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. 2025 Mar 20;16:370. doi: 10.1007/s12672-025-02086-1

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

Yahui Shen 1,✉, Peihan Yang 2, Yanping Lu 1,✉
PMCID: PMC11926321  PMID: 40113641

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 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 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 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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02086-1.

Keywords: NR3C1, Uterine corpus endometrial carcinoma, Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes, Gene ontology

Introduction

Uterine corpus endometrial carcinoma (UCEC), a common malignancy arising 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. Multiple risk factors contribute to the occurrence and progression of UCEC, including prolonged exposure to estrogen, metabolic disorders 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 classified 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, accounting for 3.88% of female malignant tumors, ranking just  behind cervical cancer. In developed countries such as the United States and Europe, UCEC is the most prevalent gynecologic malignancy [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 [1].

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 [5]. Extensive research has demonstrated that NR3C1 is predominantly expressed in various cellular compartments, including the cell membrane, cytosol, 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 [6]. 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 [7]. 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.

Material and methods

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 [8] (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 [9] (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63678) and GSE115810 [10] (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 [11, 12] (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.

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 [13–16]. 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 [17]. 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.

Construction of PPI network and screening of key genes

Protein–protein interaction (PPI) Networks leverage the STRING online database 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 [18] (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 [19]. The Cytohubba plug-in in Cytoscape was utilized to screen and identify the top ten key genes among DEGs for further detailed analysis.

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 [20, 21]. 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, GEPIA 1.0 (http://gepia.cancer-pku.cn/detail.php?gene=&clicktag=survival) was used to analyze the correlation between the expression of ten key genes in tumors and overall survival (OS). Samples were divided into high-expression and low-expression groups based on the median expression level of ten genes. Statistical analysis was performed using the Log-rank test to calculate hazard ratios (HR) and p-values with 95% confidence intervals (CI). The analysis was performed using UCEC datasets, with survival time measured in months. The analysis results were then screened based on a significance threshold of p < 0.05 to identify hub genes.

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 [22]. 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.

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 [23]. 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 [24]. 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 [25]. 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.

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 [26] (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.

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 [27] (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.

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 [28]. 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 [29] (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 [30] (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.

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 [31]. 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 [32] (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.

Results

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. 1a–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. 1e). These 72 co-expressed genes warrant further investigation for their potential implications in UCEC pathogenesis.

Fig. 1.

Fig. 1

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Identification of differentially expressed genes in UCEC based on the GEO database. 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. e The four datasets showed an overlap of 72 genes using a Venn diagram

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. 2a). 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. 2b). The changes in CC mainly involve the Nucleus, Nucleoplasm, and Chromosomes (Fig. 2c). 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. 2d).

Fig. 2.

Fig. 2

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Significantly functional enrichment pathway of 72 DEGs. a KEGG pathway enrichment analysis. 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 < 0.05 was set as the threshold

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. 3a). 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. 3b). These ten genes are recognized as core key genes that potentially play a significant role in the development of UCEC.

Fig. 3.

Fig. 3

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PPI network and the most significant module of DEGs. a The PPI network of DEGs was constructed using STRING. b The top most significant module was obtained from CytoHubba plugin

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. 4a–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. 4k). 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.

Fig. 4.

Fig. 4

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Overall survival analysis and expression of hub genes in normal and cancer tissues. a–j Survival curves analysis for GATA2, NR3C1, SFRP4, MAF, MUC1, WT1, OGN, FOXL2, UBE2I and JUN in in normal endometrial tissue and UCEC tissue. k Differential expression of NR3C1 in the normal and cancer tissues (TIMER). p-value Significant Codes: *< 0.05. **< 0.01. ***< 0.001

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. 5a–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. 5e). 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.

Fig. 5.

Fig. 5

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ROC and DCA curve analysis. a–d ROC curve analysis of NR3C1 on the GEO database (GSE17025, GSE36389, GSE63678 and GSE115810). e DCA curve analysis shows the net benefit of NR3C1 in the 5-year survival

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. 6a–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.

Fig. 6.

Fig. 6

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a–c Cox analysis of clinical stage, age, NR3C1 expression

Table 1.

Association with overall survival and clinicopathologic characteristic in TCGA patients using Cox regression analysis

Clinical characteristics HR (95% CI) p-value
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**
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*P < 0.05, **P < 0.01, ***P < 0.001

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 [33, 34]. 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. 7a). A correlation heatmap depicting the relationships between these 22 immune cell types in UCEC was generated (Fig. 7b). 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. 7c). 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. 7d–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.

Fig. 7.

Fig. 7

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The relationship between the NR3C1 expression and tumor-infiltration immune cells. a Barplot showed the relative content of 22 immune cells in UCEC samples. b Block diagram showed the correlation of 22 immune cells in UCEC (Note: *< 0.05. **< 0.01. ***< 0.001.). c Violin diagram showed the difference of NR3C1 expression in 22 immune cells. High expression groups are indicated in red and low expression groups in blue. d–n Scatterplot showed the correlation between NR3C1 and immune cells

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. 8a, 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.

Fig. 8.

Fig. 8

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Analysis of the protein expression level of NR3C1 by the Human Protein Atlas (HPA) database. a Analysis of the protein expression level of NR3C1 in Endometrium by the HPA database. b Analysis of the protein expression level of NR3C1 in UCEC by the HPA database

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. 9a). 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. 9b). These findings contribute to a deeper understanding of the molecular interactions and potential regulatory networks involving NR3C1 in UCEC.

Fig. 9.

Fig. 9

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Heat map of the co-expression gene NR3C1 in UCEC. a Heatmap of positively correlated expression genes. b Heatmap of negatively correlated expressed genes

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
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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. 10a). 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. 10b). 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. 10c). 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. 10d).

Fig. 10.

Fig. 10

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Enrichment plots from gene set enrichment analysis (GSEA). a–d Differential enrichment of gene in KEGG, GO-BP, GO-CC and GO-MF pathways with high NR3C1expression. (KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function)

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 [3]. 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 [35]. 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 [36]. 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 [37]. 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 [38]. 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 [39]. 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 [40]. 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 [41]. 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.

Previous research has thoroughly investigated the role of NR3C1 in the development and progression of UCEC, offering valuable insights. For example, Mayayo-Peralta investigated the dual role of the NR3C1 in cancer, examining its potential as both a tumor suppressor and an oncogene. The authors also evaluated its potential as a therapeutic target, with a particular focus on its application in hormone-dependent cancers [42]. Vahrenkamp focused on the immune microenvironment, revealing NR3C1’s dual role in inhibiting tumor growth while promoting immune tolerance, highlighting its potential as an immunotherapy target [43]. Tangen specifically studied NR3C1 in UCEC, emphasizing its involvement in hormonal signaling and tumor progression, and proposing it as a prognostic marker for UCEC [44]. Together, these studies lay a solid foundation for further research on NR3C1’s role in the biology and treatment strategies of UCEC.

In our study, we present a more comprehensive analysis compared to prior research by employing an integrated bioinformatics approach, which combines GEO data, KEGG and GO analyses, and Cox regression to elucidate the functional role of NR3C1 in UCEC. Our results suggest that downregulation of NR3C1 may serve as an early biomarker for disease onset. Furthermore, we investigate immune cell interactions within the tumor microenvironment, demonstrating a novel link between NR3C1 expression and immune therapy responses. This insight offers significant clinical implications, paving the way for personalized treatment strategies in UCEC. Additionally, our findings suggest that NR3C1 may function as an independent prognostic factor for UCEC progression and could play a critical role in early detection, prognosis, and predicting treatment responses, particularly to immune checkpoint inhibitors. These findings highlight the potential of NR3C1 as a valuable biomarker in the management and treatment of UCEC.

Our findings show that NR3C1 expression is significantly dysregulated in UCEC tissues compared to normal endometrial tissues, indicating its potential as a biomarker for early detection. Early diagnosis of UCEC remains a challenge, and identifying reliable biomarkers could significantly improve diagnostic accuracy. The downregulation of NR3C1 in UCEC suggests that it may be an early indicator of disease development [44]. If validated in clinical settings, NR3C1 could serve as a measurable biomarker for UCEC, particularly in high-risk populations, facilitating earlier diagnosis and enabling timely intervention before the disease reaches advanced stages.

In addition to its potential role in early detection, NR3C1 expression was found to be strongly correlated with survival outcomes in UCEC patients, further supporting its potential as a prognostic marker [45]. Lower expression levels of NR3C1 were associated with poorer survival, indicating that NR3C1 downregulation may correlate with more aggressive forms of UCEC. Clinically, NR3C1 could be used to stratify patients based on prognosis, aiding in the prediction of disease progression and helping to tailor individualized treatment strategies.

A significant aspect of this study is the observed relationship between NR3C1 expression and immune cell infiltration in UCEC tissues. Immune checkpoint inhibitors, such as PD-1/PD-L1 inhibitors, are a cornerstone of modern cancer immunotherapy, but their efficacy varies among patients [46]. Understanding the molecular factors that influence immune responses to these therapies is crucial for improving patient outcomes. Our study suggests that upregulation of NR3C1 correlates with increased infiltration of naïve B cells, memory CD4 + T cells, and other immune cell populations involved in tumor immunity. This finding implies that NR3C1 expression could play a role in shaping the immune microenvironment and could be used to predict a patient’s response to immune checkpoint inhibitors.Specifically, the correlation between NR3C1 expression and immune cell populations such as naïve B cells and memory CD4 + T cells suggests that NR3C1 could be a useful biomarker for selecting patients who are more likely to benefit from immunotherapy [47, 48]. Clinicians could use NR3C1 expression levels to predict whether a patient's tumor is likely to respond to PD-1/PD-L1 inhibitors, enabling more precise patient selection and potentially improving treatment outcomes by directing patients to the most effective therapy.

However, several limitations of this study should be acknowledged. First, due to time and funding constraints, no clinical experimental validation was performed, which limits the immediate clinical applicability of the findings. Additionally, while bioinformatics analyses suggest an association between NR3C1 expression and prognosis, as well as immune responses in UCEC, these findings need to be validated through comparative studies using UCEC tissue and normal endometrial tissue samples to ensure the robustness and reliability of the results. Furthermore, although public databases provide a wealth of gene expression data, potential biases in sample selection and the lack of representativeness may limit the generalizability of the findings to real-world clinical settings. Future studies should aim to increase the sample size and include a more diverse patient population, encompassing various pathological subtypes of UCEC as well as women from different ethnic backgrounds, etc. Moreover, integrating bioinformatics findings with clinical data, as well as conducting in vitro and in vivo experiments, is crucial for confirming the precise role of NR3C1 in UCEC. In conclusion, further investigation into the expression of NR3C1 in UCEC, along with a deeper understanding of its underlying mechanisms, may lead to the identification of novel biomarkers for UCEC and provide promising targets for immunotherapy. Such research would not only improve our understanding of the pathogenesis of endometrial cancer but also open new avenues for optimizing clinical treatment strategies.

Conclusion

In conclusion, the findings from this study suggest that NR3C1 could serve as a valuable biomarker for early detection and prognosis in UCEC. Its expression patterns not only reflect disease progression but also provide insight into the immune landscape of the tumor microenvironment, making it a potential predictor of immune checkpoint inhibitor efficacy. Future studies should aim to clinically validate these findings through prospective studies and clinical trials to further assess the reliability and applicability of NR3C1 as a diagnostic and prognostic tool for UCEC.

To fully establish the clinical relevance of NR3C1, further experimental studies are needed to explore its role in tumor progression and immune modulation. In vitro and in vivo experiments, as well as comparative studies using primary UCEC and normal endometrial tissues, will be crucial in understanding the precise biological functions of NR3C1. Moreover, integrating bioinformatics findings with clinical data will provide a more comprehensive understanding of how NR3C1 can be used to predict treatment responses, particularly in the context of immunotherapy. Continued investigation of NR3C1 as both a biomarker and therapeutic target holds great promise for improving clinical strategies for UCEC, enhancing patient management, and ultimately improving clinical outcomes.

Supplementary Information

Additional file 1 (XLSX 35 KB) (34.5KB, xlsx)
Additional file 2 (XLSX 4423 KB) (4.3MB, xlsx)
Additional file 3 (XLSX 328 KB) (328.1KB, xlsx)
Additional file 4 (XLSX 103 KB) (103.1KB, xlsx)

Acknowledgements

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.

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 characteristic

AUC

Area under the curve

DCA

Decision curve analysis

HPA

Human Protein Atlas

TCGA

The Cancer Genome Atlas

CPTAC

Clinical Proteomic Tumor Analysis Consortium

Author contributions

Yahui Shen and conceived and designed the experiments, conducted the experiments, prepared charts and tables and drafted or reviewed the manuscript. During this revision process, Peihan Yang provided significant technical support, including code optimization, database validation, and other essential contributions. Yanping Lu conceived and designed the experiments, oversaw the entire experimental process, reviewed the manuscript, and approved the final draft.

Funding

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 shengzhifq@gmail.com.

Declarations

Approval of the research protocol by an Institutional Reviewer Board

Not applicable.

Informed Consent

Not applicable.

Registry and the Registration No. of the study/trial

Not applicable.

Animal Studies

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yahui Shen, Email: shengzhifq@gmail.com.

Yanping Lu, Email: luyp301@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1 (XLSX 35 KB) (34.5KB, xlsx)
Additional file 2 (XLSX 4423 KB) (4.3MB, xlsx)
Additional file 3 (XLSX 328 KB) (328.1KB, xlsx)
Additional file 4 (XLSX 103 KB) (103.1KB, xlsx)

Data Availability Statement

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 shengzhifq@gmail.com.

Articles from Discover Oncology are provided here courtesy of Springer

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