Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets

Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets | 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 Article Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets Yahui Shen, Yanping Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4383100/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 Uterine corpus endometrial carcinoma (UCEC), a prevalent malignancy in the female reproductive system, has witnessed a 30% increase in recent year. Recognizing the significance of early treatment in reducing patient mortality, the identification of potential biomarkers for UCEC plays a crucial role in early diagnosis. This study was to identify key genes associated with UCEC utilizing the Gene Expression Omnibus (GEO) database, followed by validating their prognostic value across multiple databases. Analysis of four UCEC databases (GSE17025, GSE36389, GSE63678, GSE115810) yielded 72 co-expressed genes. KEGG and GO enrichment analyses revealed their involvement in physiological processes such as transcriptional misregulation in cancer. Constructing a Protein-Protein Interaction (PPI) network for these 72 genes, the top 10 genes with significant interactions were identified. Survival regression analysis highlighted NR3C1 as the gene with a substantial impact on UCEC prognostic outcomes. Differential expression analysis indicated lower expression of NR3C1 in UCEC compared to normal endometrial tissue. Cox regression analysis, performed on clinical datasets of UCEC patients, identified clinical stage III, clinical stage IV, age, and NR3C1 as independent prognostic factors influencing UCEC outcomes. The LinkedOmics online database revealed the top 50 positively and negatively correlated genes with NR3C1 in UCEC. Subsequent investigations into the relationship between NR3C1 and tumor-infiltrating immune cells were conducted using R software. Gene set enrichment analysis (GSEA) provided insights into NR3C1 -related genes, showing enrichment in processes such as Ribosome, Oxidative phosphorylation in UCEC. Collectively, these comprehensive analyses suggest that NR3C1 may serve as a potential biomarker indicating the prognosis of UCEC. Biological sciences/Cancer/Gynaecological cancer/Endometrial cancer Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Tumour biomarkers NR3C1 Uterine corpus endometrial carcinoma Gene set enrichment analysis Kyoto Encyclopedia of Genes and Genomes Gene Ontology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1．Introduction Uterine corpus endometrial carcinoma (UCEC), a prevalent malignancy originating from the inner lining of the uterus, or endometrium, poses a substantial global health challenge. This condition, also known as endometrial carcinoma, displays a diverse array of characteristics that significantly influence its diagnosis, treatment, and overall prognosis. Numerous risk factors contribute to the occurrence and progression of UCEC, including continuous exposure to estrogen, metabolic irregularities such as obesity and diabetes, early onset of menstruation, infertility, delayed onset of menopause, carrying susceptibility genes, and advanced age (greater than 60). Clinically, UCEC is categorized into type I and type II. The former is hormone-dependent, predominantly presenting as endometrioid carcinoma with a more favorable prognosis, while the latter is hormone-independent and typically associated with a poorer prognosis. According to statistics from the National Cancer Center of China in 2019, the incidence rate of UCEC is 10.28 per 100,000, with a mortality rate of 1.9 per 100,000, constituting 3.88% of female malignant tumors, following closely behind cervical cancer. Furthermore, UCEC holds the leading position in developed countries such as Europe and the United States [ 1 – 4 ] . For instance, in the United States alone, it is estimated that 65,950 new cases emerged in 2022, resulting in 12,550 deaths attributed to this disease [ 5 ] . The gene NR3C1 , located on the reverse chain of human chromosome 5q31, plays a crucial role in encoding glucocorticoid receptors in human cells. This gene exhibits notable complexity with 16 brief variants, generating 3 splice isomers known as GR- α, GR- β, and GR-P [ 6 ] . Extensive research has demonstrated that NR3C1 is predominantly expressed in various cellular compartments, including the cell membrane, cytoplasmic sol, and nucleus. NR3C1 serves multiple physiological functions within human cells, encompassing glucocorticoid receptor activity, identical protein binding activity, and protein kinase binding activity. Furthermore, NR3C1 is implicated in the positive regulation of pri-miRNA transcription by RNA polymerase II. It actively participates in fundamental cellular processes such as glandular synthesis, glucocorticoid signaling pathways, and the regulation of cellular biosynthesis processes [ 7 ] . In the human system, NR3C1 is primarily expressed in vital systems such as the digestive system, central nervous system, urogenital system, and respiratory system. Recent studies have revealed that dysregulation in NR3C1 expression is associated with the occurrence and progression of various diseases, including but not limited to anorexia nervosa, severe depressive disorder, renal cell carcinoma, among others [ 8 ] . Despite these insights, many aspects of the relationship between the gene NR3C1 and UCEC remain unclear, necessitating further exploration and investigation. In this study, we conducted a comprehensive analysis by initially identifying differentially expressed genes (DEGs) between normal and UCEC tissues based on the UCEC dataset in the Gene Expression Omnibus (GEO) database. Subsequently, a series of bioinformatics analyses, including survival analysis, Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis, were meticulously performed on the identified DEGs. The findings of our study underscore the pivotal role of the gene NR3C1 in influencing the occurrence and progression of UCEC. Notably, NR3C1 emerges as a significant player not only in the pathogenesis of the disease but also in its diagnostic and prognostic aspects. These insights contribute to a deeper understanding of the molecular landscape of UCEC, emphasizing the potential significance of NR3C1 as a key molecular player in the complex dynamics of the disease. The implications of NR3C1 in UCEC further underscore its importance as a potential biomarker and therapeutic target, warranting continued exploration and validation in future studies. 2. Material and Methods 2.1 Data collection During the data collection phase, we initiated the process by searching for the keyword "uterine corpus endometrial carcinoma" in the GEO database on PUBMED ( https://www.ncbi.nlm.nih.gov/geo/ ). The results obtained from this search were meticulously screened to ensure relevance and reliability. Subsequently, a total of four datasets GSE17025 [ 9 ] ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025 ), GSE36389 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE36389), GSE63678 [ 10 ] ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63678 ) and GSE115810 [ 11 ] ( https://www.ncbi.nlm.nih.gov/geo/query/acc . cgi? acc = GSE115810) were included in this study. The GSE17025 includes 12 normal endometrial tissues and 91 UCEC tissues. GSE36389 contains 7 normal endometrial tissues and 13 UCEC tissues. GSE63678 comprises 5 normal endometrial tissues and 7 UCEC tissues. GSE115810 is consists of 3 normal endometrial tissues and 24 UCEC tissues. Importantly, it is crucial to note that all four datasets were derived from online sources, and as such, they were not subject to review by an Ethics Committee. This ethical consideration aligns with the nature of online, publicly available data utilized in our study. Following the selection of four datasets, we utilized Pubmed GEO2R to conduct a comprehensive analysis of them [ 12 – 13 ] ( https://www.ncbi.nlm.nih.gov/geo/geo2r/ ). DEGs between normal endometrial tissues and UCEC tissues were systematically identified. To refine our findings, genes with multiple probes and those lacking corresponding probe groups were meticulously excluded, employing a significance threshold of P < 0.05. Subsequently, a meticulous integration of the resulting genes from each dataset was performed using an online Venn map ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ). This strategic approach unveiled a subset of coexpressed DEGs shared among all four datasets, which represents a robust set of molecular signatures consistently implicated in UCEC pathogenesis. 2.2 Enrichment Analysis of GO and KEGG Pathways in DEG To delve into the biological insights of DEGs in UCEC, we utilized the DAVID online database ( https://david.ncifcrf.gov/ ) for comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses [ 14 – 17 ] . GO analysis systematically annotated gene properties across Cellular Component (CC), Molecular Function (MF), and Biological Process (BP), offering a nuanced understanding of gene roles within cellular contexts. Meanwhile, KEGG pathway analysis provided a holistic view of gene involvement in diverse metabolic pathways, enriching our comprehension of intricate cellular functions [ 18 ] . This integrated and amplified analytical approach not only uncovered crucial biological mechanisms but also holds the potential to unveil promising therapeutic targets and diagnostic biomarkers for UCEC. The depth and breadth of this exploration significantly contribute to advancing our understanding of the molecular landscape underlying UCEC, paving the way for more targeted and effective interventions in the clinical landscape. 2.3 Construction of PPI Network and Screening of Key Genes Protein-Protein Interaction (PPI) Networks leverage the STRING online database to to analyze interactions among proteins, aiming to comprehend the involvement of related proteins in biological signal transmission, gene expression regulation, energy metabolism, material metabolism, cell cycle, and other life processes [ 19 ] ( https://string-db.org/ ). The PPI network serves as a valuable tool for conducting in-depth biological information analysis and identifying key core genes pivotal to the onset and progression of diseases. Following the establishment of the PPI network for DEGs through the STRING online database, preprocessing data for DEGs was acquired from the STRING website to facilitate subsequent enrichment analysis. This data was then employed to visualize the PPI network using Cytoscape 3.10.0 ( https://cytoscape.org/ ). Subsequent module analysis was carried out using the Mcode and Centiscape plug-ins within Cytoscape [ 20 ] . The Cytohubba plug-in in Cytoscape was utilized to screen and identify the top ten key genes among DEGs for further detailed analysis. 2.4 Validation of hub genes by the GEPIA After screening with Cytoscape software, we identified ten key genes, which underwent further verification through the Gene Expression Profiling Interactive Analysis (GEPIA) online analysis website ( http://gepia.cancer-pku.cn/ ). GEPIA serves as an online platform for biological information analysis, compiling the expression values of each searchable gene across various tumor samples. It can calculate the expression levels of genes in specific tumors [ 21 – 22 ] . GEPIA offers a wide range of analyses, including tumor/normal differential expression profile analysis, expression distribution, pathological stage analysis, survival analysis, identification of similar genes, gene expression correlation analysis, and dimension reduction analysis. In this study, we utilized GEPIA specifically for survival analysis of the ten key genes. The analysis results were then screened based on a significance threshold of P < 0.05 to identify hub genes. 2.5 Cox proportional risk regression analysis In order to further explore the impact of clinical factors and hub genes on the onset and progression of UCEC, Cox proportional hazards regression analysis was employed in this study [ 23 ] . The first step involved downloading UCEC-related gene expression information and clinical data from the UCSC Xena website ( https://xenabrowser.net/datapages/ ). Subsequently, the data underwent preprocessing to extract pertinent variables such as age, tumor stage, survival time, follow-up outcome, and key gene expression levels. Following data preparation, Cox regression analyses were carried out for UCEC using R software. These analyses aimed to assess the influence of variables such as age and tumor stage on the outcome of cases. The analysis outcomes were then screened based on a significance threshold of P-value < 0.05. Ultimately, the variable factors influencing the prognosis of endometrial carcinoma were determined through this comprehensive analytical approach. 2.6 ROC and DCA curve analysis The Receiver Operating Characteristic curve (ROC curve) is a graphical tool used for assessing the overall accuracy of a classifier, particularly in binary classification problems. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various decision thresholds [ 24 ] . The area under ROC curve (AUC) generally indicates better classifier performance. In this study, we employed the pROC package ( https://cran.r-project.org/web/ packages/pROC/index.html ) in the R software to evaluate the sensitivity and specificity of NR3C1 in diagnosing UCEC. Decision Curve Analysis (DCA) is a method used to evaluate the clinical utility of predictive models, diagnostic tests, or molecular markers [ 25 ] . Unlike the ROC curve, DCA considers the preferences of patients or decision-makers. By integrating these preferences into the analysis, DCA aims to provide a more practical evaluation of the diagnostic value of a marker. This concept has gained popularity in clinical analysis as it aligns more closely with real-world decision-making scenarios [ 26 ] . In this study, we used clinical data from the UCSC Xena website and the ggDCA ( https://github.com/cran/ggDCA ) and survival package ( https://cran.r-project.org/web/packages/survival/index.html ) in R software to draw DCA curves. These curves were utilized to assess the diagnostic value of NR3C1 for UCEC in a manner that considers the practical implications of clinical decision-making. 2.7 Analysis of NR3C1 protein expression level by the HPA database The Human Protein Atlas (HPA) database is a comprehensive resource that integrates data from proteomics, transcriptomics, and systems biology to provide detailed information on the tissue and cell distribution of around 26,000 human proteins. Notably, it covers both tumor tissues and normal tissues, allowing for a holistic understanding of protein expression patterns. The database facilitates the creation of intricate maps depicting the distribution of proteins across various biological contexts, including tissues, cells, and organs [ 27 ] ( https://www.proteinatlas.org/ ). In the context of this study, our focus was on comparing the protein expression level of NR3C1 in UCEC and normal endometrial tissues. By leveraging the wealth of information available in the HPA database, we aimed to gain insights into how the expression of NR3C1 varies between UCEC and normal endometrial tissues. This comparative analysis can contribute valuable data to our understanding of the potential role of NR3C1 in UCEC and its significance in normal endometrial physiology. 2.8 Co-expression genes of NR3C1 in UCEC were analyzed by LinkedOmics LinkedOmics is a versatile multi-omics database that seamlessly integrates global mass spectrometry-based proteomics data derived from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) into specific The Cancer Genome Atlas (TCGA) tumor samples [ 28 ] ( https://www.linkedomics.org/ ). This comprehensive resource incorporates multi-omics data across all 32 TCGA cancer types and 10 CPTAC cancer cohorts. In the study, our analysis focuses on examining the co-expressed genes that share expression patterns with the NR3C1 gene in UCEC through the LinkedOmics database. By exploring the co-expression patterns, we aim to uncover potential functional relationships and molecular pathways associated with NR3C1 in the context of UCEC. 2.9 The relationship between NR3C1 expression and tumor infiltrating immune cells in UCEC Tumor tissue is a complex microenvironment comprising various cell types, including tumor cells, stromal cells, fibroblasts, and immune cells. The collective interaction of these cells forms the tumor microenvironment. Analyzing immune microinfiltration involves assessing the proportion of immune cells within tumor tissue. A quantitative study of infiltrating immune cells can provide insights into the mechanisms of tumor immune responses and aid in evaluating the immunogenicity of tumor therapy [ 29 ] . In this study, the relationship between the expression of NR3C1 and immune cell infiltration in UCEC was investigated. The content of 22 types of human immune cells in UCEC samples was calculated using R software along with the Cibersort [ 30 ] ( https://github.com/topics/ cibersort) and ggplot package ( https://cran.r-project.org/web/ packages/ggplot2/index.html). Subsequently, the correlation between the 22 types of immune cells was visualized using the Heatmap package ( https://cran.r-project.org/web/packages/tidyHeatmap/vignettes/introduction . html). To further explore the impact of the NR3C1 on the 22 types of immune cells, UCEC samples were categorized into high and low expression groups based on the median expression level of NR3C1 . The differences in immune cell content between these two groups were visualized using the vioplot package [ 31 ] ( https://cran.r-project.org/web/packages/vioplot/index . html ). For immune cells exhibiting statistically significant differences (P < 0.05), the correlation between the expression of NR3C1 and these immune cells was analyzed using the ggpubr package ( https://cran.r-project . org/ web/packages/ggpubr/index.html). This comprehensive analysis aims to shed light on the relationship between NR3C1 expression and immune cell infiltration in UCEC. 2.10 Enrichment analysis of gene NR3C1 in UCEC GSEA is a powerful method used to determine whether a set of genes at a functional node is significantly enriched compared to random levels. This analysis can be extended from a simple annotation of a single gene to a comprehensive group analysis of multiple gene collections, providing valuable insights into the functional roles of genes within biological pathways and processes [ 32 ] . In this study, the objective was to explore the role of the NR3C1 in the progression of UCEC. R software, along with the patchwork package ( https://cran.r-project.org/web/packages/patchwor/index.html ) , ggsci package ( https://cran.r-project.org/web/ packages/ggsci/vignettes/ggsci.html), and limma package [ 33 ] ( https://bioconductor.org/packages/release/bioc/html/limma.html ), was employed for gene set enrichment analysis of NR3C1 in UCEC. This approach aimed to elucidate the involvement of NR3C1 in molecular pathways and physiological functions relevant to UCEC progression. 3. Results 3.1 Screening out DEG in UCEC DEGs was performed using four datasets (GSE17025, GSE36389, GSE63678, and GSE115810) from the GEO database. The GEO2R standardized gene microarray tool was utilized for this purpose, with results filtered based on the criterion of P < 0.05. According to volcanic map analysis, a total of 5987 DEGs were identified in UCEC tissue using gene expression profiles from GSE17025 compared to normal endometrial tissue, including 2786 up-regulated genes and 3201 down-regulated genes. The gene expression profile of GSE36389 identified a total of 244 DEGs, including 71 up-regulated genes and 173 down-regulated genes. The gene expression profile of GSE63678 identified a total of 1491 DEGs, including 770 up-regulated genes and 721 down-regulated genes. The gene expression profile of GSE115810 identified a total of 483 DEGs, including 69 up-regulated genes and 414 down-regulated genes (Fig. 1 A-D). To identify common DEGs across the four datasets, Venn diagrams were generated. The overlap analysis revealed 72 co-expressed genes that were consistently identified as DEGs in the screening for UCEC across all datasets (Fig. 1 E). These 72 co-expressed genes warrant further investigation for their potential implications in UCEC pathogenesis. 3.2 GO and KEGG pathway enrichment analysis In this study, we elucidated the biological functions of the DEGs in UCEC through comprehensive KEGG and GO enrichment analyses. KEGG pathway analysis shows that these 72 genes are mainly enriched in Transcriptional misregulation in cancer and Leukocyte transendothelial migration (Fig. 2 A). In this research analysis, the BP of the 72 key genes mentioned above are concentrated in the Negative regulation of transcription from RNA polymerase II promoter and the Positive regulation of transcription from RNA polymerase II promoter (Fig. 2 B). The changes in CC mainly involve the Nucleus, Nucleoplasm, and Chromosomes (Fig. 2 C). The MF of DEGs is mainly related to RNA polymerase II transcription regulatory region sequence-specific binding, Sequence-specific DNA binding, Sequence-specific double-stranded DNA binding, RNA polymerase II core promoter proximal region sequence-specific DNA binding, Protein binding and DNA binding (Fig. 2 D). 3.3 Construction of PPI network and screening of Hub genes Based on the aforementioned study, a total of 72 co-expressed genes were identified through the intersection of four datasets. In this investigation, PPI networks involving these 72 co-expressed genes were constructed using the STRING online database (Fig. 3 A). Subsequently, the top 10 genes within the network module were extracted using Cytoscape software and its CytoHubba plug-in. Notably, these key genes include NR3C1, SFRP4, GATA2, MAF, MUC1, WT1, OGN, FOXL2, UBE2I , and JUN (Fig. 3 B). These ten genes are recognized as core key genes that potentially play a significant role in the development of UCEC. 3.4 Validation of pivot genes by GEPIA In the investigation of hub genes, a survival analysis of the expression of the aforementioned ten genes in UCEC was conducted using GEPIA. The findings revealed that among the ten genes, the expression of NR3C1 exhibited significant differences in UCEC prognosis (P < 0.05) (Fig. 4 A- 4 J). Further expression analysis by GEPIA indicated that NR3C1 was lower expression in UCEC tissues compared to normal tissues, suggesting a potential association between the expression of NR3C1 and the prognosis of UCEC (Fig. 4 K). These results underscore the potential significance of NR3C1 as a hub gene in UCEC, warranting further exploration of its role in UCEC progression and clinical outcomes. 3.5 ROC and DCA curve analysis on NR3C1 In the analysis based on the four UCEC datasets (GSE17025, GSE36389, GSE63678, and GSE115810) from the GEO database, the expression of NR3C1 in UCEC was assessed. Subsequently, ROC curves were plotted to assess the diagnostic value of NR3C1 in UCEC. The results from the ROC curves are summarized as follows: (1) The ROC curve of GSE17025 has an offline area of 0.914 and a sensitivity of 81 30%, specificity 100.00%, Cut-off value 2305.500, indicating that in this dataset, NR3C1 gene expression levels above 2305.500 can be classified as UCEC, while expression levels below 2305.500 can be classified as non UCEC. (2) The offline area of the ROC curve of GSE36389 is 0.791, the sensitivity is 76.90%, the specificity is 85.70%, and the Cut-off value of this dataset is 2.926. (3) The offline area of the ROC curve of GSE63678 is 0.786, the sensitivity is 71.40%, the specificity is 78.60%, and the Cut-off value of this dataset is 8.750. (4) The offline area of the ROC curve of GSE115810 is 0.882, the sensitivity is 90.90%, the specificity is 80.00%, and the Cut-off value of this dataset is 6.636. In conclusion, the AUC values for all four datasets exceeded 0.78 (Fig. 5 A- 5 D), signifying substantial sensitivity and specificity of NR3C1 in UCEC diagnosis. Moreover, the DCA curve illustrated that the net benefit of NR3C1 consistently surpassed that of the reference model across the threshold range (Fig. 5 E). These findings collectively indicate that NR3C1 holds promise as a potential biomarker for the diagnosis of UCEC. The robust diagnostic performance observed across multiple datasets enhances the credibility of NR3C1 as a valuable tool for UCEC detection. 3.6 Cox risk regression analysis in UCEC Utilizing clinical data obtained from the UCSC Xena website related to UCEC, a preprocessing step was implemented to eliminate samples with missing data, resulting in a dataset containing 516 UCEC samples. Subsequently, Cox analysis was conducted on variables including tumor staging, patient age, and the expression of NR3C1 within the dataset. The analysis outcomes affirmed a significant correlation between clinical stage III, clinical stage IV, age, and the expression of NR3C1 with the occurrence and progression of UCEC (P < 0.05) (Fig. 6 A-C) ( Table 1 ). These findings further confirm that clinical stage III, clinical stage IV, age, and NR3C1 are independent factors influencing the prognosis of UCEC. This suggests that these variables, particularly the expression of NR3C1 , could serve as valuable and independent prognostic indicators for UCEC. Table 1 Association with overall survival and clinicopathologic characteristic in TCGA patients using Cox regression analysis. Clinical characteristics HR (95%CI) P-val Age ≤ 60 Ref. >60 2.94 [1.14–2.89] 0.012 * Stage Stage I Ref. Stage II 2.62 [0.82–3.59] 0.154 Stage III 4.55 [1.96–5.17] < 0.001 *** Stage IV 12.39 [4.69–15.39] < 0.001 *** NR3C1 Low Ref. High 2.17 [1.29–3.04] 0.00173 ** Note: * P < 0.05. ** P < 0.01. *** P < 0.001. 3.7 Correlation analysis between NR3C1 expression and tumor-infiltrating immune cells in UCEC Tumor immune microenvironment (TIME) refers to the complex network surrounding tumor cells, including immune cells, inflammatory cells, blood vessels, and extracellular matrix, plays a pivotal role in tumor development and treatment response. According to research, immune cells in TIME can participate in tumor resistance by producing various cytokines and chemicals. The changes in immune infiltrating cells in TIME have become an important factor in predicting the clinical outcomes of tumor patients [ 34 – 35 ] . In the process of analyzing the immune infiltration of gene NR3C1 in UCEC, we obtained the proportion of 22 types of immune infiltration cells in 546 UCEC samples (Fig. 7 A). A correlation heatmap depicting the relationships between these 22 immune cell types in UCEC was generated (Fig. 7 B). Based on the median expression of NR3C1 , the UCEC samples were stratified into high and low expression groups. The analysis revealed differences in the content of 11 types of immune cells, including naïve B cells, dendritic cells, M0, M1, Mast cell resting, Mast cells activated, NK cells activated, resting memory CD4 + T cells, activated memory CD4 + T cells, gamma delta (γδ) T cells, and regulatory T cells (P < 0.05) (Fig. 7 C). A relationship map was constructed between NR3C1 expression and these 11 immune cells, uncovering notable correlations. Specifically, there was a positive correlation between NR3C1 expression and naïve B cells (R = 0.12, P = 0.0047), M1 (R = 0.17, P = 2.9e-05), Mast cells resting (R = 0.22, P = 6.4e-08), resting memory CD4 + T cells (R = 0.18, P = 1.7e-05), activated memory CD4 + T cells (R = 0.082, P = 0.049), gamma delta (γδ) T cells (R = 0.11, P = 0.011). Conversely, M0 (R=-0.3, P = 1.9e-13), Mast cells activated (R=-0.18, P = 1.3e-05), NK cells activated (R=-0.1, P = 0.013), and regulatory T cells (R=-0.14, P = 0.00065) exhibited a negative correlation with NR3C1 expression (Fig. 7 D-N). The findings suggest that as the expression level of NR3C1 increases, the content of immune cells such as naïve B cells and memory CD4 + T cells with immune-killing effects also increases. This observation provides a partial explanation for the lower survival rate observed in samples with high expression of NR3C1 , emphasizing the complex interplay between NR3C1 expression and the immune microenvironment in UCEC. 3.8 Protein expression level of NR3C1 on the HPA database Utilizing data from the HPA database, information regarding the expression level of NR3C1 and the clinical status of patients was obtained. The conclusive results validate that as the expression of NR3C1 increases, the disease status of UCEC continues to worsen (Fig. 8 A-B). This observation underscores the potential prognostic significance of NR3C1 expression in predicting the clinical outcomes of UCEC patients. The correlation between NR3C1 expression and disease status further emphasizes the role of NR3C1 as a potential biomarker for assessing and understanding the progression of UCEC. 3.9 Analysis of co-expression genes of NR3C1 gene in UCEC by LinkedOmics database In the analysis conducted using the LinkedOmics database, a comprehensive set of 50 co-expression genes associated with NR3C1 in UCEC was identified. Among these, the top ten positively correlated genes include ARHGAP2, ADCY9, GVIN1, FLJ40330, ARHGAP20, SHE, RFTN1, FLI1, LOC339290 , and WIPF1 (Fig. 9 A). Simultaneously, 50 genes exhibiting negative correlation in expression were also identified, with CD276, C19ORF10, BSG, TMEM132A, TMED3, STAP2, EFNA4, SPATA2L, THOP1 , and TMEM9 being the top ten negatively correlated genes (Fig. 9 B). These findings contribute to a deeper understanding of the molecular interactions and potential regulatory networks involving NR3C1 in UCEC. 3.10 Gene sets enriched in NR3C1 expression phenotype NR3C1 related signaling pathways were analyzed base on GSEA to identify the signaling pathways with significant differences (FDR Q-val < 0.05, NOM P-value < 0.05) in GO and KEGG enrichment of the highly expression data sets in UCEC (Table 2 ). Table 2 Gene sets enrichment from GSEA of NR3C1 in UCEC. Gene set name Set Size NES NOM p-val FDR Q-val KEGG KEGG_RIBOSOME 151 -2.734 0.017 0.035 KEGG_OXIDATIVE_PHOSPHORYLATION 106 -1.907 0.011 0.027 KEGG_TH1_AND_TH2_CELL_DIFFERENTIATION 90 1.830 0.001 0.005 KEGG_TH17_CELL_DIFFERENTIATION 106 1.817 0.001 0.005 KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 51 1.813 0.001 0.005 GO_BP GO_IMMUNOGLOBULIN_PRODUCTION 199 1.984 0.001 0.002 GO_PRODUCTION_OF_MOLECULAR_MEDIATOR_OF_ IMMUNE_RESPONSE 313 1.885 0.001 0.002 GO_ CELL_MATRIX_ADHESION 235 1.746 0.001 0.002 GO_ANTIGEN_RECEPTOR_MEDIATED_SIGNALING_PATHWAY 194 1.735 0.001 0.002 GO_NEGATIVE_REGULATION_OF_BLOOD_VESSEL_ MORPHOGENESIS 140 1.725 0.001 0.002 GO_CC GO_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE 408 1.818 0.001 0.004 GO_T_CELL_RECEPTOR_COMPLEX 131 1.761 0.001 0.004 GO_BLOOD_MICROPARTICLE 142 1.755 0.001 0.004 GO_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX 302 1.645 0.001 0.004 GO_SARCOLEMMA 133 1.636 0.001 0.004 GO_MF GO_ANTIGEN_BINDING 115 2.101 0.001 0.005 GO_EXTRACELLULAR_MATRIX_STRUCTURAL_CONSTITUENT 164 1.829 0.001 0.005 GO_CYTOKINE_BINDING 128 1.755 0.001 0.005 GO_INTEGRIN_BINDING 144 1.622 0.001 0.005 GO_IMMUNE_RECEPTOR_ACTIVITY 136 1.608 0.001 0.005 Five KEGG items, including Ribosome, Oxidative phosphorylation, Systemic lupus erythematosus, Th1 and Th2 cell differentiation, and Th17 cell differentiation, demonstrated significantly differential enrichment in the NR3C1 high expression phenotype (Fig. 10 A). The results of GO items revealed that the BP associated with the NR3C1 high expression phenotype were predominantly enriched in the Antigen receptor mediated signaling pathway, Cell matrix adhesion, Immunoglobulin production, Negative regulation of blood vessel morphogenesis, and Production of molecular mediators of immune response (Fig. 10 B). In terms of CC, the NR3C1 high expression phenotype exhibited substantial enrichment in Blood microparticles, External side of plasma membrane, Plasma membrane signaling receptor complex, Sarcolemma, and T cell receptor complex (Fig. 10 C). The MF associated with the NR3C1 high expression phenotype were primarily enriched in catalytic activity on Antigen binding, Cytokine binding, Extracellular matrix structural constituents, Immune receptor activity, and Integrin binding (Fig. 10 D). 4. Discussion UCEC is an epithelial malignancy originating in the endometrium. Also referred to as uterine body cancer, it stands as one of the three prevalent malignant tumors affecting the female reproductive tract, commonly manifesting in perimenopausal and postmenopausal women [ 36 ] . Over the past two decades, there has been a noticeable increase in the incidence of UCEC, a trend that has been attributed to the rising average life expectancy and shifts in lifestyle choices. Notably, in Western countries, UCEC has claimed the top spot in the incidence of malignant tumors within the female reproductive system [ 37 ] . While significant strides have been made in the treatment of UCEC in recent years, advanced-stage UCEC remains a formidable challenge with a high mortality rate [ 38 ] . Early detection and timely intervention are paramount in mitigating the mortality associated with UCEC. Identifying potential biomarkers for UCEC plays a crucial role in the early diagnosis and treatment of this condition. As such, ongoing efforts to uncover these biomarkers are integral to improving outcomes and addressing the evolving epidemiological landscape of UCEC. In this study, our focus was on exploring the potential risk associated with the NR3C1 in UCEC. To gain deeper insights into the impact of NR3C1 , we conducted an extensive analysis by mining data from the online GEO database. Our objective was to examine the expression levels of NR3C1 in UCEC. The outcomes of our investigation revealed a notable downregulation of the expression of NR3C1 in UCEC. Furthermore, our findings indicated that the expression of NR3C1 affected the survival rate of UCEC patients. These results collectively suggest a pivotal role for NR3C1 in both the progression and prognosis of UCEC. In this study, we conducted KEGG and GO enrichment analyses on a set of 72 candidate genes associated with UCEC. The results revealed that these 72 genes predominantly contribute to the dysregulation of cancer-related transcription and the transendothelial migration of white blood cells. Notably, our findings suggest that the development of UCEC may be closely linked to the dysregulation of cell transcription. Survival analysis focusing on the top 10 genes derived from PPI networks demonstrated a significant correlation between the total survival time and the NR3C1 , indicating its potential prognostic relevance in UCEC. Indeed, our thorough examination across multiple databases consistently showed a decrease in the expression of NR3C1 in UCEC tissues compared to normal or adjacent tissues. Furthermore, we conducted Cox regression analyses, incorporating clinical information from UCEC patients. The results highlighted correlations between UCEC and NR3C1 , patient age, clinical stage III, and clinical stage IV. This suggests that clinical stage III, clinical stage IV, age, and NR3C1 are independent factors influencing the prognosis of UCEC. ROC and DCA curve analysis further supported and reinforced these findings. Collectively, our comprehensive approach, integrating bioinformatic analyses, survival assessments, and clinical data, strengthens the evidence for the potential significance of NR3C1 in UCEC progression and prognosis. In recent years, cancer immunotherapy has gained prominence as an effective method for treating cancer. Consequently, our study aimed to investigate the potential association between the expression of NR3C1 and immune infiltrating cells in UCEC. Our findings revealed that an elevation in NR3C1 gene expression correlated with increased levels of naïve B cells, M1, Mast cells resting, memory CD4 + T cells, and gamma delta (γδ) T cells. Recent research has highlighted the role of naïve B cells in tumor tissue, demonstrating their ability to produce lymphotoxins, inducing angiogenesis, and can also activate Fc on myeloid cells through the formation of antigen-antibody immune complexes via γ receptor. This activation leads to the transformation of myeloid cells into inhibitory cells, thereby suppressing the anti-tumor response of CD4 + and CD8 + T cells, consequently promoting tumor tissue growth [ 39 ] . Speiser's research further elucidates that memory CD4 + T cells in tumor tissue can contribute to tumor growth by interacting with CD4 + Treg and follicular helper T cells (TFH), such as the consumption of IL-2 and the reduction of antigen presentation through CTLA-4. An imbalance in the proportion of memory CD4 + T cells is shown to facilitate tumor growth [ 40 ] . Additionally, our analysis indicates that an increase in the expression of NR3C1 is associated with a decrease in the proportion of M0, Mast cells activated, NK cell activated, regulatory T cells in immune cells within the immune cell population. M0, the most abundant immune cells in the tumor microenvironment, exhibit different activation properties in the M1 and M2 directions. M1 activation involves the secretion of reactive oxygen species (ROS), nitric oxide (NO), and pro-inflammatory cytokines such as IL-1β, IL-6, IL-12, and IL-23, mediating the killing effect on tumor cells, and M2 recruit other immune cells into the tumor microenvironment and altering their function [ 41 ] . On the other hand, Mast cells activated releases chemokines and cytokines, recruiting CD8 + T cells and CD4 + T cells into the tumor microenvironment, thereby enhancing the anti-tumor effect [ 42 ] . In the tumor microenvironment, NK cells release IFN-γ, TNF-α, GM-CSF, and more, enhancing antigen-specific T cell responses and regulating cross-regulatory networks with DC cells and neutrophils. Additionally, NK cells release perforin and granzyme when encountering tumor cells, penetrating the cell membrane and inducing tumor cell apoptosis [ 43 ] . Based on our bioinformatics analysis, our study demonstrates that an increase in the proportion of naïve B cells and memory CD4 + T cells or a decrease in the proportion of M0, Mast cells activated, NK cell activated in UCEC tissue is associated with a poorer prognosis, consistent with previous results. However, further exploration of the detailed molecular typing of the aforementioned five immune cell subtypes is crucial to better understand their potential impact on the prognosis of UCEC patients. In this study, to elucidate the functional role of NR3C1 in UCEC, we conducted a comprehensive single gene enrichment analysis using GSEA. The KEGG analysis revealed significant differential enrichment in several pathways for the NR3C1 high expression phenotype, including Oxidative phosphorylation, Ribosome metabolism, Th1 and Th2 cell differentiation, and Th17 cell differentiation. The GO project analysis provided further insights into the CC, BP, and MF associated with NR3C1 high expression in UCEC. Specifically, CC with high NR3C1 expression were enriched in Blood microparticles, External side of plasma membrane, Plasma membrane signaling receptor complex, Sarcolemma, and T cell receptor complex. The BP associated with NR3C1 high expression included the Antigen receptor mediated signaling pathway, Cell matrix adhesion, Immunoglobulin production, Negative regulation of blood vessel morphogenesis, and Production of molecular mediators of immune response. MF of NR3C1 high expression were concentrated in Antigen binding, Cytokine binding, Extracellular matrix structural constituents, Immune receptor activity, and Integrin binding. It is noteworthy that the enrichment analysis did not reveal significant results for phenotypes with low expression of NR3C1 .These findings collectively suggest that NR3C1 holds potential as a valuable biomarker and therapeutic target for predicting the prognosis of UCEC patients. The differential enrichment of specific pathways and functions in the context of NR3C1 high expression implies its involvement in crucial biological processes and cellular functions associated with UCEC. Further exploration of NR3C1's role in UCEC could contribute to the development of targeted therapeutic interventions and personalized treatment strategies for affected patients. While our study extensively mined and analyzed information from various online biological databases, it is crucial to acknowledge that we did not conduct corresponding experimental validations. To further strengthen the robustness of our findings, future research endeavors could include experimental techniques such as quantitative polymerase chain reaction (qPCR) or Western Blot to validate the expression of NR3C1 in UCEC. 5. Conclusion Our study, based on data from GEO, KEGG, and other biological databases, has identified NR3C1 as a potentially influential independent prognostic factor in the onset and progression of UCEC. This discovery holds promise as a novel target for enhancing clinical diagnostic strategies for UCEC. Furthermore, our investigation highlights a notable correlation between NR3C1 expression and immune infiltrating cells. This finding not only adds depth to our understanding of the role of NR3C1 in UCEC but also presents a promising avenue for advancing immunotherapeutic approaches in the treatment of UCEC. Future research endeavors should consider incorporating experimental validations to solidify the biological significance of NR3C1 and its potential implications for clinical applications in UCEC. Abbreviations UCEC Uterine corpus endometrial carcinoma DEGs Differentially expressed genes GEO Gene Expression Omnibus GSEA Gene Set Enrichment Analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes CC Cellular Component MF Molecular Function BP Biological Process PPI Protein-Protein Interaction GEPIA Gene Expression Profiling Interactive Analysis ROC Receiver Operating Characteristi AUC Area Under the Curve DCA Decision Curve Analysis HPA Human Protein Atlas TCGA The Cancer Genome Atlas CPTAC Clinical Proteomic Tumor Analysis Consortium Declarations Acknowledgments: In the course of this research, I extend my sincere appreciation to my laboratory colleagues for their invaluable technical support and insightful suggestions during the experimental procedures. I would like to express gratitude to all those who have contributed to this study, with special acknowledgment to my mentor, Professor Lu Yanping. The professional insights provided by you have significantly propelled the advancement of our work. Your guidance and support have been instrumental in ensuring the seamless progression of this research, facilitating continuous learning and improvement on my part. Once again, I wish to convey my heartfelt gratitude. Disclosure: Funding Information This work was supported by grants from the National Key Research and Development Program (No. 2021YFC1005300). The funders played a significant guiding role in the study design, data collection and analysis, decision to publish, and preparation of the manuscript. Data availability Data is provided within supplementary information files. If the editorial department or readers want more detailed data, please feel free to contact Dr. Shen Yahui. His address is [email protected] . Conflict of Interest The authors declare that they have no conflict of interest . Ethics Statement - Approval of the research protocol by an Institutional Reviewer Board: N/A - Informed Consent: N/A - Registry and the Registration No. of the study/trial: N/A - Animal Studies: N/A Author Contributions Yahui Shen conceived and designed the experiments, conducted the experiments, prepared charts and tables, and drafted or reviewed the manuscript. Yanping Lu conceived and designed the experiments, oversaw the entire experimental process, reviewed the manuscript, and approved the final draft. References Abu-Rustum N, Yashar C, Arend R, et al. Uterine Neoplasms, Version 1.2023, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2023;21(2):181–209. doi: 10.6004/jnccn.2023.0006 . Miller KD, Nogueira L, Devasia T, et al. Cancer treatment and survivorship statistics, 2022. J Natl Compr Canc Netw. 2023;21(2):181–209. doi: 10.3322/caac.21731 . Wang PH, Yang ST, Liu CH, Chang WH, Lee FK, Lee WL. Endometrial cancer: Part I. Basic concept. 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J Hematol Oncol. 2021, 14(1):7. doi: 10.1186/s13045-020-01014-w Additional Declarations No competing interests reported. Supplementary Files GSE115810.xlsx GSE17025.xlsx GSE36389.xlsx GSE63678.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4383100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":306105670,"identity":"266dd0c1-df9f-4457-bdfb-3207170578c7","order_by":0,"name":"Yahui Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACCTB5gIe9gfkAiCtDvBaeA2wJIC4P0VoYeA7wGIBYhLXIz24+9vDLnzsyPOw5n1/dqLHgYWA/fHQDPi0Gd46lG8vwPOPh4Xm7zTrnGNBhPGlpN/Bqkcgxk5aQOMxjL5G7zTiHDahFgscMrxb5GfnfpCUMDvPwSOQ8M875R4QWhhs5bJIfEsBamB/nthGhxeBGmpk0wwGgFp5nZsy5fRI8bIT8Ij8j+Znkjz+H7XnYkx9/zvlWJ8fPfvgYfocBATMkLhLYwHHERkg5CDD+gGhh/kCM6lEwCkbBKBh5AAAY3EWESOYXAwAAAABJRU5ErkJggg==","orcid":"","institution":"the First Medical Center of PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yahui","middleName":"","lastName":"Shen","suffix":""},{"id":306105671,"identity":"c362e58a-1536-48ed-8514-d26ae40919a6","order_by":1,"name":"Yanping Lu","email":"","orcid":"","institution":"the First Medical Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-05-07 12:39:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4383100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4383100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57516857,"identity":"5366e39a-2472-4bf0-b659-73474fb2cc3e","added_by":"auto","created_at":"2024-05-31 20:10:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":744644,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes in UCEC based on the GEO database.\u003c/p\u003e\n\u003cp\u003e(A-D): Volcano plot of the expression level of differentially expressed genes in normal and cancer tissues from GSE17025、GSE36389、GSE63678 and GSE115810. Red dots represent a high expression of genes and blue dots represent a low expression of genes.\u003c/p\u003e\n\u003cp\u003e(E): The four datasets showed an overlap of 72 genes using a Venn diagram.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/ad97e9c749c9fe901f80f83d.png"},{"id":57516869,"identity":"9393f062-dbad-415e-80eb-9399794a78ee","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":700758,"visible":true,"origin":"","legend":"\u003cp\u003eSignificantly functional enrichment pathway of 72 DEGs.\u003c/p\u003e\n\u003cp\u003e(A): KEGG pathway enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(B-D): T top 10 terms significantly enriched in the three GO categories: (B) biological process; (C) cellular component and (D) molecular function. P-value \u0026lt; 0.05 was set as the threshold.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/6120052c14fa45b1d704c5c0.png"},{"id":57516871,"identity":"231ed4fb-b334-48fb-a144-92f0463b3927","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1043556,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network and the most significant module of DEGs.\u003c/p\u003e\n\u003cp\u003e(A): The PPI network of DEGs was constructed using STRING.\u003c/p\u003e\n\u003cp\u003e(B): The top most significant module was obtained from CytoHubba plugin.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/70ef5536b6b1b2104619d107.png"},{"id":57516866,"identity":"d65e4b65-8eb1-4d23-ac61-cd9bdfbe65f2","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":792679,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival analysis and expression of hub genes in normal and cancer tissues.\u003c/p\u003e\n\u003cp\u003e(A-J): Survival curves analysis for \u003cem\u003eGATA2, NR3C1, SFRP4, MAF, MUC1, WT1, OGN, FOXL2, UBE2I\u003c/em\u003e and \u003cem\u003eJUN\u003c/em\u003e in in normal endometrial tissue and UCEC tissue.\u003c/p\u003e\n\u003cp\u003e(K): Differential expression of \u003cem\u003eNR3C1\u003c/em\u003e in the normal and cancer tissues (TIMER).\u003c/p\u003e\n\u003cp\u003eP-value Significant Codes: * \u0026lt;0.05. ** \u0026lt;0.01. *** \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/8476364eaab33bb67563c980.png"},{"id":57516862,"identity":"7d0abd88-1c3a-4832-b040-3669cfceca0e","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":436935,"visible":true,"origin":"","legend":"\u003cp\u003eROC and DCA curve analysis.\u003c/p\u003e\n\u003cp\u003e(A–D): ROC curve analysis of\u003cem\u003e NR3C1\u003c/em\u003e on the GEO database (GSE17025、GSE36389、GSE63678 and GSE115810).\u003c/p\u003e\n\u003cp\u003e(E): DCA curve analysis shows the net benefit of \u003cem\u003eNR3C1\u003c/em\u003e in the 5-year survival.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/ad84bdf83a49cb3a757b336b.png"},{"id":57517318,"identity":"6179b16f-a07b-4238-9efb-dbcc5213eaf0","added_by":"auto","created_at":"2024-05-31 20:18:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":203659,"visible":true,"origin":"","legend":"\u003cp\u003e（A-C）Cox analysis of Clinical Stage, Age, \u003cem\u003eNR3C1\u003c/em\u003eexpression.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/dd10fff81fe95f7a38de219d.png"},{"id":57516872,"identity":"bc89031b-0f91-42e9-bb28-0928298728aa","added_by":"auto","created_at":"2024-05-31 20:10:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1216184,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between the \u003cem\u003eNR3C1\u003c/em\u003e expression and tumor-infiltration immune cells.\u003c/p\u003e\n\u003cp\u003e(A): Barplot showed the relative content of 22 immune cells in UCEC samples.\u003c/p\u003e\n\u003cp\u003e(B): Block diagram showed the correlation of 22 immune cells in UCEC (Note: * \u0026lt;0.05. ** \u0026lt;0.01. *** \u0026lt;0.001.).\u003c/p\u003e\n\u003cp\u003e(C): Violin diagram showed the difference of \u003cem\u003eNR3C1\u003c/em\u003e expression in 22 immune cells. High expression groups are indicated in red and low expression groups in blue.\u003c/p\u003e\n\u003cp\u003e(D-N): Scatterplot showed the correlation between \u003cem\u003eNR3C1\u003c/em\u003e and immune cells.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/80e023041caff60fc26bcda9.png"},{"id":57516865,"identity":"c3be7182-30b3-4710-8d89-2706c17b030f","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3074778,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the protein expression level of \u003cem\u003eNR3C1\u003c/em\u003e by the Human Protein Atlas (HPA) database.\u003c/p\u003e\n\u003cp\u003e(A) Analysis of the protein expression level of \u003cem\u003eNR3C1\u003c/em\u003e in Endometrium by the HPA database.\u003c/p\u003e\n\u003cp\u003e(B) Analysis of the protein expression level of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC by the HPA database.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/b09c82c9b6615baf689d8a23.png"},{"id":57516863,"identity":"61c0ccf4-1b38-44ad-94c7-cba26da9f170","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":573763,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the co-expression gene \u003cem\u003eNR3C1\u003c/em\u003e in UCEC\u003c/p\u003e\n\u003cp\u003e(A): Heatmap of positively correlated expression genes\u003c/p\u003e\n\u003cp\u003e(B): Heatmap of negatively correlated expressed genes\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/a29d9b26215639b56ee14f42.png"},{"id":57516870,"identity":"d3788cb4-50df-49c1-9f83-ddfd35dc63b3","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":733266,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment plots from GSEA of NR3C1 in UCEC.\u003c/p\u003e\n\u003cp\u003e(A-D): Differential enrichment of gene in KEGG, GO-BP, GO-CC and GO-MF pathways with high \u003cem\u003eNR3C1\u003c/em\u003eexpression. (KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function).\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/ad67829b31e7bfa3a092d0a8.png"},{"id":65170713,"identity":"baa2a879-d9e7-4bf1-9574-805e5c83e885","added_by":"auto","created_at":"2024-09-24 11:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11638275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/a23db684-f8b8-4516-aecc-27e68e8a0081.pdf"},{"id":57516858,"identity":"7fee3610-5947-4e0e-9b9f-caa799db5609","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35329,"visible":true,"origin":"","legend":"","description":"","filename":"GSE115810.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/df24c1b2abbfdfd157de12dc.xlsx"},{"id":57516856,"identity":"ed0c5d81-9526-47d2-9b51-c1b4852e67f8","added_by":"auto","created_at":"2024-05-31 20:10:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4529072,"visible":true,"origin":"","legend":"","description":"","filename":"GSE17025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/9def2ab219e5b436c895f2f5.xlsx"},{"id":57516867,"identity":"eb37ef0b-829b-4300-aad4-33df36ee1692","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":336022,"visible":true,"origin":"","legend":"","description":"","filename":"GSE36389.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/e1771a4298f68ad501fcd213.xlsx"},{"id":57516864,"identity":"a1bc17ac-735d-45d0-b192-c6cfff9fa254","added_by":"auto","created_at":"2024-05-31 20:10:25","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":105600,"visible":true,"origin":"","legend":"","description":"","filename":"GSE63678.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4383100/v1/ce87d1d4e253d56388f6c1c2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets","fulltext":[{"header":"1．Introduction","content":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC), a prevalent malignancy originating from the inner lining of the uterus, or endometrium, poses a substantial global health challenge. This condition, also known as endometrial carcinoma, displays a diverse array of characteristics that significantly influence its diagnosis, treatment, and overall prognosis. Numerous risk factors contribute to the occurrence and progression of UCEC, including continuous exposure to estrogen, metabolic irregularities such as obesity and diabetes, early onset of menstruation, infertility, delayed onset of menopause, carrying susceptibility genes, and advanced age (greater than 60). Clinically, UCEC is categorized into type I and type II. The former is hormone-dependent, predominantly presenting as endometrioid carcinoma with a more favorable prognosis, while the latter is hormone-independent and typically associated with a poorer prognosis. According to statistics from the National Cancer Center of China in 2019, the incidence rate of UCEC is 10.28 per 100,000, with a mortality rate of 1.9 per 100,000, constituting 3.88% of female malignant tumors, following closely behind cervical cancer. Furthermore, UCEC holds the leading position in developed countries such as Europe and the United States \u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. For instance, in the United States alone, it is estimated that 65,950 new cases emerged in 2022, resulting in 12,550 deaths attributed to this disease \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe gene \u003cem\u003eNR3C1\u003c/em\u003e, located on the reverse chain of human chromosome 5q31, plays a crucial role in encoding glucocorticoid receptors in human cells. This gene exhibits notable complexity with 16 brief variants, generating 3 splice isomers known as GR- α, GR- β, and GR-P \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Extensive research has demonstrated that \u003cem\u003eNR3C1\u003c/em\u003e is predominantly expressed in various cellular compartments, including the cell membrane, cytoplasmic sol, and nucleus. \u003cem\u003eNR3C1\u003c/em\u003e serves multiple physiological functions within human cells, encompassing glucocorticoid receptor activity, identical protein binding activity, and protein kinase binding activity. Furthermore, \u003cem\u003eNR3C1\u003c/em\u003e is implicated in the positive regulation of pri-miRNA transcription by RNA polymerase II. It actively participates in fundamental cellular processes such as glandular synthesis, glucocorticoid signaling pathways, and the regulation of cellular biosynthesis processes \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In the human system, \u003cem\u003eNR3C1\u003c/em\u003e is primarily expressed in vital systems such as the digestive system, central nervous system, urogenital system, and respiratory system. Recent studies have revealed that dysregulation in \u003cem\u003eNR3C1\u003c/em\u003e expression is associated with the occurrence and progression of various diseases, including but not limited to anorexia nervosa, severe depressive disorder, renal cell carcinoma, among others \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Despite these insights, many aspects of the relationship between the gene \u003cem\u003eNR3C1\u003c/em\u003e and UCEC remain unclear, necessitating further exploration and investigation.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a comprehensive analysis by initially identifying differentially expressed genes (DEGs) between normal and UCEC tissues based on the UCEC dataset in the Gene Expression Omnibus (GEO) database. Subsequently, a series of bioinformatics analyses, including survival analysis, Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis, were meticulously performed on the identified DEGs. The findings of our study underscore the pivotal role of the gene \u003cem\u003eNR3C1\u003c/em\u003e in influencing the occurrence and progression of UCEC. Notably, \u003cem\u003eNR3C1\u003c/em\u003e emerges as a significant player not only in the pathogenesis of the disease but also in its diagnostic and prognostic aspects. These insights contribute to a deeper understanding of the molecular landscape of UCEC, emphasizing the potential significance of \u003cem\u003eNR3C1\u003c/em\u003e as a key molecular player in the complex dynamics of the disease. The implications of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC further underscore its importance as a potential biomarker and therapeutic target, warranting continued exploration and validation in future studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eDuring the data collection phase, we initiated the process by searching for the keyword \"uterine corpus endometrial carcinoma\" in the GEO database on PUBMED (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The results obtained from this search were meticulously screened to ensure relevance and reliability. Subsequently, a total of four datasets GSE17025\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSE36389 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e GSE36389), GSE63678\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63678\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63678\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GSE115810\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. cgi? acc = GSE115810) were included in this study. The GSE17025 includes 12 normal endometrial tissues and 91 UCEC tissues. GSE36389 contains 7 normal endometrial tissues and 13 UCEC tissues. GSE63678 comprises 5 normal endometrial tissues and 7 UCEC tissues. GSE115810 is consists of 3 normal endometrial tissues and 24 UCEC tissues. Importantly, it is crucial to note that all four datasets were derived from online sources, and as such, they were not subject to review by an Ethics Committee. This ethical consideration aligns with the nature of online, publicly available data utilized in our study.\u003c/p\u003e \u003cp\u003eFollowing the selection of four datasets, we utilized Pubmed GEO2R to conduct a comprehensive analysis of them \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). DEGs between normal endometrial tissues and UCEC tissues were systematically identified. To refine our findings, genes with multiple probes and those lacking corresponding probe groups were meticulously excluded, employing a significance threshold of P \u0026lt; 0.05. Subsequently, a meticulous integration of the resulting genes from each dataset was performed using an online Venn map (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This strategic approach unveiled a subset of coexpressed DEGs shared among all four datasets, which represents a robust set of molecular signatures consistently implicated in UCEC pathogenesis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis of GO and KEGG Pathways in DEG\u003c/h2\u003e \u003cp\u003eTo delve into the biological insights of DEGs in UCEC, we utilized the DAVID online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e for comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses \u003csup\u003e[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. GO analysis systematically annotated gene properties across Cellular Component (CC), Molecular Function (MF), and Biological Process (BP), offering a nuanced understanding of gene roles within cellular contexts. Meanwhile, KEGG pathway analysis provided a holistic view of gene involvement in diverse metabolic pathways, enriching our comprehension of intricate cellular functions \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This integrated and amplified analytical approach not only uncovered crucial biological mechanisms but also holds the potential to unveil promising therapeutic targets and diagnostic biomarkers for UCEC. The depth and breadth of this exploration significantly contribute to advancing our understanding of the molecular landscape underlying UCEC, paving the way for more targeted and effective interventions in the clinical landscape.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.3\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of PPI Network and Screening of Key Genes\u003c/h2\u003e \u003cp\u003eProtein-Protein Interaction (PPI) Networks leverage the STRING online database to to analyze interactions among proteins, aiming to comprehend the involvement of related proteins in biological signal transmission, gene expression regulation, energy metabolism, material metabolism, cell cycle, and other life processes \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e (\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\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The PPI network serves as a valuable tool for conducting in-depth biological information analysis and identifying key core genes pivotal to the onset and progression of diseases. Following the establishment of the PPI network for DEGs through the STRING online database, preprocessing data for DEGs was acquired from the STRING website to facilitate subsequent enrichment analysis. This data was then employed to visualize the PPI network using Cytoscape 3.10.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Subsequent module analysis was carried out using the Mcode and Centiscape plug-ins within Cytoscape \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The Cytohubba plug-in in Cytoscape was utilized to screen and identify the top ten key genes among DEGs for further detailed analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Validation of hub genes by the GEPIA\u003c/h2\u003e \u003cp\u003eAfter screening with Cytoscape software, we identified ten key genes, which underwent further verification through the Gene Expression Profiling Interactive Analysis (GEPIA) online analysis website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e GEPIA serves as an online platform for biological information analysis, compiling the expression values of each searchable gene across various tumor samples. It can calculate the expression levels of genes in specific tumors \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. GEPIA offers a wide range of analyses, including tumor/normal differential expression profile analysis, expression distribution, pathological stage analysis, survival analysis, identification of similar genes, gene expression correlation analysis, and dimension reduction analysis. In this study, we utilized GEPIA specifically for survival analysis of the ten key genes. The analysis results were then screened based on a significance threshold of P \u0026lt; 0.05 to identify hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cox proportional risk regression analysis\u003c/h2\u003e \u003cp\u003eIn order to further explore the impact of clinical factors and hub genes on the onset and progression of UCEC, Cox proportional hazards regression analysis was employed in this study \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The first step involved downloading UCEC-related gene expression information and clinical data from the UCSC Xena website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Subsequently, the data underwent preprocessing to extract pertinent variables such as age, tumor stage, survival time, follow-up outcome, and key gene expression levels. Following data preparation, Cox regression analyses were carried out for UCEC using R software. These analyses aimed to assess the influence of variables such as age and tumor stage on the outcome of cases. The analysis outcomes were then screened based on a significance threshold of P-value \u0026lt; 0.05. Ultimately, the variable factors influencing the prognosis of endometrial carcinoma were determined through this comprehensive analytical approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 ROC and DCA curve analysis\u003c/h2\u003e \u003cp\u003eThe Receiver Operating Characteristic curve (ROC curve) is a graphical tool used for assessing the overall accuracy of a classifier, particularly in binary classification problems. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various decision thresholds \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The area under ROC curve (AUC) generally indicates better classifier performance. In this study, we employed the pROC package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epackages/pROC/index.html\u003c/span\u003e) in the R software to evaluate the sensitivity and specificity of \u003cem\u003eNR3C1\u003c/em\u003e in diagnosing UCEC. Decision Curve Analysis (DCA) is a method used to evaluate the clinical utility of predictive models, diagnostic tests, or molecular markers \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Unlike the ROC curve, DCA considers the preferences of patients or decision-makers. By integrating these preferences into the analysis, DCA aims to provide a more practical evaluation of the diagnostic value of a marker. This concept has gained popularity in clinical analysis as it aligns more closely with real-world decision-making scenarios \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In this study, we used clinical data from the UCSC Xena website and the ggDCA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/cran/ggDCA\u003c/span\u003e\u003cspan address=\"https://github.com/cran/ggDCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and survival package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/survival/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/survival/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e in R software to draw DCA curves. These curves were utilized to assess the diagnostic value of \u003cem\u003eNR3C1\u003c/em\u003e for UCEC in a manner that considers the practical implications of clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.7 Analysis of\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003eprotein expression level by the HPA database\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Human Protein Atlas (HPA) database is a comprehensive resource that integrates data from proteomics, transcriptomics, and systems biology to provide detailed information on the tissue and cell distribution of around 26,000 human proteins. Notably, it covers both tumor tissues and normal tissues, allowing for a holistic understanding of protein expression patterns. The database facilitates the creation of intricate maps depicting the distribution of proteins across various biological contexts, including tissues, cells, and organs \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e In the context of this study, our focus was on comparing the protein expression level of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC and normal endometrial tissues. By leveraging the wealth of information available in the HPA database, we aimed to gain insights into how the expression of \u003cem\u003eNR3C1\u003c/em\u003e varies between UCEC and normal endometrial tissues. This comparative analysis can contribute valuable data to our understanding of the potential role of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC and its significance in normal endometrial physiology.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.8 Co-expression genes of\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003ein UCEC were analyzed by LinkedOmics\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLinkedOmics is a versatile multi-omics database that seamlessly integrates global mass spectrometry-based proteomics data derived from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) into specific The Cancer Genome Atlas (TCGA) tumor samples \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.linkedomics.org/\u003c/span\u003e\u003cspan address=\"https://www.linkedomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This comprehensive resource incorporates multi-omics data across all 32 TCGA cancer types and 10 CPTAC cancer cohorts. In the study, our analysis focuses on examining the co-expressed genes that share expression patterns with the \u003cem\u003eNR3C1\u003c/em\u003e gene in UCEC through the LinkedOmics database. By exploring the co-expression patterns, we aim to uncover potential functional relationships and molecular pathways associated with \u003cem\u003eNR3C1\u003c/em\u003e in the context of UCEC.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.9 The relationship between\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003eexpression and tumor infiltrating immune cells in UCEC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTumor tissue is a complex microenvironment comprising various cell types, including tumor cells, stromal cells, fibroblasts, and immune cells. The collective interaction of these cells forms the tumor microenvironment. Analyzing immune microinfiltration involves assessing the proportion of immune cells within tumor tissue. A quantitative study of infiltrating immune cells can provide insights into the mechanisms of tumor immune responses and aid in evaluating the immunogenicity of tumor therapy \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In this study, the relationship between the expression of \u003cem\u003eNR3C1\u003c/em\u003e and immune cell infiltration in UCEC was investigated. The content of 22 types of human immune cells in UCEC samples was calculated using R software along with the Cibersort \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/topics/\u003c/span\u003e\u003cspan address=\"https://github.com/topics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e cibersort) and ggplot package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e packages/ggplot2/index.html). Subsequently, the correlation between the 22 types of immune cells was visualized using the Heatmap package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/tidyHeatmap/vignettes/introduction\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/tidyHeatmap/vignettes/introduction\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. html). To further explore the impact of the \u003cem\u003eNR3C1\u003c/em\u003e on the 22 types of immune cells, UCEC samples were categorized into high and low expression groups based on the median expression level of \u003cem\u003eNR3C1\u003c/em\u003e. The differences in immune cell content between these two groups were visualized using the vioplot package \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/vioplot/index\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/vioplot/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehtml\u003c/span\u003e). For immune cells exhibiting statistically significant differences (P \u0026lt; 0.05), the correlation between the expression of \u003cem\u003eNR3C1\u003c/em\u003e and these immune cells was analyzed using the ggpubr package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project\u003c/span\u003e\u003cspan address=\"https://cran.r-project\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. org/ web/packages/ggpubr/index.html). This comprehensive analysis aims to shed light on the relationship between \u003cem\u003eNR3C1\u003c/em\u003e expression and immune cell infiltration in UCEC.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.10 Enrichment analysis of gene\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003ein UCEC\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGSEA is a powerful method used to determine whether a set of genes at a functional node is significantly enriched compared to random levels. This analysis can be extended from a simple annotation of a single gene to a comprehensive group analysis of multiple gene collections, providing valuable insights into the functional roles of genes within biological pathways and processes \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In this study, the objective was to explore the role of the \u003cem\u003eNR3C1\u003c/em\u003e in the progression of UCEC. R software, along with the patchwork package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/patchwor/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/patchwor/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, ggsci package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e packages/ggsci/vignettes/ggsci.html), and limma package\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/limma.html\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/release/bioc/html/limma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was employed for gene set enrichment analysis of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. This approach aimed to elucidate the involvement of \u003cem\u003eNR3C1\u003c/em\u003e in molecular pathways and physiological functions relevant to UCEC progression.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Screening out DEG in UCEC\u003c/h2\u003e\u003cp\u003eDEGs was performed using four datasets (GSE17025, GSE36389, GSE63678, and GSE115810) from the GEO database. The GEO2R standardized gene microarray tool was utilized for this purpose, with results filtered based on the criterion of P \u0026lt; 0.05. According to volcanic map analysis, a total of 5987 DEGs were identified in UCEC tissue using gene expression profiles from GSE17025 compared to normal endometrial tissue, including 2786 up-regulated genes and 3201 down-regulated genes. The gene expression profile of GSE36389 identified a total of 244 DEGs, including 71 up-regulated genes and 173 down-regulated genes. The gene expression profile of GSE63678 identified a total of 1491 DEGs, including 770 up-regulated genes and 721 down-regulated genes. The gene expression profile of GSE115810 identified a total of 483 DEGs, including 69 up-regulated genes and 414 down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D). To identify common DEGs across the four datasets, Venn diagrams were generated. The overlap analysis revealed 72 co-expressed genes that were consistently identified as DEGs in the screening for UCEC across all datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These 72 co-expressed genes warrant further investigation for their potential implications in UCEC pathogenesis.\u003c/p\u003e\u003ch2\u003e3.2 GO and KEGG pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eIn this study, we elucidated the biological functions of the DEGs in UCEC through comprehensive KEGG and GO enrichment analyses. KEGG pathway analysis shows that these 72 genes are mainly enriched in Transcriptional misregulation in cancer and Leukocyte transendothelial migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In this research analysis, the BP of the 72 key genes mentioned above are concentrated in the Negative regulation of transcription from RNA polymerase II promoter and the Positive regulation of transcription from RNA polymerase II promoter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The changes in CC mainly involve the Nucleus, Nucleoplasm, and Chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The MF of DEGs is mainly related to RNA polymerase II transcription regulatory region sequence-specific binding, Sequence-specific DNA binding, Sequence-specific double-stranded DNA binding, RNA polymerase II core promoter proximal region sequence-specific DNA binding, Protein binding and DNA binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003ch2\u003e3.3 Construction of PPI network and screening of Hub genes\u003c/h2\u003e\u003cp\u003eBased on the aforementioned study, a total of 72 co-expressed genes were identified through the intersection of four datasets. In this investigation, PPI networks involving these 72 co-expressed genes were constructed using the STRING online database (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, the top 10 genes within the network module were extracted using Cytoscape software and its CytoHubba plug-in. Notably, these key genes include \u003cem\u003eNR3C1, SFRP4, GATA2, MAF, MUC1, WT1, OGN, FOXL2, UBE2I\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These ten genes are recognized as core key genes that potentially play a significant role in the development of UCEC.\u003c/p\u003e\u003ch2\u003e3.4 Validation of pivot genes by GEPIA\u003c/h2\u003e\u003cp\u003eIn the investigation of hub genes, a survival analysis of the expression of the aforementioned ten genes in UCEC was conducted using GEPIA. The findings revealed that among the ten genes, the expression of \u003cem\u003eNR3C1\u003c/em\u003e exhibited significant differences in UCEC prognosis (P \u0026lt; 0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Further expression analysis by GEPIA indicated that \u003cem\u003eNR3C1\u003c/em\u003e was lower expression in UCEC tissues compared to normal tissues, suggesting a potential association between the expression of \u003cem\u003eNR3C1\u003c/em\u003e and the prognosis of UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). These results underscore the potential significance of \u003cem\u003eNR3C1\u003c/em\u003e as a hub gene in UCEC, warranting further exploration of its role in UCEC progression and clinical outcomes.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.5 ROC and DCA curve analysis on\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the analysis based on the four UCEC datasets (GSE17025, GSE36389, GSE63678, and GSE115810) from the GEO database, the expression of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC was assessed. Subsequently, ROC curves were plotted to assess the diagnostic value of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. The results from the ROC curves are summarized as follows:\u003c/p\u003e\u003cp\u003e(1) The ROC curve of GSE17025 has an offline area of 0.914 and a sensitivity of 81 30%, specificity 100.00%, Cut-off value 2305.500, indicating that in this dataset, \u003cem\u003eNR3C1\u003c/em\u003e gene\u003c/p\u003e\u003cp\u003eexpression levels above 2305.500 can be classified as UCEC, while expression levels below 2305.500 can be classified as non UCEC.\u003c/p\u003e\u003cp\u003e(2) The offline area of the ROC curve of GSE36389 is 0.791, the sensitivity is 76.90%, the specificity is 85.70%, and the Cut-off value of this dataset is 2.926.\u003c/p\u003e\u003cp\u003e(3) The offline area of the ROC curve of GSE63678 is 0.786, the sensitivity is 71.40%, the specificity is 78.60%, and the Cut-off value of this dataset is 8.750.\u003c/p\u003e\u003cp\u003e(4) The offline area of the ROC curve of GSE115810 is 0.882, the sensitivity is 90.90%, the specificity is 80.00%, and the Cut-off value of this dataset is 6.636.\u003c/p\u003e\u003cp\u003eIn conclusion, the AUC values for all four datasets exceeded 0.78 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), signifying substantial sensitivity and specificity of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC diagnosis. Moreover, the DCA curve illustrated that the net benefit of \u003cem\u003eNR3C1\u003c/em\u003e consistently surpassed that of the reference model across the threshold range (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). These findings collectively indicate that \u003cem\u003eNR3C1\u003c/em\u003e holds promise as a potential biomarker for the diagnosis of UCEC. The robust diagnostic performance observed across multiple datasets enhances the credibility of \u003cem\u003eNR3C1\u003c/em\u003e as a valuable tool for UCEC detection.\u003c/p\u003e\u003ch2\u003e3.6 Cox risk regression analysis in UCEC\u003c/h2\u003e\u003cp\u003eUtilizing clinical data obtained from the UCSC Xena website related to UCEC, a preprocessing step was implemented to eliminate samples with missing data, resulting in a dataset containing 516 UCEC samples. Subsequently, Cox analysis was conducted on variables including tumor staging, patient age, and the expression of \u003cem\u003eNR3C1\u003c/em\u003e within the dataset. The analysis outcomes affirmed a significant correlation between clinical stage III, clinical stage IV, age, and the expression of \u003cem\u003eNR3C1\u003c/em\u003e with the occurrence and progression of UCEC (P \u0026lt; 0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C) ( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings further confirm that clinical stage III, clinical stage IV, age, and \u003cem\u003eNR3C1\u003c/em\u003e are independent factors influencing the prognosis of UCEC. This suggests that these variables, particularly the expression of \u003cem\u003eNR3C1\u003c/em\u003e, could serve as valuable and independent prognostic indicators for UCEC.\u003c/p\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eAssociation with overall survival and clinicopathologic characteristic in TCGA patients using Cox regression analysis.\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\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-val\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94 [1.14–2.89]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.62 [0.82–3.59]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55 [1.96–5.17]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.39 [4.69–15.39]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNR3C1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17 [1.29–3.04]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00173\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote: *\u003c/b\u003e \u003cb\u003eP\u003c/b\u003e \u003cb\u003e\u0026lt; 0.05. **\u003c/b\u003e \u003cb\u003eP\u003c/b\u003e \u003cb\u003e\u0026lt; 0.01. ***\u003c/b\u003e \u003cb\u003eP\u003c/b\u003e \u003cb\u003e\u0026lt; 0.001.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003e3.7 Correlation analysis between\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003eexpression and tumor-infiltrating immune cells in UCEC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTumor immune microenvironment (TIME) refers to the complex network surrounding tumor cells, including immune cells, inflammatory cells, blood vessels, and extracellular matrix, plays a pivotal role in tumor development and treatment response. According to research, immune cells in TIME can participate in tumor resistance by producing various cytokines and chemicals. The changes in immune infiltrating cells in TIME have become an important factor in predicting the clinical outcomes of tumor patients \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In the process of analyzing the immune infiltration of gene \u003cem\u003eNR3C1\u003c/em\u003e in UCEC, we obtained the proportion of 22 types of immune infiltration cells in 546 UCEC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). A correlation heatmap depicting the relationships between these 22 immune cell types in UCEC was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Based on the median expression of \u003cem\u003eNR3C1\u003c/em\u003e, the UCEC samples were stratified into high and low expression groups. The analysis revealed differences in the content of 11 types of immune cells, including naïve B cells, dendritic cells, M0, M1, Mast cell resting, Mast cells activated, NK cells activated, resting memory CD4 + T cells, activated memory CD4 + T cells, gamma delta (γδ) T cells, and regulatory T cells (P \u0026lt; 0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). A relationship map was constructed between \u003cem\u003eNR3C1\u003c/em\u003e expression and these 11 immune cells, uncovering notable correlations. Specifically, there was a positive correlation between \u003cem\u003eNR3C1\u003c/em\u003e expression and naïve B cells (R = 0.12, \u003cem\u003eP\u003c/em\u003e = 0.0047), M1 (R = 0.17, \u003cem\u003eP\u003c/em\u003e = 2.9e-05), Mast cells resting (R = 0.22, \u003cem\u003eP\u003c/em\u003e = 6.4e-08), resting memory CD4 + T cells (R = 0.18, \u003cem\u003eP\u003c/em\u003e = 1.7e-05), activated memory CD4 + T cells (R = 0.082, \u003cem\u003eP\u003c/em\u003e = 0.049), gamma delta (γδ) T cells (R = 0.11, \u003cem\u003eP\u003c/em\u003e = 0.011). Conversely, M0 (R=-0.3, \u003cem\u003eP\u003c/em\u003e = 1.9e-13), Mast cells activated (R=-0.18, \u003cem\u003eP\u003c/em\u003e = 1.3e-05), NK cells activated (R=-0.1, \u003cem\u003eP\u003c/em\u003e = 0.013), and regulatory T cells (R=-0.14, \u003cem\u003eP\u003c/em\u003e = 0.00065) exhibited a negative correlation with \u003cem\u003eNR3C1\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-N). The findings suggest that as the expression level of \u003cem\u003eNR3C1\u003c/em\u003e increases, the content of immune cells such as naïve B cells and memory CD4 + T cells with immune-killing effects also increases. This observation provides a partial explanation for the lower survival rate observed in samples with high expression of \u003cem\u003eNR3C1\u003c/em\u003e, emphasizing the complex interplay between \u003cem\u003eNR3C1\u003c/em\u003e expression and the immune microenvironment in UCEC.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.8 Protein expression level of\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003eon the HPA database\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUtilizing data from the HPA database, information regarding the expression level of \u003cem\u003eNR3C1\u003c/em\u003e and the clinical status of patients was obtained. The conclusive results validate that as the expression of \u003cem\u003eNR3C1\u003c/em\u003e increases, the disease status of UCEC continues to worsen (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). This observation underscores the potential prognostic significance of \u003cem\u003eNR3C1\u003c/em\u003e expression in predicting the clinical outcomes of UCEC patients. The correlation between \u003cem\u003eNR3C1\u003c/em\u003e expression and disease status further emphasizes the role of \u003cem\u003eNR3C1\u003c/em\u003e as a potential biomarker for assessing and understanding the progression of UCEC.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.9 Analysis of co-expression genes of\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003egene in UCEC by LinkedOmics database\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the analysis conducted using the LinkedOmics database, a comprehensive set of 50 co-expression genes associated with \u003cem\u003eNR3C1\u003c/em\u003e in UCEC was identified. Among these, the top ten positively correlated genes include \u003cem\u003eARHGAP2, ADCY9, GVIN1, FLJ40330, ARHGAP20, SHE, RFTN1, FLI1, LOC339290\u003c/em\u003e, and \u003cem\u003eWIPF1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Simultaneously, 50 genes exhibiting negative correlation in expression were also identified, with \u003cem\u003eCD276, C19ORF10, BSG, TMEM132A, TMED3, STAP2, EFNA4, SPATA2L, THOP1\u003c/em\u003e, and \u003cem\u003eTMEM9\u003c/em\u003e being the top ten negatively correlated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). These findings contribute to a deeper understanding of the molecular interactions and potential regulatory networks involving \u003cem\u003eNR3C1\u003c/em\u003e in UCEC.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.10 Gene sets enriched in\u003c/b\u003e \u003cb\u003eNR3C1\u003c/b\u003e \u003cb\u003eexpression phenotype\u003c/b\u003e\u003c/p\u003e\u003cp\u003e \u003cem\u003eNR3C1\u003c/em\u003e related signaling pathways were analyzed base on GSEA to identify the signaling pathways with significant differences (FDR Q-val \u0026lt; 0.05, NOM P-value \u0026lt; 0.05) in GO and KEGG enrichment of the highly expression data sets in UCEC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eGene sets enrichment from GSEA of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene set name\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSet\u003c/p\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNES\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOM p-val\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFDR Q-val\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_RIBOSOME\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.734\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_OXIDATIVE_PHOSPHORYLATION\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.907\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_TH1_AND_TH2_CELL_DIFFERENTIATION\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.830\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_TH17_CELL_DIFFERENTIATION\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.817\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGO_BP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_IMMUNOGLOBULIN_PRODUCTION\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_PRODUCTION_OF_MOLECULAR_MEDIATOR_OF_\u003c/p\u003e \u003cp\u003eIMMUNE_RESPONSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.885\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_ CELL_MATRIX_ADHESION\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_ANTIGEN_RECEPTOR_MEDIATED_SIGNALING_PATHWAY\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_NEGATIVE_REGULATION_OF_BLOOD_VESSEL_\u003c/p\u003e \u003cp\u003eMORPHOGENESIS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.725\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGO_CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.818\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_T_CELL_RECEPTOR_COMPLEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.761\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_BLOOD_MICROPARTICLE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_SARCOLEMMA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.636\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGO_MF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_ANTIGEN_BINDING\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.101\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_EXTRACELLULAR_MATRIX_STRUCTURAL_CONSTITUENT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.829\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_CYTOKINE_BINDING\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_INTEGRIN_BINDING\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.622\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO_IMMUNE_RECEPTOR_ACTIVITY\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eFive KEGG items, including Ribosome, Oxidative phosphorylation, Systemic lupus erythematosus, Th1 and Th2 cell differentiation, and Th17 cell differentiation, demonstrated significantly differential enrichment in the \u003cem\u003eNR3C1\u003c/em\u003e high expression phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). The results of GO items revealed that the BP associated with the \u003cem\u003eNR3C1\u003c/em\u003e high expression phenotype were predominantly enriched in the Antigen receptor mediated signaling pathway, Cell matrix adhesion, Immunoglobulin production, Negative regulation of blood vessel morphogenesis, and Production of molecular mediators of immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). In terms of CC, the \u003cem\u003eNR3C1\u003c/em\u003e high expression phenotype exhibited substantial enrichment in Blood microparticles, External side of plasma membrane, Plasma membrane signaling receptor complex, Sarcolemma, and T cell receptor complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). The MF associated with the \u003cem\u003eNR3C1\u003c/em\u003e high expression phenotype were primarily enriched in catalytic activity on Antigen binding, Cytokine binding, Extracellular matrix structural constituents, Immune receptor activity, and Integrin binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUCEC is an epithelial malignancy originating in the endometrium. Also referred to as uterine body cancer, it stands as one of the three prevalent malignant tumors affecting the female reproductive tract, commonly manifesting in perimenopausal and postmenopausal women\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Over the past two decades, there has been a noticeable increase in the incidence of UCEC, a trend that has been attributed to the rising average life expectancy and shifts in lifestyle choices. Notably, in Western countries, UCEC has claimed the top spot in the incidence of malignant tumors within the female reproductive system\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. While significant strides have been made in the treatment of UCEC in recent years, advanced-stage UCEC remains a formidable challenge with a high mortality rate\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Early detection and timely intervention are paramount in mitigating the mortality associated with UCEC. Identifying potential biomarkers for UCEC plays a crucial role in the early diagnosis and treatment of this condition. As such, ongoing efforts to uncover these biomarkers are integral to improving outcomes and addressing the evolving epidemiological landscape of UCEC.\u003c/p\u003e\u003cp\u003eIn this study, our focus was on exploring the potential risk associated with the \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. To gain deeper insights into the impact of \u003cem\u003eNR3C1\u003c/em\u003e, we conducted an extensive analysis by mining data from the online GEO database. Our objective was to examine the expression levels of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. The outcomes of our investigation revealed a notable downregulation of the expression of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. Furthermore, our findings indicated that the expression of \u003cem\u003eNR3C1\u003c/em\u003e affected the survival rate of UCEC patients. These results collectively suggest a pivotal role for \u003cem\u003eNR3C1\u003c/em\u003e in both the progression and prognosis of UCEC.\u003c/p\u003e\u003cp\u003eIn this study, we conducted KEGG and GO enrichment analyses on a set of 72 candidate genes associated with UCEC. The results revealed that these 72 genes predominantly contribute to the dysregulation of cancer-related transcription and the transendothelial migration of white blood cells. Notably, our findings suggest that the development of UCEC may be closely linked to the dysregulation of cell transcription. Survival analysis focusing on the top 10 genes derived from PPI networks demonstrated a significant correlation between the total survival time and the \u003cem\u003eNR3C1\u003c/em\u003e, indicating its potential prognostic relevance in UCEC. Indeed, our thorough examination across multiple databases consistently showed a decrease in the expression of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC tissues compared to normal or adjacent tissues. Furthermore, we conducted Cox regression analyses, incorporating clinical information from UCEC patients. The results highlighted correlations between UCEC and \u003cem\u003eNR3C1\u003c/em\u003e, patient age, clinical stage III, and clinical stage IV. This suggests that clinical stage III, clinical stage IV, age, and \u003cem\u003eNR3C1\u003c/em\u003e are independent factors influencing the prognosis of UCEC. ROC and DCA curve analysis further supported and reinforced these findings. Collectively, our comprehensive approach, integrating bioinformatic analyses, survival assessments, and clinical data, strengthens the evidence for the potential significance of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC progression and prognosis.\u003c/p\u003e\u003cp\u003eIn recent years, cancer immunotherapy has gained prominence as an effective method for treating cancer. Consequently, our study aimed to investigate the potential association between the expression of \u003cem\u003eNR3C1\u003c/em\u003e and immune infiltrating cells in UCEC. Our findings revealed that an elevation in \u003cem\u003eNR3C1\u003c/em\u003e gene expression correlated with increased levels of naïve B cells, M1, Mast cells resting, memory CD4 + T cells, and gamma delta (γδ) T cells. Recent research has highlighted the role of naïve B cells in tumor tissue, demonstrating their ability to produce lymphotoxins, inducing angiogenesis, and can also activate Fc on myeloid cells through the formation of antigen-antibody immune complexes via γ receptor. This activation leads to the transformation of myeloid cells into inhibitory cells, thereby suppressing the anti-tumor response of CD4 + and CD8 + T cells, consequently promoting tumor tissue growth \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Speiser's research further elucidates that memory CD4 + T cells in tumor tissue can contribute to tumor growth by interacting with CD4 + Treg and follicular helper T cells (TFH), such as the consumption of IL-2 and the reduction of antigen presentation through CTLA-4. An imbalance in the proportion of memory CD4 + T cells is shown to facilitate tumor growth \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Additionally, our analysis indicates that an increase in the expression of \u003cem\u003eNR3C1\u003c/em\u003e is associated with a decrease in the proportion of M0, Mast cells activated, NK cell activated, regulatory T cells in immune cells within the immune cell population. M0, the most abundant immune cells in the tumor microenvironment, exhibit different activation properties in the M1 and M2 directions. M1 activation involves the secretion of reactive oxygen species (ROS), nitric oxide (NO), and pro-inflammatory cytokines such as IL-1β, IL-6, IL-12, and IL-23, mediating the killing effect on tumor cells, and M2 recruit other immune cells into the tumor microenvironment and altering their function \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. On the other hand, Mast cells activated releases chemokines and cytokines, recruiting CD8 + T cells and CD4 + T cells into the tumor microenvironment, thereby enhancing the anti-tumor effect \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. In the tumor microenvironment, NK cells release IFN-γ, TNF-α, GM-CSF, and more, enhancing antigen-specific T cell responses and regulating cross-regulatory networks with DC cells and neutrophils. Additionally, NK cells release perforin and granzyme when encountering tumor cells, penetrating the cell membrane and inducing tumor cell apoptosis \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Based on our bioinformatics analysis, our study demonstrates that an increase in the proportion of naïve B cells and memory CD4 + T cells or a decrease in the proportion of M0, Mast cells activated, NK cell activated in UCEC tissue is associated with a poorer prognosis, consistent with previous results. However, further exploration of the detailed molecular typing of the aforementioned five immune cell subtypes is crucial to better understand their potential impact on the prognosis of UCEC patients.\u003c/p\u003e\u003cp\u003eIn this study, to elucidate the functional role of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC, we conducted a comprehensive single gene enrichment analysis using GSEA. The KEGG analysis revealed significant differential enrichment in several pathways for the \u003cem\u003eNR3C1\u003c/em\u003e high expression phenotype, including Oxidative phosphorylation, Ribosome metabolism, Th1 and Th2 cell differentiation, and Th17 cell differentiation. The GO project analysis provided further insights into the CC, BP, and MF associated with \u003cem\u003eNR3C1\u003c/em\u003e high expression in UCEC. Specifically, CC with high \u003cem\u003eNR3C1\u003c/em\u003e expression were enriched in Blood microparticles, External side of plasma membrane, Plasma membrane signaling receptor complex, Sarcolemma, and T cell receptor complex. The BP associated with \u003cem\u003eNR3C1\u003c/em\u003e high expression included the Antigen receptor mediated signaling pathway, Cell matrix adhesion, Immunoglobulin production, Negative regulation of blood vessel morphogenesis, and Production of molecular mediators of immune response. MF of \u003cem\u003eNR3C1\u003c/em\u003e high expression were concentrated in Antigen binding, Cytokine binding, Extracellular matrix structural constituents, Immune receptor activity, and Integrin binding. It is noteworthy that the enrichment analysis did not reveal significant results for phenotypes with low expression of \u003cem\u003eNR3C1\u003c/em\u003e.These findings collectively suggest that \u003cem\u003eNR3C1\u003c/em\u003e holds potential as a valuable biomarker and therapeutic target for predicting the prognosis of UCEC patients. The differential enrichment of specific pathways and functions in the context of \u003cem\u003eNR3C1\u003c/em\u003e high expression implies its involvement in crucial biological processes and cellular functions associated with UCEC. Further exploration of \u003cem\u003eNR3C1's\u003c/em\u003e role in UCEC could contribute to the development of targeted therapeutic interventions and personalized treatment strategies for affected patients.\u003c/p\u003e\u003cp\u003eWhile our study extensively mined and analyzed information from various online biological databases, it is crucial to acknowledge that we did not conduct corresponding experimental validations. To further strengthen the robustness of our findings, future research endeavors could include experimental techniques such as quantitative polymerase chain reaction (qPCR) or Western Blot to validate the expression of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study, based on data from GEO, KEGG, and other biological databases, has identified \u003cem\u003eNR3C1\u003c/em\u003e as a potentially influential independent prognostic factor in the onset and progression of UCEC. This discovery holds promise as a novel target for enhancing clinical diagnostic strategies for UCEC. Furthermore, our investigation highlights a notable correlation between \u003cem\u003eNR3C1\u003c/em\u003e expression and immune infiltrating cells. This finding not only adds depth to our understanding of the role of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC but also presents a promising avenue for advancing immunotherapeutic approaches in the treatment of UCEC. Future research endeavors should consider incorporating experimental validations to solidify the biological significance of \u003cem\u003eNR3C1\u003c/em\u003e and its potential implications for clinical applications in UCEC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eUCEC\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Uterine corpus endometrial carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs \u0026nbsp;\u0026nbsp;\u003c/strong\u003eDifferentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Cellular Component\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMF\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Molecular Function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Biological Process\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI \u0026nbsp;\u0026nbsp;\u003c/strong\u003eProtein-Protein Interaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEPIA \u0026nbsp;\u003c/strong\u003e Gene Expression Profiling Interactive Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC \u0026nbsp;\u0026nbsp;\u003c/strong\u003eReceiver Operating Characteristi\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC \u0026nbsp;\u003c/strong\u003e Area Under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDCA\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Decision Curve Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHPA\u0026nbsp;\u003c/strong\u003e\u0026nbsp; Human Protein Atlas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCGA\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCPTAC\u003c/strong\u003e\u0026nbsp; \u0026nbsp; Clinical Proteomic Tumor Analysis Consortium\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the course of this research, I extend my sincere appreciation to my laboratory colleagues for their invaluable technical support and insightful suggestions during the experimental procedures. I would like to express gratitude to all those who have contributed to this study, with special acknowledgment to my mentor, Professor Lu Yanping. The professional insights provided by you have significantly propelled the advancement of our work. Your guidance and support have been instrumental in ensuring the seamless progression of this research, facilitating continuous learning and improvement on my part. Once again, I wish to convey my heartfelt gratitude.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Key Research and Development Program\u0026nbsp;(No. 2021YFC1005300). The funders played a significant guiding role in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within supplementary information files. If the editorial department or readers want more detailed data, please feel free to contact Dr. Shen Yahui. His address is [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no\u003cem\u003e\u0026nbsp;conflict of interest\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;-\u0026nbsp;Approval of the research protocol by an Institutional Reviewer Board:\u0026nbsp;N/A\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;- Informed Consent:\u0026nbsp;N/A\u003cbr\u003e\u0026nbsp;\u0026nbsp;- Registry and the Registration No. of the study/trial: N/A\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;- Animal Studies: N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYahui Shen conceived and designed the experiments, conducted the experiments, prepared charts and tables, and drafted or reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eYanping Lu conceived and designed the experiments, oversaw the entire experimental process, reviewed the manuscript, and approved the final draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu-Rustum N, Yashar C, Arend R, et al. 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J Hematol Oncol. 2021, 14(1):7. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-020-01014-w\u003c/span\u003e\u003cspan address=\"10.1186/s13045-020-01014-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"NR3C1, Uterine corpus endometrial carcinoma, Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes, Gene Ontology","lastPublishedDoi":"10.21203/rs.3.rs-4383100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4383100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC), a prevalent malignancy in the female reproductive system, has witnessed a 30% increase in recent year. Recognizing the significance of early treatment in reducing patient mortality, the identification of potential biomarkers for UCEC plays a crucial role in early diagnosis. This study was to identify key genes associated with UCEC utilizing the Gene Expression Omnibus (GEO) database, followed by validating their prognostic value across multiple databases. Analysis of four UCEC databases (GSE17025, GSE36389, GSE63678, GSE115810) yielded 72 co-expressed genes. KEGG and GO enrichment analyses revealed their involvement in physiological processes such as transcriptional misregulation in cancer. Constructing a Protein-Protein Interaction (PPI) network for these 72 genes, the top 10 genes with significant interactions were identified. Survival regression analysis highlighted \u003cem\u003eNR3C1\u003c/em\u003e as the gene with a substantial impact on UCEC prognostic outcomes. Differential expression analysis indicated lower expression of \u003cem\u003eNR3C1\u003c/em\u003e in UCEC compared to normal endometrial tissue. Cox regression analysis, performed on clinical datasets of UCEC patients, identified clinical stage III, clinical stage IV, age, and \u003cem\u003eNR3C1\u003c/em\u003e as independent prognostic factors influencing UCEC outcomes. The LinkedOmics online database revealed the top 50 positively and negatively correlated genes with \u003cem\u003eNR3C1\u003c/em\u003e in UCEC. Subsequent investigations into the relationship between \u003cem\u003eNR3C1\u003c/em\u003e and tumor-infiltrating immune cells were conducted using R software. Gene set enrichment analysis (GSEA) provided insights into \u003cem\u003eNR3C1\u003c/em\u003e-related genes, showing enrichment in processes such as Ribosome, Oxidative phosphorylation in UCEC. Collectively, these comprehensive analyses suggest that \u003cem\u003eNR3C1\u003c/em\u003e may serve as a potential biomarker indicating the prognosis of UCEC.\u003c/p\u003e","manuscriptTitle":"Expression and prognosis of NR3C1 in uterine corpus endometrial carcinoma based on multiple datasets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 20:10:19","doi":"10.21203/rs.3.rs-4383100/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":"6ff85097-d0f6-4329-ba37-615f778007be","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32331275,"name":"Biological sciences/Cancer/Gynaecological cancer/Endometrial cancer"},{"id":32331276,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":32331277,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2024-11-02T11:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 20:10:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4383100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4383100","identity":"rs-4383100","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}