. 2D, F, H). 2.1.3The connection between SLC2A1, MPST, and Immune Cells The connection between SLC2A1 expression and immune cell infiltration was analyzed using data from the TCGA database. As exposed in Fig. 3A, the expression level of SLC2A1 was found to be negatively correlated with various immune cell types, including CD8 T cells (Fig. 3B), B cells (Fig. 3C), iDC cells (Fig. 3D), eosinophils (Fig. 3E), CD56bright cells (Fig. 3F), mast cells (Fig. 3G), CD56dim cells (Fig. 3H), NK cells (Fig. 3I), pDC cells (Fig. 3J), T cells (Fig. 3K), T helper cells (Fig. 3L), TFH cells (Fig. 3M), Th17 cells (Fig. 3N), and TReg cells (Fig. 3O). The expression levels of SLC2A1 were positively connected with macrophages (Fig. 3P), Th2 cells (Fig. 3Q), and Tcm (Fig. 3R). On the other hand, the expression levels of MPST were negatively correlated with macrophages (Fig. 4B), aDC (Fig. 4C), T helper cells (Fig. 4D), Tcm (Fig. 4E), Tgd (Fig. 4F), and Th2 cells (Fig. 4G). Additionally, the expression levels of MPST were positively connected with Th17 cells (Fig. 4H), pDC (Fig. 4I), NK cells (Fig. 4J), NK CD56dim cells (Fig. 4K), NK CD56bright cells (Fig. 4L), neutrophils (Fig. 4M), iDC (Fig. 4N), and cytotoxic cells (Fig. 4O). The connection between MPST expression and immune cell infiltration is illustrated in Fig. 4A. 2.1.4Important immune genes associated with tumor immune escape CD96, CTLA-4 and PDCD-1 are significant immune checkpoint proteins associated with tumor immune escape. We found that in UCEC samples of TCGA dataset, the expression level of SLC2A1 was negatively contacted with the expression levels of CD96, CTLA-4, and PDCD-1 (Fig. 5A-C). The expression level of MPST was positively associated with that of CD96, CTLA-4, and PDCD-1 (Fig. 5E-G). The TP53 tumor inhibitor gene is low expressed in normal cells but highly expressed in malignant tumors. We found that SLC2A1 and MPST were positively connected with the expression level of TP53 (Fig. 5D, H). Furthermore, we can also obtain the above results based on the TISIDB database (Fig. 5I-N). 2.1.5Correlation analysis of the methylation levels of SLC2A1 and MPST with UCEC Promoter DNA methylation has been shown to affect transcriptional repression and participate in tumorigenesis, considering that functional enrichment analysis identified SLC2A1 likely involved in the methylation process, we then analyzed methylation of SLC2A1 and MPST expression. We compared the methylation values of SLC2A1 and MPST between normal and tumor tissues, and from Fig. 6A, the methylation values of SLC2A1 were significantly decreased at P 0.05) in MPST (Fig. 6B), suggesting that the transcriptional expression of SLC2A1 may be related to promoter hypomethylation. We analyzed the level of DNA methylation in the SLC2A1 gene as well as the prognostic value of CpG islands in the SLC2A1 gene using the MetSurv tool. The results revealed 27 methylated CpG islands in SLC2A1. Including cg07803811, cg03128534, cg22176566, cg07499643,cg21877974,cg20345840, cg01907688, cg04287330, cg26188818, cg26681016, cg08159148, cg13790796, cg03106288, cg12656391 showing reduced DNA methylation levels (Fig. 6C). Furthermore, MethSurv-analysis showed that patients had lower overall survival than those with high SLC2A1 methylation (P < 0.05)(Table 3 ). We found that the seven CpG sites located on the CpG islands had a poor prognosis, including cg00102166, cg01924561, cg12656391, cg15089806, cg20294984, cg21474257, and cg22025263 (Fig. 6D-J). The reduction in SLC2A1 methylation in these seven CpG islands, compared to patients with higher CpG methylation in SLC2A1, was connected with poorer overall survival in UCEC patients. 2.1.6Genetic Alterations in SLC2A1 and MPST Are Not Connected With Survival Outcomes in UCEC Patients We researched the SLC2A1 and MPST genes based on 549 UCEC samples with mutations, and SLC2A1 gene changes were observed only in 4% of UCEC patients (Fig. 7A), K-M survival curve test showed OS (P = 0.809) (Fig. 7C). MPST gene change was observed only in 1.7% of UCEC patients (Fig. 7B), and K-M survival curve test showed OS (P = 0.587) (Fig. 7D). There was no significant difference between SLC2A1 as well as between patients with or no genetic change in the MPST gene. 2.1.7Baseline data of the UCEC patients Clinical data and expression data of 543 UCEC cases were downloaded from TCGA data in February 2023 are shown in (Table 4 ). This study included 206 patients under 60 years old (38.1% of the total) and 343 patients over 60 years old (61.9%). The multitude of patients were diagnosed with stage I disease (62.4%), followed by stage II (22.8%), stage III (9.4%), and stage IV (5.3%). The primary treatment outcomes for UCEC were as follows: stable disease (1.3%), progressive disease (4.2%), partial response (2.5%), and complete response (92%). The majority of tissues analyzed were derived from endometrioid tissue (75%), while 21% were serous tissue. In terms of residual tumor, 90.7% of patients had no residual tumor, and 9.3% had residual tumor. Regarding histological grade, 18.4% of UCEC patients had high differentiation (G1), 22.6% had moderate differentiation (G2), and the majority had low differentiation (G3), accounting for 59%. 2.1.8Clinical correlation analysis of SLC2A1 and MPST To further understand the relevance and the mechanisms underlying the expression of SLC2A1, MPST in UCEC, we investigated the asociation betweens SLC2A1, MPST expression and clinical features. The expression level of SLC2A1 was higher in UCEC tissues compared to normal tissues (Fig. 8A). Correlation analysis prompted that there were significant differences between SLC2A1 and clinical stage (Fig. 8B), histological type (Fig. 8C), and histological grade (Fig. 8F), no significant difference between age and tumor remnant(Fig. 8D, E). In the Fig. 8, the expression level of MPST was higher in UCEC tissues compared to normal tissue (Fig. 8G). Correlation analysis showed significant differences between MPST and clinical stage (Fig. 8H), histological type (Fig. 8L), histological grade (Fig. 8I) and age (Fig. 8J), and no significant differences between tumor residues(Fig. 8K). Immunohistochemical staining of the HPA database also confirmed higher levels of SLC2A1 and MPST in tumor tissue than in vicinity normal endometrial tissue (Fig. 8M, N). 2.1.9Prognostic analysis Figure 9 illustrates the diagnostic potential of SLC2A1 and MPST genes in distinguishing between normal and tumor tissues. We found that SLC2A1 has a strong discriminatory power, and the area under the ROC curve (AUC) of SLC2A1 was 0.844 as shown in Fig. 9A. Similarly, the ROC curve for MPST had an AUC of 0.761 (Fig. 9B), suggesting its usefulness in diagnostic identification in UCEC. Furthermore, the Kaplan-Meier survival curves demonstrated that higher expression of MPST was contacted with better overall survival (OS) prognosis (Fig. 9F), whereas lower expression of SLC2A1 was contacted with improved OS prognosis (Fig. 9E). The OS survival analysis, conducted using the UALCAN database, consistently supported these findings (Fig. 9C, D). 2.1.10Construction and evaluation of the nomogram model To examine the influence of MPST expression on the outcome of endometrial cancer, we conducted a univariate Cox regression analysis of MPST (Table 5 ). Using the results from this analysis, we structured a nomogram model to validate its prognostic value. Additionally, we utilized calibration curves to assess the veracity of the nomogram model in forecasting survival at 1, 3, and 5 years. Our findings demonstrated that MPST exhibited strong predictive capability, as evidenced by the good accuracy of the 1-year, 3-year, and 5-year survival prediction calibration curves of the nomogram model. Methods 5.1.1Source and treatment of the samples We acquired RNAseq data and relevant clinical information for 587 samples from the TCGA-UCEC project of the Cancer Gene Atlas ( https://portal.gdc.cancer.gov/ ). After removing samples without clinical information and duplicates, we converted the RNA sequencing data from FPKM format to transcript per million reads (TPM) format. Based on the median expression values for SLC2A1 and MPST, UCEC patients were divided into low and high expression groups. Statistical analysis was visualized using the ggplot2 software package and conducted using R software v3.6.3. The Wilcoxon Rank sum test identified two statistically significant data sets ( P < 0.05). Statistical significance was determined as *** ( P < 0.001), ** ( P < 0.01), * ( P 0.05). We confirm that whole necessary informed consent was acquired anterior to data collection as per the guidelines provided for accessing the TCGA database, which is publicly accessible. 5.1.2Clinical correlation analysis We conducted correlation analysis of SLC2A1 and MPST with tumor stage using the R software, specifically utilizing the ggplot2 package. The variables considered in the analysis included clinical stage, histological grade, pathological stage, histological type and pathological stage. Futhermore, we generated a Kaplan-Meier plot and performed diagnostic ROC curve analysis which are using the R packages pROC and ggplot2. 5.1.3Construction of the protein-protein interaction network and GO-KEGG analysis To visualize the protein-protein interaction(PPI) network, we employed Cytoscape (version 3.7.2) and accessed the STRING database ( https://string-db.org/ ). A protein interaction score threshold of 0.4 was used to determine statistically significant interactions. Subsequently, we identified 10 functional partner genes for further analysis, focusing on GO term enrichment and KEGG pathway analysis to gain insights into the function of SLC2A1 and MPST. 5.1.4Methylation We explored the DNA methylation levels of the SLC2A1 and MPST genes and assessed the prognostic significance of CpG islands within these genes for patients with UCEC. This analysis was emerged using the MetSurv database ( https://biit.cs.ut.ee/methsurv/ ). Statistical significance was stipulated as a P -value below 0.05. 5.1.5Analysis of the immune infiltration We utilized the study by Bindea et al.(16) to extract marker genes for 24 immune cells. Using GSEA (ssGSEA) on UCEC mRNA TPM data, we calculated the levels of tumor-infiltrating immune cells(17). We performed correlation search between the expression levels of SLC2A1 and MPST genes, as well as the relationship between immune checkpoint genes (including CD96, CTLA4, and PDCD1) and TP53. This analysis was conducted on UCEC samples from the TCGA database utilizing Spearman’s correlation analysis and the “ggplot2” (v3.3.3) R package. Correlations were defined significant if the P -value was below 0.05. 5.1.6Gene alterations in UCEC samples We utilized cBioPortal ( https://www.cbioportal.org/ ) for conducting log-rank tests and K-M survival curve analysis. These analyses were performed to assess the prognostic significance of genomic alterations in the SLC2A1 and MPST genes. Statistical significance was determined utilizing a threshold of P < 0.05. 5.1.7Assessment of the Prognostic Significance of MPST Expression in UCEC To evaluate the survival outcomes of UCEC patients, we conducted Kaplan-Meier survival curve analysis and multivariate and univariate Cox regression analyses, focusing on the expression levels of SLC2A1 and MPST. Furthermore, we employed the “pROC” (v1.17.0.1), “timeROC” (v0.4), and “ggplot2” (v3.3.3) R packages to perform diagnostic ROC curve and nomogram model analyses. The purpose of these analyses was to determine if MPST expression levels can be used to predict UCEC diagnosis. 5.1.8Statistical analysis To conduct the statistical analysis, we utilized R (v.3.6.3). Group differences were contrasted utilizing either the Wilcoxon rank-sum test or the t-test. For the K-M survival analysis, we employed the Telog-rank test. And for the COX regression analysis, we used hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) to calculate . 5.1.9Availability of data and materials All data generated or analyzed during this study are included in this published article. Discussion In the present study, we observed a notable overexpression of SLC2A1 in 22 out of 33 human cancer tissues, and MPST was found to be upregulated in 13 out of 33 tumor tissues. These findings indicate a high expression of both SLC2A1 and MPST in clinical samples of endometrial cancer, as well as in the TCGA database. The SLC2A1 gene encodes the solute carrier of the glucose transporter (GLUT) family, which is responsible for the initial step in glucose utilization. Previous studies have shown that SLC2A1 is a larvaceous prognostic biomarker for immunotherapy in lung adenocarcinoma(18), and that in colorectal cancer, METTL3 plays an oncogenic role by stabilizing HK2 and SLC1A2 mRNA through the IGF2BPs axis, thereby regulating glycolytic metabolism and cell proliferation(19). As a result of these findings, there is evidence that SLC2A1 and MPST can be used as diagnostic markers in UCEC.The analysis of SLC2A1 Related DEGs showed a significant correlation with various cancer-related pathways, including carbon metabolism, thyroid hormone signaling, HIF 1 signaling, membrane rafts, cerebral cortex development, and lactate metabolism. On the other hand, KEGG analysis revealed that MPST is primarily involved in cysteine and methionine metabolism, leading to the production of homocysteine. Homocysteine plays a critical role in physiological processes such as cell cycle progression and maintenance of cell homeostasis(20). Based on the aforementioned discussion, it can be inferred that SLC2A1 potentially plays a role in the progression of UCEC by modulating carbon metabolism and lactic acid metabolism within cancer cells. Additionally, there may be a correlation between MPST and UCEC development, as it is involved in cysteine and methionine metabolism. Our study provides evidence of a potential association between the expression of SLC2A1 and MPST, and immune cell infiltration. The expression of SLC2A1 was found to have a negative association with various immune cell types, including B cells, T cells, CD56dim cells, eosinophils, iDC cells, mast cells, NK cells, CD56bright cells, and pDC cells. Similarly, the expression of MPST was negatively correlated with macrophages, T helper cells, Tcm, Th2 cells, and Tgd cells. It is worth noting that M1 macrophages are known for their potential antitumor activity. NK cells play a crucial role in inducing apoptosis in tumor cells by binding to Fas ligand (FasL) or tumor necrosis factor-associated apoptosis-inducing ligand (TRAIL) receptors. Furthermore, studies have demonstrated that highly activated multifunctional CD4 (+) T cells play a significant role in enhancing and sustaining the overall antitumor immunity within the host(21). Our findings indicate that the overexpression of SLC2A1 and MPST contributes to the immune evasion mechanism of UCEC cells, fostering their progression and growth. CD96, CTLA-4, and PDCD1 are the key proteins contacted with tumor immune escape(22). Although CD96 inhibitors have not yet undergone clinical trials, preclinical data demonstrate their efficacy in inhibiting experimental or spontaneous cancer metastasis in various mouse models(23). Moreover, TP53 exhibits high expression levels in malignancies, and its mutations are linked to unfavorable prognoses in various human cancers(24). TP53 mutations suppress antitumor immunity and reduce the efficacy of cancer immunotherapy(25–28). Thus, we assessed the correlation between the expression levels of SLC2A1 and MPST and immune checkpoint genes, namely CD96, CTLA-4, PDCD1, and TP53. There was a significant correlation between the expression levels of SLC2A1 and immune checkpoint genes, as well as TP53 expression. This suggests that targeting SLC2A1 and MPST could potentially enhance the effectiveness of immunotherapy in patients with UCEC. Promoter DNA methylation plays a vital role in transcriptional repression and contributes to tumorigenesis. Alterations in gene methylation have been linked to the development, expansion, and advancement of different types of cancer(29–31). UCEC patients' prognosis was associated with methylation of the SLC2A1 gene. We found that hypermethylation at specific sites, namely cg00102166, cg01924561, cg12656391, cg15089806, cg20294984, cg21474257, and cg22025263, was associated with poorer overall survival. TP53, CTNNB1, PTEN, ARID1A, and KMT2D gene mutations have notable implications for the diagnosis and treatment of UCEC patients(32). In the present study, we observed a low incidence of SLC2A1 and MPST gene mutations in UCEC tissues, with rates of only 4% and 1.7% respectively. Additionally, we found no association between these mutations and overall survival (OS) in UCEC patients. Our study found significant associations between the expression level of SLC2A1 in UCEC tissues and clinical stage, histological type, histological grade, and overall survival. Similarly, the expression level of MPST was remarkably correlated with age, histological type, histological grade, clinical stage, and overall survival. Moreover, higher expression levels of SLC2A1 were linked to a poorer prognosis in UCEC patients, while lower expression levels of MPST were associated with a poorer prognosis in UCEC patients. The K-M survival curves demonstrated a notable association between higher expression of SLC2A1 and lower overall survival, while lower expression of MPST correlated with higher overall survival. The differential diagnosis values for UCEC were 0.844 and 0.761 for SLC2A1 and MPST, respectively. These findings indicate that SLC2A1 and MPST have potential as diagnostic and prognostic biomarkers for UCEC. Conclusion In the present study, we have confirmed the diagnostic and prognostic value of SLC2A1 and MPST in UCEC. SLC2A1 is found to be overexpressed in tumor tissues of UCEC patients, while MPST shows high expression in these tissues as well. It has been shown that SLC2A1 and MPST expression levels correlate with the presence of immune cells in tumors, suggesting they might play a role in immune response therapy for UCEC patients. SLC2A1 is believed to play a key role in cancer cell proliferation and metastasis through multiple potential mechanisms, including cancer central carbon metabolism, the HIF-1 signaling pathway, and lactic acid metabolic processes. On the other hand, MPST appears to take part in cysteine and methionine metabolism, contributing to the development of UCEC. The expression levels of SLC2A1 and MPST in UCEC tumors are also related to the infiltration status of different immune cell types, which may influence immune therapy. Moreover, the methylation and gene expression of SLC2A1 have been found to be associated with the prognosis of UCEC. Considering these findings, targeting SLC2A1 and MPST could serve as a potential therapeutic strategy, and their expression levels could be useful as diagnostic indicators. Moreover, these discoveries open up opportunities for the development of novel immunotherapy approaches in UCEC. Abbreviations UCEC: Uterine corpus endometrial carcinoma H2S:hydrogen sulfide CBS:cystathionine-synthase CSE:cystathionine-lyase 3-MST:3-mercaptopyruvate sulfurtransferase PPI:protein-protein interaction HRs:hazard ratios CIs:confidence intervals ACC:adrenocortical cancer BLCA:bladder urothelial carcinoma BRCA:breast invasive carcinoma cervical CESC:endocervical cancer CHOL:cholangiocarcinoma COAD:colon adenocarcinoma ESCA:esophageal carcinoma GBM:glioblastoma multiforme HNSC:head and neck squamous cell carcinoma KIRC:kidney clear cell carcinoma LGG:brain lower grade glioma LUSC:lung squamous cell carcinoma LIHC:liver hepatocellular carcinoma LUAD:lung adenocarcinoma OV:ovarian serous cystadenocarcinoma PAAD:pancreatic adenocarcinoma READ:rectum adenocarcinoma STAD:stomach adenocarcinoma TGCT:testicular germ cell tumor THCA:thyroid carcinoma UCS:uterine carcinosarcoma BP:biological process CC:cellular component MF:molecular function OS:overall survival GLUT:glucose transporter Declarations Competing interests The authors declare that they have no competing interests. Author Contributions GXX, LYX, CBR, and XCC obtained and analyzed the data, and performed the software application and data visualization. XXY wrote the original draft of the manuscript. DJX and XXY contributed to conception and design of the study, GXX, LYX, CBR, and XCC wrote the manuscript. All authors read and approved the final manuscript. References Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017;67(1):7-30. Thrastardottir TO, Copeland VJ, Constantinou C. The Association Between Nutrition, Obesity, Inflammation, and Endometrial Cancer: A Scoping Review. Curr Nutr Rep. 2023;12(1):98-121. Lei S, Cao Y, Sun J, Li M, Zhao D. H(2)S promotes proliferation of endometrial stromal cells via activating the NF-kappaB pathway in endometriosis. Am J Transl Res. 2018;10(12):4247-57. Ancey PB, Contat C, Boivin G, Sabatino S, Pascual J, Zangger N, et al. GLUT1 Expression in Tumor-Associated Neutrophils Promotes Lung Cancer Growth and Resistance to Radiotherapy. 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Du J, Wang P, Gou Q, Jin S, Xue H, Li D, et al. Hydrogen sulfide ameliorated preeclampsia via suppression of toll-like receptor 4-activated inflammation in the rostral ventrolateral medulla of rats. Biomed Pharmacother. 2022;150:113018. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782-95. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. Zheng H, Long G, Zheng Y, Yang X, Cai W, He S, et al. Glycolysis-Related SLC2A1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy. Cancers (Basel). 2022;14(21). Shen C, Xuan B, Yan T, Ma Y, Xu P, Tian X, et al. m(6)A-dependent glycolysis enhances colorectal cancer progression. Mol Cancer. 2020;19(1):72. Zaric BL, Obradovic M, Bajic V, Haidara MA, Jovanovic M, Isenovic ER. Homocysteine and Hyperhomocysteinaemia. Curr Med Chem. 2019;26(16):2948-61. Burkard-Mandel L, O'Neill R, Colligan S, Seshadri M, Abrams SI. Tumor-derived thymic stromal lymphopoietin enhances lung metastasis through an alveolar macrophage-dependent mechanism. Oncoimmunology. 2018;7(5):e1419115. Dougall WC, Kurtulus S, Smyth MJ, Anderson AC. TIGIT and CD96: new checkpoint receptor targets for cancer immunotherapy. Immunol Rev. 2017;276(1):112-20. Blake SJ, Stannard K, Liu J, Allen S, Yong MC, Mittal D, et al. Suppression of Metastases Using a New Lymphocyte Checkpoint Target for Cancer Immunotherapy. Cancer Discov. 2016;6(4):446-59. Wang X, Sun Q. TP53 mutations, expression and interaction networks in human cancers. Oncotarget. 2017;8(1):624-43. Jiang Z, Liu Z, Li M, Chen C, Wang X. Immunogenomics Analysis Reveals that TP53 Mutations Inhibit Tumor Immunity in Gastric Cancer. Transl Oncol. 2018;11(5):1171-87. Xiao W, Du N, Huang T, Guo J, Mo X, Yuan T, et al. TP53 Mutation as Potential Negative Predictor for Response of Anti-CTLA-4 Therapy in Metastatic Melanoma. EBioMedicine. 2018;32:119-24. Lyu H, Li M, Jiang Z, Liu Z, Wang X. Correlate the TP53 Mutation and the HRAS Mutation with Immune Signatures in Head and Neck Squamous Cell Cancer. Comput Struct Biotechnol J. 2019;17:1020-30. Daver NG, Maiti A, Kadia TM, Vyas P, Majeti R, Wei AH, et al. TP53-Mutated Myelodysplastic Syndrome and Acute Myeloid Leukemia: Biology, Current Therapy, and Future Directions. Cancer Discov. 2022;12(11):2516-29. Malta TM, de Souza CF, Sabedot TS, Silva TC, Mosella MS, Kalkanis SN, et al. Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications. Neuro Oncol. 2018;20(5):608-20. Baylin SB, Jones PA. A decade of exploring the cancer epigenome - biological and translational implications. Nat Rev Cancer. 2011;11(10):726-34. Ma X, Zhang L, Liu L, Ruan D, Wang C. 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Tables Table 1 GO and KEGG enrichment analyses of SLC2A1 and functional partner genes in UCEC Ontology ID Description P value BP GO:0006089 lactate metabolic process 6.05e-08 BP GO:0050767 regulation of neurogenesis 5.91e-07 BP GO:0051960 regulation of nervous system development 1.57e-06 BP GO:0060284 regulation of cell development 2.94e-06 BP GO:0045926 negative regulation of growth 5.56e-06 CC GO:0045121 membrane raft 0.0005 CC GO:0098857 membrane microdomain 0.0005 CC GO:0042470 melanosome 0.0013 CC GO:0048770 pigment granule 0.0013 CC GO:0072562 blood microparticle 0.0024 MF GO:0035374 chondroitin sulfate binding 1.19e-05 MF GO:0043621 protein self-association 0.0005 MF GO:0002039 p53 binding 0.0006 MF GO:0042826 histone deacetylase binding 0.0020 MF GO:0008201 heparin binding 0.0036 KEGG hsa05230 Central carbon metabolism in cancer 2.57e-08 KEGG hsa04066 HIF-1 signaling pathway 2.35e-05 KEGG hsa04919 Thyroid hormone signaling pathway 3.21e-05 KEGG hsa04137 Mitophagy - animal 0.0007 KEGG hsa05211 Renal cell carcinoma 0.0007 Table 2 GO and KEGG enrichment analyses of MPST and functional partner genes in UCEC Ontology ID Description P value BP GO:0006790 sulfur compound metabolic process 6.87e-06 BP GO:0043650 dicarboxylic acid biosynthetic process 1.44e-05 BP GO:0002098 tRNA wobble uridine modification 1.66e-05 BP GO:0006103 2-oxoglutarate metabolic process 2.15e-05 BP GO:0002097 tRNA wobble base modification 2.42e-05 CC GO:0005759 mitochondrial matrix 4.23e-07 CC GO:0005758 mitochondrial intermembrane space 0.0334 CC GO:0031970 organelle envelope lumen 0.0374 MF GO:0016783 sulfurtransferase activity 3.07e-12 MF GO:0016782 transferase activity, transferring sulphur-containing groups 1.25e-08 MF GO:0030170 pyridoxal phosphate binding 1.4e-06 MF GO:0070279 vitamin B6 binding 1.48e-06 MF GO:0019842 vitamin binding 2.77e-05 KEGG hsa00270 Cysteine and methionine metabolism 9.16E-10 KEGG hsa00920 Sulfur metabolism 1.14E-07 KEGG hsa01230 Biosynthesis of amino acids 8.35E-07 KEGG hsa00360 Phenylalanine metabolism 0.0001 KEGG hsa01210 2-Oxocarboxylic acid metabolism 0.0002 Table 3 Effects of methylation levels in the CpG sites of the SLC2A1 gene on the prognosis of UCEC patients Name HR P value Cg00102166 0.606 0.04 Cg01907688 1.734 0.082 Cg01924561 0.424 0.0004 Cg03106288 1.455 0.21 Cg03128534 1.697 0.024 Cg04287330 1.826 0.049 Cg05034603 1.389 0.19 Cg05802386 0.625 0.13 Cg06094523 1.181 0.56 Cg07499643 1.58 0.053 Cg07803811 0.823 0.5 Cg08159148 1.968 0.0047 Cg09502149 0.756 0.27 Cg12101479 0.645 0.13 Cg12656391 0.491 0.025 Cg13790796 1.658 0.034 Cg15089806 0.551 0.015 Cg16738646 1.103 0.68 Cg20282814 0.615 0.1 Cg20294984 0.539 0.01 Cg20345840 2.745 0.0046 Cg21474257 0.431 0.014 Cg21877974 1.684 0.067 Cg22025263 0.43 0.0008 Cg22176566 1.653 0.09 Cg26188818 0.638 0.16 Cg26681016 1.539 0.16 Table 4 Baseline data sheet for UCEC patients Characteristic levels Overall n 543 Clinical stage, n (%) Stage I 339 (62.4%) Stage II 51 (9.4%) Stage III 124 (22.8%) Stage IV 29 (5.3%) Primary therapy outcome, n (%) PD 20 (4.2%) SD 6 (1.3%) PR 12 (2.5%) CR 436 (92%) Age, n (%) 60 334 (61.9%) Histological type, n (%) Endometrioid 407 (75%) Mixed 22 (4.1%) Serous 114 (21%) Residual tumor, n (%) R0 372 (90.7%) R1 22 (5.4%) R2 16 (3.9%) Histologic grade, n (%) G1 98 (18.4%) G2 120 (22.6%) G3 314 (59%) Tumor invasion(%), n (%) =50 211 (44.9%) OS event, n (%) Alive 452 (83.2%) Dead 91 (16.8%) Table 5 Univariate and multivariate Cox regression analysis between UCEC clinical characteristics and OS Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Clinical stage 553 Stage I&Stage II 394 Reference Reference Stage III&Stage IV 159 3.553 (2.362–5.344) < 0.001 4.392 (2.443–7.896) < 0.001 Residual tumor 414 R0 376 Reference Reference R1&R2 38 3.112 (1.774–5.459) < 0.001 1.951 (1.022–3.725) 0.043 Tumor invasion(%) 475 = 50 214 2.825 (1.752–4.554) < 0.001 1.381 (0.771–2.473) 0.278 MPST 553 Low 277 Reference Reference High 276 0.640 (0.425–0.964) 0.033 0.567 (0.335–0.961) 0.035 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3876179","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268940863,"identity":"0c0297d3-a3a5-4597-8cc6-3a9bf2cf77ea","order_by":0,"name":"Xiaoyu Xi","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Xi","suffix":""},{"id":268940864,"identity":"c23fb610-d6fc-44ff-83f0-fcdce075b1d0","order_by":1,"name":"Xinxin Gong","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Gong","suffix":""},{"id":268940865,"identity":"c844a95c-aa1b-4ef0-ab4b-e19d5c4983ed","order_by":2,"name":"Yixi Liu","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixi","middleName":"","lastName":"Liu","suffix":""},{"id":268940866,"identity":"1d9aad57-ac06-4b13-a5e0-e0526aa26cdd","order_by":3,"name":"Boran Cui","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Boran","middleName":"","lastName":"Cui","suffix":""},{"id":268940867,"identity":"bb43be58-5558-481c-90a4-a93f62b8e830","order_by":4,"name":"Chenchen Xia","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenchen","middleName":"","lastName":"Xia","suffix":""},{"id":268940869,"identity":"125522b3-b25f-40ed-a3db-8dacca0f8bf2","order_by":5,"name":"Jiexian Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACfvbmAwcSKmzs+CUY2CBCBwhokew5lvjgwZm0ZMkZxGoxuJFjbPiw7RDjhhvEamE4c8BMIrHtALPx7eZjj262Mcjx3Uhg/FyARwdje0OaRMK5O3xmd46lG+e2MRhL3khglp6BRwszz4FjEgllz5jNbuSYSQO1JG64kcDGzINHCxvQVRIJbIcZN8/I/wbSUk9QC49EMrNBQtthxg0SOWwgLQkGhLRI8BxjfJAADGSJG2nmxjnnJAxnnnnYLI1Pi/3x/g8Hf4Cickbys8c5ZTbyfMeTD37GpwXDViBmbCBBwygYBaNgFIwCbAAAufdTuOT86wMAAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiexian","middleName":"","lastName":"Du","suffix":""},{"id":268940871,"identity":"2c64427b-98f2-46aa-8b66-8024fcc17d67","order_by":6,"name":"Shan Qin","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2024-01-18 15:15:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3876179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3876179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50182368,"identity":"4a083a04-7469-424a-a150-52c619d573ae","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3228297,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of SLC2A1 and MPST genes in tumor and normal tissues. (A) The expression of SLC2A1 in TCGA tumors and normal tissues compared with GTEx database data; (B) The expression of MPST in TCGA tumors and normal tissues compared with GTEx database data; (C,D) Expressions of SLC2A1 and MPST in tumor grade normal tissues in TIMER database; (E,F)Expression level of SLC2A1 and MPST were significantly higher in UCEC tissue than in adjacent peritumor uterus tissue (*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/d67ab0e7033006d57acf98ef.png"},{"id":50182365,"identity":"0b8d2a66-1272-4f3f-bfcc-68f0df745ccc","added_by":"auto","created_at":"2024-01-25 18:54:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1754932,"visible":true,"origin":"","legend":"\u003cp\u003eConstruct PPI network and make enrichment analysis. (A,B)Synthesis analysis of protein-protein interaction of SLC2A1 and MPST; (C,D)GO enrichment show the enriched biological functions (BP), cellular components (CC), and molecular functions (MF); (E,F)KEGG pathway enrichment analysis of SLC2A1 and MPST; (G,H)GO term and KEGG pathway enrichment analysis of SLC2A1and MPST.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/843b0fb7878f2c1f8b432211.png"},{"id":50182367,"identity":"ec84582f-877f-463e-a528-1dfb609f7dd7","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2656335,"visible":true,"origin":"","legend":"\u003cp\u003eThe relation of SLC2A1 and immune cells. (A)The relationship between SLC2A1 expression and immune cell infiltration was analyzed based on TCGA database; (B-O)The correlation analysis results between the expression levels of SLC2A1 and the expression levels of some immune cells in the TCGA-UCEC dataset.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/08c4a0561be76e2696118f30.png"},{"id":50182827,"identity":"b9950f3e-4668-4874-8433-e3265fa36d53","added_by":"auto","created_at":"2024-01-25 19:02:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2240280,"visible":true,"origin":"","legend":"\u003cp\u003eThe relation of MPST and immune cells. (A)The relationship between MPST expression and immune cell infiltration was analyzed based on TCGA database; (B-O)The correlation analysis results between the expression levels of MPST and the expression levels of some immune cells in the TCGA-UCEC dataset.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/31c43fba860141322840b3b8.png"},{"id":50182371,"identity":"b9ff4388-e1ec-4f78-84bb-299c91c475f8","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2395911,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of SLC2A1(A-D) and MPST(E-H) is associated with CD96, CTLA-4, PDCD-1 and TP53 in tumor immune escape; (I-N)The same results in the TISIDIB database.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/13ce0a8fdf89b035ce001581.png"},{"id":50182828,"identity":"1f2493f1-a363-40db-a876-c2cabe1e2927","added_by":"auto","created_at":"2024-01-25 19:02:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1846586,"visible":true,"origin":"","legend":"\u003cp\u003eDNA methylation levels in the SLC2A1 gene are associated with the prognosis of UCEC patients. DNA methylation levels in the SLC2A1 and MPST gene(A,B).The heat map showed the SLC2A1 DNA methylation at CpG sites(C). (D-J) the Kaplan–Meier (K-M) curves of overall survival (OS) shows the difference between the low and high expression of SLC2A1 methylation of cg00102166(D),cg01924561(E),cg22025263(F),cg12656391(G),cg20294984(H), cg15089806(I),cg21474257(J)CpG sites in UCEC.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/4288d22d12359f670ed36344.png"},{"id":50182829,"identity":"1ea7a351-2abe-47dd-8176-a2518dba6614","added_by":"auto","created_at":"2024-01-25 19:02:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":658151,"visible":true,"origin":"","legend":"\u003cp\u003eSLC2A1 and MPST gene alterations are not associated with the survival outcomes in UCEC. (A,B) OncoPrint visual summary of the alterations in the SLC2A1 and MPAT gene. (C,D) Kaplan–Meier survival curves show the overall survival rates of UCEC patients with or without SLC2A1 and MPST gene alterations.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/fbae05ad4c19ee6119fd27c8.png"},{"id":50182374,"identity":"fd772523-8192-49a3-87ee-97bf822d0b14","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3164256,"visible":true,"origin":"","legend":"\u003cp\u003eSLC2A1 and MPST expression levels correlate with multiple clinicopathological characteristics of UCEC patients. (A–F) The correlation analysis between SLC2A1 expression levels and (A) Expression level, (B) clinical stages, (C) histological grade, (D) age, (E) residual tumor, (F) Histological type of ucec patients; (G-L) The correlation analysis between MPST expression levels and (G) Expression level, (H) clinical stages, (I) histological grade, (J) age, (K) residual tumor, (L) Histological type of ucec patients *P \u0026lt; 0.05, **P \u0026lt;0.01,***P\u0026lt;0.001; (M,N)Immunohistochemical analysis of UCEC and normal liver tissue determined by HPA database.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/771446ded202d4c935c8b5f2.png"},{"id":50182370,"identity":"a2c4b596-4282-4cd7-bd32-83591b849343","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":985447,"visible":true,"origin":"","legend":"\u003cp\u003e(A,B) Diagnostic ROC curves to distinguish UCEC tissues and normal tissues based on the SLC2A1 and MPST expression levels. Based on UALCAN database analysis, (C) OS survival analysis of SLC2A1, (D) OS survival analysis of MPST; Kaplan-Meier plotter database analysis showed that Kaplan-Meier curves showed overall survival of patients in high-and low-risk groups; (E) OS survival analysis of SLC2A1; (F) OS survival analysis of MPST.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/0629648162d53891542dd966.png"},{"id":50182373,"identity":"e8bde5ba-d79c-498f-8ac1-e2ddd06039ba","added_by":"auto","created_at":"2024-01-25 18:54:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":510007,"visible":true,"origin":"","legend":"\u003cp\u003eShow the superior diagnostic and prognostic performance of MPST using a nomogram model. (A) A nomogram model for MPST expression in UCEC; (B) a nomogram model for calibration curve evaluation of MPST at 1, 3 and 5 years.\u003c/p\u003e","description":"","filename":"figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/aa888b97b75e763e7c711563.png"},{"id":50336170,"identity":"004cb03d-f421-464b-879a-b7f4d50502fd","added_by":"auto","created_at":"2024-01-30 02:22:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5205420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3876179/v1/967095be-d96f-4fff-ad38-32bf7838e784.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Role of SLC2A1 and MPST in Uterine Corpus Endometrial Carcinoma: Diagnostic and Prognostic Insights","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe occurrence of uterine corpus endometrial carcinoma (UCEC), a common type of gynecological cancer, is steadily rising worldwide(1). In recent years, the rising prevalence of obesity, lifestyle changes, and increased use of estrogen replacement therapy have contributed to the significant increase in UCEC cases, posing a substantial health risk to women(2). Currently, surgery is the primary treatment option for UCEC. However, it is crucial to select the appropriate adjuvant treatment based on the tumor\u0026rsquo;s pathology and clinical stage. Therefore, identifying suitable gene targets for UCEC treatment and finding reliable diagnostic and prognostic indicators are essential. These advancements present an opportunity to explore novel immunotherapy strategies. After nitric oxide and carbon monoxide, hydrogen sulfide (H2S) has been recognized as the third gas signaling molecule. It is found abundantly in mammals. Endogenous H2S is primarily produced by enzymes such as cystathionine-synthase (CBS), cystathionine-lyase (CSE), and 3-mercaptopyruvate sulfurtransferase (3-MST)(3). The enzyme CSE is encoded by the SLC2A1 gene, while 3-MST is encoded by the MPST gene. CBS, on the other hand, is encoded by the cystathionine beta-synthase gene. The gene SLC2A1 is responsible for encoding a protein that is essential for the functioning of cellular energy metabolism pathways(4\u0026ndash;6). It is predominantly expressed in endothelial and trophoblastic cells, making it highly significant in these cellular contexts. As a facilitative glucose transporter, SLC2A1 is responsible for the continuous, or basal, uptake and transport of glucose(7). The glucose transporter proteins (GLUTs) are crucial membrane proteins responsible for mediating the transmembrane transport of glucose, maintaining cell energy supply, and supporting normal cellular functions. The overexpression of SLC2A1 in different types of cancer, including breast, lung, liver, endometrial, oral, and gastric cancer(4, 8\u0026ndash;12), is particularly intriguing. Nevertheless, the role of SLC2A1 in UCEC is still not fully understood.\u003c/p\u003e \u003cp\u003eMPST is a crucial enzyme that regulates the biosynthesis of endogenous hydrogen sulfide (H\u003csub\u003e2\u003c/sub\u003eS) and is expressed in various human tissues. Activation of MPST is involved in important processes such as tRNA sulfuration, protein aminoacylation, and cyanide detoxification(13). The experimental results indicate that MPST has an important function in providing protection against oxidative stress, overseeing the functioning of mitochondria in respiration, and managing the metabolism of fatty acids(13). Furthermore, research has shown that MPST is present in endometrial tumors, indicating a direct connection with the survival rate of patients diagnosed with uterine corpus endometrial carcinoma. However, there remains a gap in understanding the specific functions of MPST in this context, including its clinical correlation analysis and functional pathway enrichment analysis, which have not been extensively investigated.\u003c/p\u003e \u003cp\u003eFluctuations in CBS expression are associated with alterations in H\u003csub\u003e2\u003c/sub\u003eS levels, contributing to the development of pathological conditions in different biological systems such as the brain, heart tissue, immune system, and liver tissue. In physiological conditions, the liver and brain are the primary sites of CBS expression, while our investigation of endometrial cancer revealed minimal expression in this context.\u003c/p\u003e \u003cp\u003eExtensive research has highlighted the significance of H\u003csub\u003e2\u003c/sub\u003eS in obstetrical and gynecological diseases, particularly in conditions like endometriosis(3) and gestational hypertension(14, 15). These studies have emphasized the crucial role of H\u003csub\u003e2\u003c/sub\u003eS in mitigating inflammation, thereby contributing to the management of such gynecological ailments. In this study, we probe the expression of SLC2A1 and MPST in multitudinous types of cancer. However, the specific roles of SLC2A1 and MPST in tumor immune cell infiltration, abnormal DNA methylation, and prognosis in UCEC have not yet been elucidated. Therefore, using comprehensive bioinformatics analysis of the TCGA database, our study aims to provide a visual representation of the diagnostic and prognostic significance of SLC2A1 and MPST in UCEC. Furthermore, we carried out an interrelated analysis between SLC2A1 and MPST methylation, and examined whether gene alterations have an impact on disease outcomes in patients. Additionally, we confirmed the beneficial effects of SLC2A1 and MPST in diagnosing and treating UCEC patients, suggesting their potential as therapeutic targets for the evolution of novel immunotherapy strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.1The expression levels of SLC2A1 and MPST genes were assessed in both normal tissues and tumor\u003c/h2\u003e \u003cp\u003eThe expression levels of SLC2A1 and MPST genes were analyzed in cancer datasets from the TCGA database. SLC2A1 was found to be observably upregulated in 22 out of 33 tumor tissues, including adrenocortical cancer (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical and endocervical cancer (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney clear cell carcinoma (KIRC), brain lower grade glioma (LGG), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), testicular germ cell tumor (TGCT), thyroid carcinoma (THCA), uterine corpus endometrioid carcinoma (UCEC), and uterine carcinosarcoma (UCS) (Fig.\u0026nbsp;1A). MPST was also found to be upregulated in 13 of the 33 tumor tissues, namely BLCA, COAD, DLBC, GBM, LGG, LIHC, PRAD, PAAD, READ, STAD, THYM, UCEC, and UCS (Fig.\u0026nbsp;1B). These results were confirmed using the TIMER database (Fig.\u0026nbsp;1C, D). Notably, both SLC2A1 and MPST were significantly upregulated in UCEC tissues (Fig.\u0026nbsp;1E,F).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2PPI network and enrichment analysis in cancer\u003c/h2\u003e \u003cp\u003eIn the research, we structured a protein-protein interaction (PPI) network for SLC2A1 and MPST genes, along with their associated genes, using the STRING database. The SLC2A1-related genes identified in the network were GIPC1, STOM, HIF1A, SERPINH1, LDHA, TP53, SEMA3A, MDK, PTPRS, and RAB28 (Fig.\u0026nbsp;2A). Similarly, the genes associated with MPST included CBSL, SUOX, ENSP00000381234, TRMU, ETHE1, GOT1, GOT2, MOCS3, CTH, and NFS1(Fig.\u0026nbsp;2B). Enrichment analysis revealed significant findings in the bubble map, which showcased GO-BP (biological process), GO-CC (cellular component), GO-MF (molecular function), and KEGG pathways. The top biological processes identified were cell upgrowth adjust, nervous system upgrowth adjust, neurogenesis adjust, and lactate metabolic process. The most enriched cellular components included membrane microdomain, membrane raft, and pigment granule. The most abundant molecular functions were chondroitin sulfate binding, protein self-association, p53 binding, and histone deacetylase binding. The KEGG pathway enrichment analysis revealed significant correlation with multipe pathways, including Renal cell carcinoma, Mitophagy - animal, HIF-1 signaling pathway, Thyroid hormone signaling pathway, and Central carbon metabolism in cancer (Fig.\u0026nbsp;2C, E, G). Regarding MPST, the key biological processes observed were alpha-amino acid biosynthetic process, cellular amino acid biosynthetic process, alpha-amino acid metabolic process, and sulfur compound metabolic process. The most enriched cell constituent were mitochondrial intermembrane gap, organelle capsule cavity, and mitochondrial matrix. The most abundant molecular functions included sulfurtransferase activity, pyridoxal phosphate binding, vitamin B6 binding, and transfer activity involved in transferring sulfur-containing groups. The KEGG pathway enrichment analysis revealed a significant association with various way, including Phenylalanine metabolism, 2-Oxalic acid metabolism, Sulfur metabolism, amino acids biosynthesis, and Cysteine and methionine metabolism (Fig.\u0026nbsp;2D, F, H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3The connection between SLC2A1, MPST, and Immune Cells\u003c/h2\u003e \u003cp\u003eThe connection between SLC2A1 expression and immune cell infiltration was analyzed using data from the TCGA database. As exposed in Fig.\u0026nbsp;3A, the expression level of SLC2A1 was found to be negatively correlated with various immune cell types, including CD8 T cells (Fig.\u0026nbsp;3B), B cells (Fig.\u0026nbsp;3C), iDC cells (Fig.\u0026nbsp;3D), eosinophils (Fig.\u0026nbsp;3E), CD56bright cells (Fig.\u0026nbsp;3F), mast cells (Fig.\u0026nbsp;3G), CD56dim cells (Fig.\u0026nbsp;3H), NK cells (Fig.\u0026nbsp;3I), pDC cells (Fig.\u0026nbsp;3J), T cells (Fig.\u0026nbsp;3K), T helper cells (Fig.\u0026nbsp;3L), TFH cells (Fig.\u0026nbsp;3M), Th17 cells (Fig.\u0026nbsp;3N), and TReg cells (Fig.\u0026nbsp;3O). The expression levels of SLC2A1 were positively connected with macrophages (Fig.\u0026nbsp;3P), Th2 cells (Fig.\u0026nbsp;3Q), and Tcm (Fig.\u0026nbsp;3R). On the other hand, the expression levels of MPST were negatively correlated with macrophages (Fig.\u0026nbsp;4B), aDC (Fig.\u0026nbsp;4C), T helper cells (Fig.\u0026nbsp;4D), Tcm (Fig.\u0026nbsp;4E), Tgd (Fig.\u0026nbsp;4F), and Th2 cells (Fig.\u0026nbsp;4G). Additionally, the expression levels of MPST were positively connected with Th17 cells (Fig.\u0026nbsp;4H), pDC (Fig.\u0026nbsp;4I), NK cells (Fig.\u0026nbsp;4J), NK CD56dim cells (Fig.\u0026nbsp;4K), NK CD56bright cells (Fig.\u0026nbsp;4L), neutrophils (Fig.\u0026nbsp;4M), iDC (Fig.\u0026nbsp;4N), and cytotoxic cells (Fig.\u0026nbsp;4O). The connection between MPST expression and immune cell infiltration is illustrated in Fig.\u0026nbsp;4A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4Important immune genes associated with tumor immune escape\u003c/h2\u003e \u003cp\u003eCD96, CTLA-4 and PDCD-1 are significant immune checkpoint proteins associated with tumor immune escape. We found that in UCEC samples of TCGA dataset, the expression level of SLC2A1 was negatively contacted with the expression levels of CD96, CTLA-4, and PDCD-1 (Fig.\u0026nbsp;5A-C). The expression level of MPST was positively associated with that of CD96, CTLA-4, and PDCD-1 (Fig.\u0026nbsp;5E-G). The TP53 tumor inhibitor gene is low expressed in normal cells but highly expressed in malignant tumors. We found that SLC2A1 and MPST were positively connected with the expression level of TP53 (Fig.\u0026nbsp;5D, H). Furthermore, we can also obtain the above results based on the TISIDB database (Fig.\u0026nbsp;5I-N).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5Correlation analysis of the methylation levels of SLC2A1 and MPST with UCEC\u003c/h2\u003e \u003cp\u003ePromoter DNA methylation has been shown to affect transcriptional repression and participate in tumorigenesis, considering that functional enrichment analysis identified SLC2A1 likely involved in the methylation process, we then analyzed methylation of SLC2A1 and MPST expression. We compared the methylation values of SLC2A1 and MPST between normal and tumor tissues, and from Fig.\u0026nbsp;6A, the methylation values of SLC2A1 were significantly decreased at P \u0026lt; 0.05, with no significant difference (P \u0026gt; 0.05) in MPST (Fig.\u0026nbsp;6B), suggesting that the transcriptional expression of SLC2A1 may be related to promoter hypomethylation. We analyzed the level of DNA methylation in the SLC2A1 gene as well as the prognostic value of CpG islands in the SLC2A1 gene using the MetSurv tool. The results revealed 27 methylated CpG islands in SLC2A1. Including cg07803811, cg03128534, cg22176566, cg07499643,cg21877974,cg20345840, cg01907688, cg04287330, cg26188818, cg26681016, cg08159148, cg13790796, cg03106288, cg12656391 showing reduced DNA methylation levels (Fig.\u0026nbsp;6C). Furthermore, MethSurv-analysis showed that patients had lower overall survival than those with high SLC2A1 methylation (P \u0026lt; 0.05)(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We found that the seven CpG sites located on the CpG islands had a poor prognosis, including cg00102166, cg01924561, cg12656391, cg15089806, cg20294984, cg21474257, and cg22025263 (Fig.\u0026nbsp;6D-J). The reduction in SLC2A1 methylation in these seven CpG islands, compared to patients with higher CpG methylation in SLC2A1, was connected with poorer overall survival in UCEC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.6Genetic Alterations in SLC2A1 and MPST Are Not Connected With Survival Outcomes in UCEC Patients\u003c/h2\u003e \u003cp\u003eWe researched the SLC2A1 and MPST genes based on 549 UCEC samples with mutations, and SLC2A1 gene changes were observed only in 4% of UCEC patients (Fig.\u0026nbsp;7A), K-M survival curve test showed OS (P = 0.809) (Fig.\u0026nbsp;7C). MPST gene change was observed only in 1.7% of UCEC patients (Fig.\u0026nbsp;7B), and K-M survival curve test showed OS (P = 0.587) (Fig.\u0026nbsp;7D). There was no significant difference between SLC2A1 as well as between patients with or no genetic change in the MPST gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.7Baseline data of the UCEC patients\u003c/h2\u003e \u003cp\u003eClinical data and expression data of 543 UCEC cases were downloaded from TCGA data in February 2023 are shown in (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study included 206 patients under 60 years old (38.1% of the total) and 343 patients over 60 years old (61.9%). The multitude of patients were diagnosed with stage I disease (62.4%), followed by stage II (22.8%), stage III (9.4%), and stage IV (5.3%). The primary treatment outcomes for UCEC were as follows: stable disease (1.3%), progressive disease (4.2%), partial response (2.5%), and complete response (92%). The majority of tissues analyzed were derived from endometrioid tissue (75%), while 21% were serous tissue. In terms of residual tumor, 90.7% of patients had no residual tumor, and 9.3% had residual tumor. Regarding histological grade, 18.4% of UCEC patients had high differentiation (G1), 22.6% had moderate differentiation (G2), and the majority had low differentiation (G3), accounting for 59%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.1.8Clinical correlation analysis of SLC2A1 and MPST\u003c/h2\u003e \u003cp\u003eTo further understand the relevance and the mechanisms underlying the expression of SLC2A1, MPST in UCEC, we investigated the asociation betweens SLC2A1, MPST expression and clinical features. The expression level of SLC2A1 was higher in UCEC tissues compared to normal tissues (Fig.\u0026nbsp;8A). Correlation analysis prompted that there were significant differences between SLC2A1 and clinical stage (Fig.\u0026nbsp;8B), histological type (Fig.\u0026nbsp;8C), and histological grade (Fig.\u0026nbsp;8F), no significant difference between age and tumor remnant(Fig.\u0026nbsp;8D, E). In the Fig.\u0026nbsp;8, the expression level of MPST was higher in UCEC tissues compared to normal tissue (Fig.\u0026nbsp;8G). Correlation analysis showed significant differences between MPST and clinical stage (Fig.\u0026nbsp;8H), histological type (Fig.\u0026nbsp;8L), histological grade (Fig.\u0026nbsp;8I) and age (Fig.\u0026nbsp;8J), and no significant differences between tumor residues(Fig.\u0026nbsp;8K). Immunohistochemical staining of the HPA database also confirmed higher levels of SLC2A1 and MPST in tumor tissue than in vicinity normal endometrial tissue (Fig.\u0026nbsp;8M, N).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.1.9Prognostic analysis\u003c/h2\u003e \u003cp\u003eFigure 9 illustrates the diagnostic potential of SLC2A1 and MPST genes in distinguishing between normal and tumor tissues. We found that SLC2A1 has a strong discriminatory power, and the area under the ROC curve (AUC) of SLC2A1 was 0.844 as shown in Fig.\u0026nbsp;9A. Similarly, the ROC curve for MPST had an AUC of 0.761 (Fig.\u0026nbsp;9B), suggesting its usefulness in diagnostic identification in UCEC. Furthermore, the Kaplan-Meier survival curves demonstrated that higher expression of MPST was contacted with better overall survival (OS) prognosis (Fig.\u0026nbsp;9F), whereas lower expression of SLC2A1 was contacted with improved OS prognosis (Fig.\u0026nbsp;9E). The OS survival analysis, conducted using the UALCAN database, consistently supported these findings (Fig.\u0026nbsp;9C, D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.1.10Construction and evaluation of the nomogram model\u003c/h2\u003e \u003cp\u003eTo examine the influence of MPST expression on the outcome of endometrial cancer, we conducted a univariate Cox regression analysis of MPST (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Using the results from this analysis, we structured a nomogram model to validate its prognostic value. Additionally, we utilized calibration curves to assess the veracity of the nomogram model in forecasting survival at 1, 3, and 5 years. Our findings demonstrated that MPST exhibited strong predictive capability, as evidenced by the good accuracy of the 1-year, 3-year, and 5-year survival prediction calibration curves of the nomogram model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1.1Source and treatment of the samples\u003c/h2\u003e \u003cp\u003eWe acquired RNAseq data and relevant clinical information for 587 samples from the TCGA-UCEC project of the Cancer Gene Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After removing samples without clinical information and duplicates, we converted the RNA sequencing data from FPKM format to transcript per million reads (TPM) format. Based on the median expression values for SLC2A1 and MPST, UCEC patients were divided into low and high expression groups. Statistical analysis was visualized using the ggplot2 software package and conducted using R software v3.6.3. The Wilcoxon Rank sum test identified two statistically significant data sets (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical significance was determined as *** (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ** (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), * (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and ns (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We confirm that whole necessary informed consent was acquired anterior to data collection as per the guidelines provided for accessing the TCGA database, which is publicly accessible.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2Clinical correlation analysis\u003c/h2\u003e \u003cp\u003eWe conducted correlation analysis of SLC2A1 and MPST with tumor stage using the R software, specifically utilizing the ggplot2 package. The variables considered in the analysis included clinical stage, histological grade, pathological stage, histological type and pathological stage. Futhermore, we generated a Kaplan-Meier plot and performed diagnostic ROC curve analysis which are using the R packages pROC and ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3Construction of the protein-protein interaction network and GO-KEGG analysis\u003c/h2\u003e \u003cp\u003eTo visualize the protein-protein interaction(PPI) network, we employed Cytoscape (version 3.7.2) and accessed the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A protein interaction score threshold of 0.4 was used to determine statistically significant interactions. Subsequently, we identified 10 functional partner genes for further analysis, focusing on GO term enrichment and KEGG pathway analysis to gain insights into the function of SLC2A1 and MPST.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4Methylation\u003c/h2\u003e \u003cp\u003eWe explored the DNA methylation levels of the SLC2A1 and MPST genes and assessed the prognostic significance of CpG islands within these genes for patients with UCEC. This analysis was emerged using the MetSurv database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/methsurv/\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/methsurv/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was stipulated as a \u003cem\u003eP\u003c/em\u003e-value below 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.1.5Analysis of the immune infiltration\u003c/h2\u003e \u003cp\u003eWe utilized the study by Bindea et al.(16) to extract marker genes for 24 immune cells. Using GSEA (ssGSEA) on UCEC mRNA TPM data, we calculated the levels of tumor-infiltrating immune cells(17). We performed correlation search between the expression levels of SLC2A1 and MPST genes, as well as the relationship between immune checkpoint genes (including CD96, CTLA4, and PDCD1) and TP53. This analysis was conducted on UCEC samples from the TCGA database utilizing Spearman\u0026rsquo;s correlation analysis and the \u0026ldquo;ggplot2\u0026rdquo; (v3.3.3) R package. Correlations were defined significant if the \u003cem\u003eP\u003c/em\u003e-value was below 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.1.6Gene alterations in UCEC samples\u003c/h2\u003e \u003cp\u003eWe utilized cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for conducting log-rank tests and K-M survival curve analysis. These analyses were performed to assess the prognostic significance of genomic alterations in the SLC2A1 and MPST genes. Statistical significance was determined utilizing a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.1.7Assessment of the Prognostic Significance of MPST Expression in UCEC\u003c/h2\u003e \u003cp\u003eTo evaluate the survival outcomes of UCEC patients, we conducted Kaplan-Meier survival curve analysis and multivariate and univariate Cox regression analyses, focusing on the expression levels of SLC2A1 and MPST. Furthermore, we employed the \u0026ldquo;pROC\u0026rdquo; (v1.17.0.1), \u0026ldquo;timeROC\u0026rdquo; (v0.4), and \u0026ldquo;ggplot2\u0026rdquo; (v3.3.3) R packages to perform diagnostic ROC curve and nomogram model analyses. The purpose of these analyses was to determine if MPST expression levels can be used to predict UCEC diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.1.8Statistical analysis\u003c/h2\u003e \u003cp\u003eTo conduct the statistical analysis, we utilized R (v.3.6.3). Group differences were contrasted utilizing either the Wilcoxon rank-sum test or the t-test. For the K-M survival analysis, we employed the Telog-rank test. And for the COX regression analysis, we used hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) to calculate .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.1.9Availability of data and materials\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we observed a notable overexpression of SLC2A1 in 22 out of 33 human cancer tissues, and MPST was found to be upregulated in 13 out of 33 tumor tissues. These findings indicate a high expression of both SLC2A1 and MPST in clinical samples of endometrial cancer, as well as in the TCGA database. The SLC2A1 gene encodes the solute carrier of the glucose transporter (GLUT) family, which is responsible for the initial step in glucose utilization. Previous studies have shown that SLC2A1 is a larvaceous prognostic biomarker for immunotherapy in lung adenocarcinoma(18), and that in colorectal cancer, METTL3 plays an oncogenic role by stabilizing HK2 and SLC1A2 mRNA through the IGF2BPs axis, thereby regulating glycolytic metabolism and cell proliferation(19). As a result of these findings, there is evidence that SLC2A1 and MPST can be used as diagnostic markers in UCEC.The analysis of SLC2A1 Related DEGs showed a significant correlation with various cancer-related pathways, including carbon metabolism, thyroid hormone signaling, HIF 1 signaling, membrane rafts, cerebral cortex development, and lactate metabolism. On the other hand, KEGG analysis revealed that MPST is primarily involved in cysteine and methionine metabolism, leading to the production of homocysteine. Homocysteine plays a critical role in physiological processes such as cell cycle progression and maintenance of cell homeostasis(20). Based on the aforementioned discussion, it can be inferred that SLC2A1 potentially plays a role in the progression of UCEC by modulating carbon metabolism and lactic acid metabolism within cancer cells. Additionally, there may be a correlation between MPST and UCEC development, as it is involved in cysteine and methionine metabolism.\u003c/p\u003e \u003cp\u003eOur study provides evidence of a potential association between the expression of SLC2A1 and MPST, and immune cell infiltration. The expression of SLC2A1 was found to have a negative association with various immune cell types, including B cells, T cells, CD56dim cells, eosinophils, iDC cells, mast cells, NK cells, CD56bright cells, and pDC cells. Similarly, the expression of MPST was negatively correlated with macrophages, T helper cells, Tcm, Th2 cells, and Tgd cells. It is worth noting that M1 macrophages are known for their potential antitumor activity. NK cells play a crucial role in inducing apoptosis in tumor cells by binding to Fas ligand (FasL) or tumor necrosis factor-associated apoptosis-inducing ligand (TRAIL) receptors. Furthermore, studies have demonstrated that highly activated multifunctional CD4 (+) T cells play a significant role in enhancing and sustaining the overall antitumor immunity within the host(21). Our findings indicate that the overexpression of SLC2A1 and MPST contributes to the immune evasion mechanism of UCEC cells, fostering their progression and growth.\u003c/p\u003e \u003cp\u003eCD96, CTLA-4, and PDCD1 are the key proteins contacted with tumor immune escape(22). Although CD96 inhibitors have not yet undergone clinical trials, preclinical data demonstrate their efficacy in inhibiting experimental or spontaneous cancer metastasis in various mouse models(23). Moreover, TP53 exhibits high expression levels in malignancies, and its mutations are linked to unfavorable prognoses in various human cancers(24). TP53 mutations suppress antitumor immunity and reduce the efficacy of cancer immunotherapy(25\u0026ndash;28). Thus, we assessed the correlation between the expression levels of SLC2A1 and MPST and immune checkpoint genes, namely CD96, CTLA-4, PDCD1, and TP53. There was a significant correlation between the expression levels of SLC2A1 and immune checkpoint genes, as well as TP53 expression. This suggests that targeting SLC2A1 and MPST could potentially enhance the effectiveness of immunotherapy in patients with UCEC.\u003c/p\u003e \u003cp\u003ePromoter DNA methylation plays a vital role in transcriptional repression and contributes to tumorigenesis. Alterations in gene methylation have been linked to the development, expansion, and advancement of different types of cancer(29\u0026ndash;31). UCEC patients' prognosis was associated with methylation of the SLC2A1 gene. We found that hypermethylation at specific sites, namely cg00102166, cg01924561, cg12656391, cg15089806, cg20294984, cg21474257, and cg22025263, was associated with poorer overall survival.\u003c/p\u003e \u003cp\u003eTP53, CTNNB1, PTEN, ARID1A, and KMT2D gene mutations have notable implications for the diagnosis and treatment of UCEC patients(32). In the present study, we observed a low incidence of SLC2A1 and MPST gene mutations in UCEC tissues, with rates of only 4% and 1.7% respectively. Additionally, we found no association between these mutations and overall survival (OS) in UCEC patients.\u003c/p\u003e \u003cp\u003eOur study found significant associations between the expression level of SLC2A1 in UCEC tissues and clinical stage, histological type, histological grade, and overall survival. Similarly, the expression level of MPST was remarkably correlated with age, histological type, histological grade, clinical stage, and overall survival. Moreover, higher expression levels of SLC2A1 were linked to a poorer prognosis in UCEC patients, while lower expression levels of MPST were associated with a poorer prognosis in UCEC patients. The K-M survival curves demonstrated a notable association between higher expression of SLC2A1 and lower overall survival, while lower expression of MPST correlated with higher overall survival. The differential diagnosis values for UCEC were 0.844 and 0.761 for SLC2A1 and MPST, respectively. These findings indicate that SLC2A1 and MPST have potential as diagnostic and prognostic biomarkers for UCEC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, we have confirmed the diagnostic and prognostic value of SLC2A1 and MPST in UCEC. SLC2A1 is found to be overexpressed in tumor tissues of UCEC patients, while MPST shows high expression in these tissues as well. It has been shown that SLC2A1 and MPST expression levels correlate with the presence of immune cells in tumors, suggesting they might play a role in immune response therapy for UCEC patients. SLC2A1 is believed to play a key role in cancer cell proliferation and metastasis through multiple potential mechanisms, including cancer central carbon metabolism, the HIF-1 signaling pathway, and lactic acid metabolic processes. On the other hand, MPST appears to take part in cysteine and methionine metabolism, contributing to the development of UCEC. The expression levels of SLC2A1 and MPST in UCEC tumors are also related to the infiltration status of different immune cell types, which may influence immune therapy. Moreover, the methylation and gene expression of SLC2A1 have been found to be associated with the prognosis of UCEC. Considering these findings, targeting SLC2A1 and MPST could serve as a potential therapeutic strategy, and their expression levels could be useful as diagnostic indicators. Moreover, these discoveries open up opportunities for the development of novel immunotherapy approaches in UCEC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUCEC:\u0026nbsp;Uterine corpus endometrial carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH2S:hydrogen sulfide\u003c/p\u003e\n\u003cp\u003eCBS:cystathionine-synthase\u003c/p\u003e\n\u003cp\u003eCSE:cystathionine-lyase\u003c/p\u003e\n\u003cp\u003e3-MST:3-mercaptopyruvate sulfurtransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI:protein-protein interaction\u003c/p\u003e\n\u003cp\u003eHRs:hazard ratios\u003c/p\u003e\n\u003cp\u003eCIs:confidence intervals\u003c/p\u003e\n\u003cp\u003eACC:adrenocortical cancer\u003c/p\u003e\n\u003cp\u003eBLCA:bladder urothelial carcinoma\u003c/p\u003e\n\u003cp\u003eBRCA:breast invasive carcinoma cervical\u003c/p\u003e\n\u003cp\u003eCESC:endocervical cancer\u003c/p\u003e\n\u003cp\u003eCHOL:cholangiocarcinoma\u003c/p\u003e\n\u003cp\u003eCOAD:colon adenocarcinoma\u003c/p\u003e\n\u003cp\u003eESCA:esophageal carcinoma\u003c/p\u003e\n\u003cp\u003eGBM:glioblastoma multiforme\u003c/p\u003e\n\u003cp\u003eHNSC:head and neck squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eKIRC:kidney clear cell carcinoma\u003c/p\u003e\n\u003cp\u003eLGG:brain lower grade glioma\u003c/p\u003e\n\u003cp\u003eLUSC:lung squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eLIHC:liver hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003eLUAD:lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eOV:ovarian serous cystadenocarcinoma\u003c/p\u003e\n\u003cp\u003ePAAD:pancreatic adenocarcinoma\u003c/p\u003e\n\u003cp\u003eREAD:rectum adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSTAD:stomach adenocarcinoma\u003c/p\u003e\n\u003cp\u003eTGCT:testicular germ cell tumor\u003c/p\u003e\n\u003cp\u003eTHCA:thyroid carcinoma\u003c/p\u003e\n\u003cp\u003eUCS:uterine carcinosarcoma\u003c/p\u003e\n\u003cp\u003eBP:biological process\u003c/p\u003e\n\u003cp\u003eCC:cellular component\u003c/p\u003e\n\u003cp\u003eMF:molecular function\u003c/p\u003e\n\u003cp\u003eOS:overall survival\u003c/p\u003e\n\u003cp\u003eGLUT:glucose transporter\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eGXX, LYX, CBR, and XCC obtained and analyzed the data, and performed the software application and data visualization. XXY wrote the original draft of the manuscript. DJX and XXY contributed to conception and design of the study, GXX, LYX, CBR, and XCC wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017;67(1):7-30.\u003c/li\u003e\n\u003cli\u003eThrastardottir TO, Copeland VJ, Constantinou C. The Association Between Nutrition, Obesity, Inflammation, and Endometrial Cancer: A Scoping Review. Curr Nutr Rep. 2023;12(1):98-121.\u003c/li\u003e\n\u003cli\u003eLei S, Cao Y, Sun J, Li M, Zhao D. H(2)S promotes proliferation of endometrial stromal cells via activating the NF-kappaB pathway in endometriosis. Am J Transl Res. 2018;10(12):4247-57.\u003c/li\u003e\n\u003cli\u003eAncey PB, Contat C, Boivin G, Sabatino S, Pascual J, Zangger N, et al. 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Gynecol Oncol. 2020;157(1):55-61.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO and KEGG enrichment analyses of SLC2A1 and functional partner genes in UCEC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elactate metabolic process\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.05e-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0050767\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eregulation of neurogenesis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.91e-07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0051960\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eregulation of nervous system development\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0060284\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eregulation of cell development\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.94e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0045926\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative regulation of growth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0045121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emembrane raft\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0098857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emembrane microdomain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0042470\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emelanosome\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0048770\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epigment granule\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0072562\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eblood microparticle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0035374\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echondroitin sulfate binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043621\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprotein self-association\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002039\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep53 binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0042826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehistone deacetylase binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0008201\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eheparin binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral carbon metabolism in cancer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57e-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04919\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThyroid hormone signaling pathway\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMitophagy - animal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenal cell carcinoma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO and KEGG enrichment analyses of MPST and functional partner genes in UCEC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006790\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esulfur compound metabolic process\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.87e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0043650\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edicarboxylic acid biosynthetic process\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etRNA wobble uridine modification\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-oxoglutarate metabolic process\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0002097\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etRNA wobble base modification\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emitochondrial matrix\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.23e-07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005758\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emitochondrial intermembrane space\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0334\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0031970\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eorganelle envelope lumen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0374\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016783\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esulfurtransferase activity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07e-12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0016782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etransferase activity, transferring sulphur-containing groups\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25e-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0030170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epyridoxal phosphate binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0070279\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evitamin B6 binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48e-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0019842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evitamin binding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77e-05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00270\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.16E-10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00920\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSulfur metabolism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14E-07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa01230\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiosynthesis of amino acids\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.35E-07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00360\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa01210\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-Oxocarboxylic acid metabolism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of methylation levels in the CpG sites of the SLC2A1 gene on the prognosis of UCEC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg00102166\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg01907688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.734\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg01924561\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg03106288\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.455\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg03128534\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.697\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg04287330\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg05034603\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg05802386\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg06094523\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg07499643\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg07803811\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg08159148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.968\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg09502149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg12101479\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg12656391\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg13790796\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg15089806\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg16738646\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg20282814\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg20294984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg20345840\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.745\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg21474257\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg21877974\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg22025263\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg22176566\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg26188818\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCg26681016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline data sheet for UCEC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elevels\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e543\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (62.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (9.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (22.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (5.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (4.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.5%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436 (92%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (38.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (61.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological type, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometrioid\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407 (75%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (4.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerous\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (21%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (90.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (5.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (18.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (22.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (59%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor invasion(%), n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (55.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211 (44.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS event, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452 (83.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (16.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis between UCEC clinical characteristics and OS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.553 (2.362–5.344)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.392 (2.443–7.896)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u0026amp;R2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.112 (1.774–5.459)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.951 (1.022–3.725)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor invasion(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e475\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd 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colname=\"c3\"\u003e \u003cp\u003e0.640 (0.425–0.964)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.567 (0.335–0.961)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e 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immune cell infiltration, immune checkpoint, methylation, gene mutation","lastPublishedDoi":"10.21203/rs.3.rs-3876179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3876179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUterine corpus endometrial carcinoma (UCEC) represents the prevailing neoplasm affecting the female reproductive system. The early diagnosis of UCEC is crucial for improving the survival rate of patients. In this study, we study the two specific genes: SLC2A1, which encodes the facilitated glucose transporter, and MPST, which encodes 3-mercaptopyruvate sulfurtransferase. SLC2A1 and MPST have been identified as important regulators in cancer. Nevertheless, it is still unknown how SLC2A1 and MPST function and operate within endometrial cancer. The objective of this study is to investigate the potential significance of SLC2A1 and MPST in terms of diagnosis and prognosis for UCEC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from the TCGA database, we analyzed the levels of expression for SLC2A1 and MPST in 33 various cancer types. Then we created a protein-protein interaction (PPI) network that incorporated SLC2A1, MPST, and relevant genes.Furthermore, we performed KEGG/GO pathway enrichment analysis on these genes. We utilized Spearman correlation analysis to examine the correlation between SLC2A1 and MPST expression and the infiltration of immune cells, as well as the association between immune checkpoint genes and TP53. We analyzed DNA methylation changes in the SLC2A1 and MPST genes and their impact on survival outcomes. We investigated the correlation between SLC2A1 and MPST expression and clinicopathological features of patients with endometrial cancer Additionally, we evaluated the diagnostic and prognostic predictive capabilities of SLC2A1 and MPST.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the tumor tissues, MPST and SLC2A1 expression levels increased significantly. Our research revealed a noteworthy association between the levels of expression of SLC2A1 and MPST, and the infiltration of immune cells, the presence of immune checkpoint genes, and TP53 in UCEC tissues. Furthermore, there was a remarkable association between the expression levels of SLC2A1 and MPST and the clinical stage, histological type, and histological grade in UCEC tissues. Our analysis using Kaplan-Meier survival curves and diagnostic subject operating characteristics (ROC) curves revealed that both SLC2A1 and MPST exhibit robust diagnostic and prognostic significance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study we conducted emphasizes the diagnostic and prognostic potential of SLC2A1 and MPST as biomarkers for UCEC. These findings offer encouraging prospects for targeted therapies.\u003c/p\u003e","manuscriptTitle":"Unveiling the Role of SLC2A1 and MPST in Uterine Corpus Endometrial Carcinoma: Diagnostic and Prognostic Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 18:54:11","doi":"10.21203/rs.3.rs-3876179/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aff1a74e-f752-44c2-8ad2-68037c6d4a35","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T04:44:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 18:54:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3876179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3876179","identity":"rs-3876179","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}