Identification and Clinical Validation of Key Immune Cells in Endometrial Cancer: A Focus on Mast Cells and Dendritic Cells

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Methods The CIBERSORT algorithm analyzed 22 immune cell subtypes based on GEO and TCGA datasets. Transcriptomics was performed on 13 EC tumors and 6 adjacent non-tumorous tissues (ANT). Immunohistochemistry (IHC) was conducted on 50 EC and 15 ANT samples, correlating with clinical features. Results Mast cells (MCs) and dendritic cells (DCs) were dysregulated. Transcriptomics identified nine signature genes differentially expressed for MCs and DCs. MC-specific genes (e.g., CMA1, CTSG, CPA3) were downregulated in EC, enriched in secretory granule function and pathways like renin-angiotensin system. DC-associated genes (e.g., CAPG, CCNA1, TNFAIP2) were upregulated, enriched in chemotaxis and cytokine interactions. IHC confirmed significantly reduced MC marker CD117 in EC, correlating with higher BMI, advanced FIGO stage, and metastasis (P < 0.05). Conversely, DC marker CD11c was elevated, associated with advanced stage and metastasis (P < 0.05). Conclusion Our findings identify MCs and DCs as pivotal immune cells in endometrial carcinoma, with MC suppression and DC-driven pro-tumorigenic activity showing significant correlations with advanced clinicopathological features. These immune subsets and their associated signature genes may serve as prognostic biomarkers and therapeutic targets for remodeling the EC microenvironment. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology Endometrial cancer CIBERSORT algorithm Mast cells Dendritic cells Tumor microenvironment Immunohistochemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Endometrial cancer (EC) represents one of the most prevalent malignancies in the female reproductive system, with The Lancet reporting a lifetime risk of approximately 3% for women [ 1 ] . Its incidence and mortality rates demonstrate an annual increase of ~ 1% [ 2 ] . However, effective early screening and diagnostic strategies remain clinically unavailable to address the escalating burden of EC [ 3 ] . Current therapeutic approaches, including surgery, radiotherapy, chemotherapy, and hormonal therapy, have achieved partial control of the disease; however, they do not provide significant improvements in long-term survival rates or quality of life [ 4 ] . Emerging immunotherapies show promise in extending survival benefits for patients with EC. Although substantial challenges persist in elucidating immune mechanisms, identifying critical immune cell-related gene pathways, and discovering therapeutic targets during EC pathogenesis. The tumor microenvironment (TME), comprising tumor cells, immune cells, stromal components, and the extracellular matrix, serves as a critical niche for neoplastic proliferation and metastasis [ 5 ] . Immune cells within TME exhibit functional duality, executing immunosurveillance while paradoxically facilitating immune escape mechanisms [ 6 ] . Despite recent breakthroughs in EC microenvironment research, significant knowledge gaps remain regarding the dynamic evolution of key immune cell populations, their regulatory mechanisms in tumorigenesis/progression, and prognostic implications – necessitating systematic investigation [ 7 ] . Advancements in biomedical databases provide unprecedented opportunities for TME exploration. The Gene Expression Omnibus (GEO) database utilizes microarray technology for known gene expression profiling [8], whereas the Cancer Genome Atlas (TCGA) employs RNA sequencing (RNA-seq) to characterize both annotated and novel transcripts [ 9 ] . These repositories offer comprehensive genomic profiles, clinicopathological data, and high-throughput sequencing resources that facilitate EC research. CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), a computational deconvolution algorithm developed by Stanford researchers and published in Nature Methods (2015), enables the precise quantification of immune cell infiltration in heterogeneous tissue samples [ 10 ] . This method leverages cell-type-specific gene expression signatures to infer immune cell proportions using linear support vector regression, establishing itself as a gold standard bioinformatics tool for immune cell profiling [ 11 ] . The functional dynamics of critical immune cell populations within the EC microenvironment—particularly their spatiotemporal regulation of tumor-immune interactions and mechanistic contributions to immunotherapeutic responses—constitute a pivotal research frontier in oncological immunology. Elucidating these processes holds transformative potential for developing innovative immunotherapeutic strategies against EC. This study leverages gene expression datasets from the GEO and TCGA databases to characterize 22 immune cell subpopulations using the CIBERSORT algorithm, aiming to identify pivotal immune cells in EC. Transcriptome analysis of clinical specimens was performed via RNA sequencing (RNA-seq) to delineate the expression profiles, biological roles, and regulatory signaling pathways of signature genes associated with these key immune cells in EC. Concurrently, immunohistochemical (IHC) validation was performed to quantify the spatial distribution of key immune cells and assess their correlations with clinicopathological parameters. By integrating multi-omics, multi-layer, and multi-angle analytical approaches, this work provides a novel perspective for understanding the immune microenvironment of EC, offering a novel conceptual framework for discovering immune cell-derived biomarkers and advancing targeted immunotherapeutic strategies. 2. Results 2.1 Identification of key immune cells via integrated analysis of GEO and TCGA databases To delineate the heterogeneity of immune microenvironments in EC tissues and normal tissues, compositional analysis of 22 immune cell subsets (Fig. 1 A) revealed elevated proportions of activated dendritic cells, activated mast cells, and M0 macrophages in tumor tissues, alongside reduced frequencies of activated NK cells, resting mast cells, and naïve CD4 + T cells. Bar plots from the GSE106191 and TCGA-UCEC datasets (Fig. 1 C-D) further illustrated distinct immune cell distribution patterns between groups. Immune cell correlation heatmaps (Fig. 1 B) delineated a complex regulatory network between the subpopulations: resting mast cells exhibited positive correlations with naïve CD4 + T cells (r = 0.55) and activated NK cells (r = 0.67), but negative correlations with activated mast cells (r = − 0.43) and plasma cells (r = − 0.44). Significant positive associations were observed between naïve CD4 + T cells and activated NK cells (r = 0.60), follicular helper T cells and M1 macrophages (r = 0.68), and CD8 + T cells (r = 0.60). These correlations suggest potential co-regulatory dynamics, where positive interactions imply synchronized abundance fluctuations, while negative correlations indicate antagonistic relationships. Integrated analysis of GSE106191 and TCGA-UCEC datasets demonstrated a significantly lower abundance of resting mast cells (GSE: P = 0.007; TCGA: P < 0.001) and a higher abundance of activated dendritic cells (GSE: P = 0.035; TCGA: P = 0.002) in EC tissues compared to normal tissues. TCGA data further identified 10 immune subsets enriched in EC, including M0 macrophages and inactivated mast cells (all P < 0.05, Fig. 1 E-F), suggesting their potential roles in pro-tumorigenic microenvironment remodeling. By cross-validating both datasets, we identified resting mast cells and activated dendritic cells as key immune cells due to their statistically significant differences in abundance in EC. However, given technical limitations in the experimental detection of cellular activation states, mast cells (MCs) and dendritic cells (DCs) were prioritized as critical immune populations for subsequent functional validation. 2.2 Sequencing data quality control and differential expression analysis Transcriptome sequencing of 19 clinical specimens generated 123.09 Gb of high-quality raw data (Supplementary Table 1), with all samples meeting stringent quality thresholds: sequencing depth ≥ 6.03 Gb, Q30 > 93.88%, and GC content ranging from 46.23% to 53.11%. Post-quality control analysis identified 40,165 expressed genes and 191,742 transcripts. Differential expression analysis using DESeq2 (threshold: log2(fold change) ≥ 1, adjusted P < 0.05) revealed 5,998 differentially expressed genes (DEGs), comprising 3,455 upregulated and 2,543 downregulated genes. These findings underscore significant transcriptional heterogeneity between EC tissues and paired ANT. 2.3 Signature gene expression and functional enrichment analysis of MCs Among 17 MCs-associated signature genes, nine significantly differentially expressed genes (DEGs) were identified through sequencing data. Box plots (Fig. 2 A) revealed the downregulation of CMA1, CTSG, ITGA9, ADAMTS3, CPA3, and EGR3 in EC tissues, while CPM, HSPA6, and S100A4 exhibited no significant expression changes. Functional enrichment analysis of MCs signature DEGs was performed using GO and KEGG databases. GO analysis highlighted enrichment in biological processes related to protein processing and maturation, angiotensin maturation, peptide hormone processing, and signal receptor ligand precursor processing. Cellular components were primarily localized to the extracellular matrix, extracellular region, vesicles, secretory granules, and exosomes. Molecular functions were enriched in peptidase activity (Fig. 2 B). KEGG pathway analysis demonstrated significant enrichment in the renin-angiotensin system, cell adhesion molecules, protein processing in the endoplasmic reticulum, neutrophil extracellular trap formation, estrogen signaling pathway, viral carcinogenesis, and endocytosis (Fig. 2 C). 2.4 Signature gene expression and functional enrichment analysis of DCs Among 35 signature genes related to DCs, nine differentially expressed genes (DEGs) were identified through sequencing data analysis. The boxplot (Fig. 3 A) revealed that CAPG, CCNA1, and TNFAIP2 were significantly upregulated in EC tissues, while CD302, SIGLEC5, and SNURF were downregulated. In contrast, TNFSF14, SLAMF9, and UBD exhibited no significant differential expression. Functional enrichment analysis of DEGs in DCs-associated genes demonstrated the following patterns: GO biological processes were predominantly enriched in positive regulation of T cell chemotaxis, regulation of T cell chemotaxis, myeloid dendritic cell differentiation, positive regulation of lymphocyte chemotaxis, and activation of myeloid dendritic cells. Cellular components were primarily associated with cyclin A1-CDK2 complex and cyclin A2-CDK2 complex (Fig. 3 B). KEGG pathway analysis highlighted significant enrichment in viral carcinogenesis, human T-cell leukemia virus type 1 (HTLV-1) infection, transcriptional misregulation in cancer, cell cycle, cellular senescence, cytokine-cytokine receptor interaction, AMPK signaling pathway, progesterone-mediated oocyte maturation, pathways in cancer, and NF-κB signaling pathway (Fig. 3 C). 2.5 Immunohistochemical staining using CD117 and CD11c for MCs and DCs The IHC results demonstrated that CD117-positive signals were localized in the cytoplasm of MCs, exhibiting characteristic brownish granular cytoplasmic staining (Fig. 4 A). CD11c-positive signals were distributed on the cell membrane and cytoplasm of DCs, with distinct brownish-yellow positivity observed in 66% (33/50) of EC specimens. In contrast, ANT displayed only faint yellowish weak positivity (Fig. 4 C). Quantitative analysis revealed that the CD117-positive cell density in EC tissues was 12.8 ± 7.15 cells per high-power field (HP), significantly lower than that in ANT (17.68 ± 6.83/HP, P < 0.05). Conversely, the CD11c-positive cell density in EC tissues (4.49 ± 3.22/HP) was markedly higher compared to ANT (1.43 ± 1.19/HP, P < 0.01) (Table 1 , Fig. 4 B-D). Table 1 Differential expression of CD117 and CD11c between EC tissues and ANT(x ± s). CD117 CD11c n (x ± s) t p n (x ± s) t p EC tissues 50 12.8 ± 7.15 -2.341 0.022 33 4.49 ± 3.22 4.794 < 0.01 ANT 15 17.68 ± 6.83 15 1.43 ± 1.19 2.6 The expression levels of CD117 and CD11c and their correlations with clinicopathological characteristics were analyzed. CD117-positive cell density demonstrated statistically significant differences concerning BMI, FIGO stage, and lymph node metastasis (all P < 0.05). Specifically, patients with advanced-stage (III- IV) disease exhibited a higher CD117-positive cell density (16.05 ± 8.36 cells/HP) compared to those with early-stage (I–II) disease (11.07 ± 6.82 cells/HP). Similarly, the CD117-positive cell density was elevated in patients with positive lymph node metastasis (16.54 ± 8.77 cells/HP) versus their negative counterparts (11.43 ± 6.83 cells/HP). Notably, a BMI < 28 kg/m² was associated with increased CD117-positive cell density (14.66 ± 7.90 cells/HP) relative to a BMI ≥ 28 kg/m² (8.97 ± 5.97 cells/HP). However, CD117 expression showed no significant correlation with patient age, depth of myometrial invasion, or menopausal status (all P > 0.05) (Table 2 ). For CD11c-positive cell density, statistically significant differences were observed concerning FIGO stage and lymph node metastasis (P < 0.05). Advanced-stage (III- IV) patients displayed higher CD11c-positive cell density (5.77 ± 3.59 cells/HPF) compared to early-stage (I–II) patients (3.42 ± 2.50 cells/HPF). Similarly, the CD11c-positive cell density was significantly elevated in lymph node metastasis-positive patients (6.35 ± 3.51 cells/HPF) versus negative patients (3.28 ± 2.40 cells/HPF). In contrast, CD11c expression demonstrated no significant association with patient age, depth of myometrial invasion, BMI, or menopausal status (all P > 0.05). (Table 2 ). Table 2 Relationship between CD117, CD11c expression in EC tissues and clinicopathological features (x ± s) Parameter Category CD117 CD11c n x ± s t p n x ± s t p Age < 50 12 13.6 ± 9.23 0.271 0.788 8 3.65 ± 2.57 -0.845 0.405 ≥ 50 38 12.89 ± 7.42 25 4.76 ± 3.4 BMI(kg/m²) < 28 36 14.66 ± 7.9 2.429 0.019 25 4.52 ± 3.12 0.09 0.929 ≥ 28 14 8.97 ± 5.97 8 4.4 ± 3.74 Staging I-II 30 11.07 ± 6.82 -2.308 0.025 18 3.42 ± 2.5 -2.212 0.034 III-VI 20 16.05 ± 8.36 15 5.77 ± 3.59 Table 2 (continued) Relationship between CD117, CD11c expression in EC tissues and clinicopathological features (x ± s) Parameter Category CD117 CD11c n x ± s t p n x ± s t p Myometrial Invasion < 1/2 29 12.13 ± 7.37 -0.995 0.325 16 3.85 ± 2.41 -1.114 0.274 ≥ 1/2 21 14.35 ± 8.35 17 5.09 ± 3.8 Lymph node metastasis Negative 34 11.43 ± 6.83 -2.250 0.029 20 3.28 ± 2.4 -2.995 0.005 Positive 16 16.54 ± 8.77 13 6.35 ± 3.51 Menopause Postmenopausal 30 13.03 ± 7.62 -0.034 0.973 20 4.79 ± 3.59 0.656 0.517 Premenopausal 20 13.11 ± 8.24 13 4.03 ± 2.61 3. Discussion This study analyzed the composition, intercellular correlations, and relative abundance differences of immune cells in EC using gene expression data from GEO and TCGA databases, employing the CIBERSORT algorithm and R software. The findings revealed a dynamic subpopulation imbalance between MCs and DCs in EC tissues: resting MCs exhibited lower relative abundance than normal tissues, while activated DCs showed elevated abundance. Both cell types demonstrated specific regulatory associations with immune subpopulations, such as CD4⁺ T and plasma cells. Under physiological homeostasis, MCs primarily contribute to immune surveillance, inflammatory responses, and tissue repair [ 12 ] . However, MCs display complex dual roles dynamically regulated by tumor type and progression stage. Tumor-associated MCs drive angiogenesis, stromal remodeling, and immune evasion by releasing pro-angiogenic factors, matrix metalloproteinases, and immunosuppressive cytokines [ 13 ] . Conversely, they may suppress tumor growth via cytotoxic granule release, DCs/T-cell immune activation, and secretion of anti-angiogenic mediators like prostaglandin D2. This functional heterogeneity is particularly pronounced in solid tumors—for instance, MC infiltration correlates with both enhanced tumor invasiveness and improved prognosis in breast cancer. In contrast, in prostate cancer, MCs predominantly promote angiogenesis and reduce the risk of recurrence [ 14 ] . As pivotal antigen-presenting cells, DCs initiate adaptive immunity by activating anti-tumor T-cell responses [ 15 ] . However, their functionality is frequently suppressed by TME-derived inhibitory factors and immunosuppressive cells, such as myeloid-derived suppressor cells, leading to immune tolerance [ 16 ] . Distinct DC subsets—including conventional DC1 (cDC1), DC2 (cDC2), and plasmacytoid DCs (p-DCs)—exhibit marked heterogeneity in tumor immunomodulation, with their roles finely regulated by tumor-specific signaling pathways [ 17 ] . These findings underscore the necessity of elucidating the mechanistic roles of MCs and DCs in EC progression, which may inform novel immunotherapeutic strategies targeting TME reprogramming, immune checkpoint modulation, or subset-specific functional enhancement. The study identified 17 MC signature genes, with nine significantly differentially expressed genes (DEGs) selected through sequencing data analysis. CMA1, CPA3, CTSG, ITGA9, ADAMTS3, and EGR3 exhibited downregulation in EC tissues, consistent with a reduced abundance of resting MCs. Functional enrichment analysis of MC-related DEGs revealed their predominant involvement in angiotensin maturation and extracellular matrix regulation via GO analysis. KEGG pathway analysis highlighted enrichment in the renin-angiotensin system and cell adhesion molecule pathways. Recent studies have demonstrated that localized RAS activation promotes EC cell proliferation via angiotensin II receptor type 1 (AGTR1) [ 18 ] , suggesting that MC-derived angiotensin-converting enzyme may serve as a therapeutic target [ 19 ] . MCs may influence TME and angiogenesis by regulating the expression of extracellular matrix components and Cell adhesion molecules. Shi et al. [ 20 ] demonstrated that CMA1 is highly expressed in gastric cancer tissues and correlates with poor patient prognosis. In contrast, Xie et al. [ 21 ] reported reduced CMA1 expression in colorectal cancer (CRC), where resting MCs (characterized by low CMA1 expression) are diminished, while activated MCs (expressing elevated TPSAB1, CPA3, and KIT) are enriched. Activated MCs in CRC may exert antitumor effects through the KITLG-KIT axis. These findings suggest that CMA1 downregulation in EC could reflect a shift toward an activated MC phenotype, which may suppress tumor progression via cytokine secretion and immune cell activation. Chan et al. [ 22 ] identified CTSG downregulation in CRC, where its low expression is associated with adverse clinical outcomes. Mechanistically, CTSG inhibits CRC cell proliferation and induces apoptosis by suppressing the Akt/mTOR/Bcl2 signaling pathway. Similarly, Hua et al. [ 23 ] revealed that CTSG downregulation in head and neck squamous cell carcinoma (HNSC) promotes tumor cell proliferation and migration via hyperactivation of the JAK2/STAT3 pathway. In EC, CTSG downregulation is hypothesized to modulate JAK2/STAT3 signaling, potentially reversing its tumor-suppressive effects on proliferation and migration. These insights position CTSG as a promising diagnostic biomarker and therapeutic target in EC. Other MCs-associated genes, including CPA3, ITGA9, EGR3, and ADAMTS3, contribute to diverse oncogenic processes such as immune regulation, cell migration, tumor invasion, and tissue remodeling across malignancies [ 24 – 27 ] . These findings suggest that MCs in EC may engage in immune surveillance, tumor suppression, and multifaceted biological regulation. However, the functional heterogeneity of MCs across tumor types—driven by distinct TME dynamics and pathogenesis—necessitates further experimental validation to elucidate their precise roles in EC progression. Through sequencing data analysis of 35 DCs signature genes, nine differentially expressed genes (DEGs) were identified. CAPG, CCNA1, and TNFAIP2 exhibited significantly elevated expression levels in EC tissues, consistent with the decreased abundance of activated DCs. Functional enrichment analysis of DCs signature gene DEGs and GO analysis demonstrated predominant involvement in biological processes such as regulating T cell chemotaxis and immune-related pathways. KEGG pathway analysis highlighted significant enrichment in critical signaling pathways, including cytokine-cytokine receptor interaction, AMPK signaling pathway, and NF-κB signaling pathway, suggesting their potential roles in modulating DCs activation and immune microenvironment dynamics. DCs may influence the immune microenvironment of EC by modulating T-cell chemotaxis and cytokine secretion, potentially contributing to immune evasion mechanisms. Zhao et al. [ 28 ] demonstrated that CAPG knockdown suppresses colorectal cancer (CRC) cell proliferation by upregulating the P53 pathway while concurrently promoting apoptosis and ferroptosis. Long et al. [ 29 ] further revealed that CAPG potentiates gastric cancer cell proliferation, migration, invasion, and metastasis via activation of the Wnt/β-catenin signaling pathway. CCNA1 (Cyclin A1), a critical regulator of the G1/S phase transition, facilitates cell cycle progression by activating CDK1/CDK2. Da Silva et al. [ 30 ] identified CCNA1 overexpression in papillary thyroid carcinoma. At the same time, Jiang et al. [ 31 ] established its pro-tumorigenic role in gastric cancer, where it promotes proliferation, cell cycle progression, and migration while inhibiting apoptosis, acting as a downstream transcriptional target of RNF6. TNFAIP2, a tumor necrosis factor α (TNF-α)-inducible protein, participates in inflammatory responses, apoptosis, angiogenesis, and tumorigenesis [ 32 ] . Ren et al. [ 33 ] elucidated that TNFAIP2 drives angiogenesis in triple-negative breast cancer through the Rac1-ERK-AP1-HIF1α signaling axis, with combinatorial inhibition of ERK (e.g., trametinib) and VEGFR (e.g., lapatinib) significantly suppressing tumor growth and vascularization in preclinical models. These findings suggest that DCs in EC may orchestrate tumor progression by regulating cell proliferation/apoptosis dynamics, cell cycle checkpoints, and angiogenic processes. However, validation through in vitro functional assays and in vivo animal models remains essential to substantiate these mechanistic hypotheses. Based on preliminary insights into the characteristic gene expression and tumor-related functions of MCs and DCs, this study further employed IHC staining to analyze these two cell types' expression and spatial distribution in tissue samples while exploring their correlation with clinicopathological features. The IHC results demonstrated that CD117-positive cells exhibited a diffuse and heterogeneously distributed pattern within the EC stromal compartment, with lower density than adjacent non-cancerous tissue(ANT). This observation aligns with findings from Mao et al. [ 34 ] , who reported similar MCs distribution patterns in colorectal cancer, where normal mucosal tissues displayed higher MCs density than tumor regions. This consistency may reflect tumor microenvironment (TME) remodeling, where the physiological milieu of adjacent tissues potentially supports MCs' survival and proliferation. In contrast, intratumoral hypoxia, nutrient competition, and tumor-derived inhibitory factors (e.g., immunosuppressive cytokines) may suppress MCs recruitment and growth [ 14 ] . Furthermore, the elevated MCs presence in ANT might signify a host immune defense mechanism against tumorigenesis, mediated through cytokine release and bioactive molecules to inhibit tumor progression. However, this protective response appears to be attenuated within the tumor core during disease progression [ 35 ] . Regarding CD11c-positive cells, distinct brown-yellow staining (indicative of strong positivity) was observed in the tumor stroma of 66% (33/50) of EC samples, whereas ANT exhibited only faint yellowish staining (weak positivity). Quantitative analysis revealed a significantly higher density of CD11c + cells in EC tissues compared to ANT samples. Notably, 34% (17/50) of EC samples showed no detectable staining, which could stem from multiple technical and biological factors. Insufficient antibody specificity might impede accurate antigen recognition, particularly given the heterogeneity in DCs' subset-specific surface markers. Suboptimal tissue fixation, inadequate antigen retrieval, or improper antibody dilution ratios may compromise staining efficacy. These findings underscore the need for protocol optimization in future studies, including validation of antibody specificity, standardization of antigen retrieval conditions, and incorporation of complementary markers to delineate DCs subsets precisely. In this study, we investigated the expression of CD117 and CD11c in EC tissues and their correlation with clinicopathological characteristics. CD117 + cell density is significantly associated with body mass index (BMI), FIGO stage, and lymph node metastasis (LNM). Specifically, higher CD117 + cell density was observed in patients with BMI < 28 kg/m², advanced FIGO stages (III-IV), or LNM-positive status. This finding aligns with the study by Goba et al. [ 36 ] , which reported elevated CD117 + cell density in normal prostate tissues compared to prostate cancer tissues. The inverse relationship between BMI and CD117 + cell density may reflect altered immune-metabolic crosstalk in obesity, where dysfunctional adipose tissue releases pro-inflammatory cytokines, induces chronic low-grade inflammation, and disrupts insulin signaling pathways, thereby modulating MCs recruitment and activation. Regarding CD11c + DCs, our data revealed that their density correlated positively with the advanced FIGO stage (III-IV) and LNM. However, conflicting evidence exists across malignancies. For instance, Lee et al. [ 37 ] demonstrated that high CD11c expression in triple-negative breast cancer was linked to higher histological grade but independent of pathological T stage, LNM, or lymphovascular invasion. Similarly, Minkov et al. [ 38 ] observed CD11c + cell density association with tumor size (T stage) in non-small cell lung cancer. However, there is no correlation with LNM, distant metastasis, clinical stage, or tumor differentiation. These discrepancies underscore the context-dependent roles of DC subsets within distinct tumor microenvironments. Current research on MCs and DCs in EC remains limited. While our study draws parallels from their roles in other malignancies (e.g., TNBC, gastrointestinal stromal tumors, and NSCLC), the observed inconsistencies in immune cell-clinicopathological correlations may stem from sample size limitations, tumor heterogeneity, or technical variability in immune cell quantification. Future investigations should employ advanced technologies such as single-cell RNA sequencing (scRNA-seq) or multiparametric flow cytometry to delineate MCs/DCs subpopulations precisely, map their spatial distribution, and characterize functional states to address these challenges. Such efforts will enhance our understanding of their mechanistic contributions to EC progression and refine precision immunotherapeutic strategies targeting these cells. While this study has delineated the spatial distribution of MCs and DCs in EC and their potential mechanistic roles, critical gaps remain in understanding their functional states, spatial interactions, and dynamic remodeling within TME. Although bioinformatics analysis, transcriptomic sequencing, and IHC have been employed to explore the biological functions of MCs/DCs and their involvement in signaling pathways, the lack of single-cell resolution data limits insights into cellular heterogeneity and intercellular cross-talk. Future studies should incorporate functional assays and animal models to validate these findings and unravel the molecular regulatory networks driving EC progression. Furthermore, integrating EC molecular subtyping (e.g., POLE-mutated, microsatellite instability-high MSI-H, copy-number low/high) could refine the interpretation of immune cell dynamics. For instance, MSI-H tumors often exhibit heightened immune infiltration and responsiveness to immune checkpoint inhibitors, whereas copy-number high subtypes may display immunosuppressive TME features [ 39 ][ 40 ] . Stratifying MCs/DCs phenotypes and functional states across these subtypes would clarify their context-dependent roles and identify subtype-specific therapeutic vulnerabilities. 4. Materials and Methods 4.1 Acquisition of transcriptomic data from public databases Transcriptomic datasets for EC were retrieved from the Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo ) and the Cancer Genome Atlas database (TCGA; https://portal.gdc.cancer.gov/ ). The GEO dataset GSE106191 [ 41 ] contains 64 EC tumors and 33 normal tissues, while the TCGA-UCEC dataset comprises 554 EC tumors and 35 normal tissues. 4.2 Collection of clinical samples Transcriptomic sequencing: Tissue specimens were collected from 13 EC patients undergoing hysterectomies at Dalian Maternal and Child Health Hospital from January 2023 to December 2024. Under sterile conditions, gynecologic surgeons and pathologists aseptically excised tumor samples (n = 13) and paired adjacent non-tumorous tissue (ANT; n = 6) samples, defined as histologically confirmed normal endometrium located more than 2 cm from the tumor margins [ 42 ] . Intraoperative specimens (5 × 5 × 2 mm³) were immediately immersed in 1.5 mL RNA-store Stabilization Reagent and incubated at 4°C for 24 hours before being transferred to -80°C for long-term storage. An IHC cohort was established using 50 archived formalin-fixed paraffin-embedded (FFPE) EC tissues from the Department of Pathology (2022–2024), with 50 tumor tissues as the experimental group and 15 pathologically confirmed ANT samples as controls. Written informed consent was obtained from all participants, and the study protocol (Ethics Approval: FEJT-KY-2024-201) was approved by the Institutional Ethics Committee. 4.3 Bioinformatics data processing Transcriptomic datasets GSE106191 (microarray) and TCGA-UCEC (RNA-seq) underwent systematic preprocessing using R software (version 4.3.1) to ensure analytical rigor. For GSE106191, probes with ambiguous genomic mapping were filtered to retain uniquely annotated gene targets, followed by cross-sample normalization to establish comparable expression baselines. The TCGA-UCEC dataset underwent gene-level deduplication, retaining entries with the highest transcripts per million (TPM) values. This pipeline generated expression matrices (GSE106191_normalized, TCGA-UCEC_TPM) suitable for downstream comparative analyses. The CIBERSORT deconvolution algorithm and the LM22 signature matrix (comprising 22 immune cell types) were applied to both preprocessed datasets for estimating immune cell fractions. A permutation analysis with 1,000 iterations was performed, and samples with a CIBERSORT P-value of less than 0.05 were retained to ensure analytical robustness. Hierarchical clustering heatmaps were generated to visualize immune cell compositional patterns, complemented by correlation matrices to elucidate intercellular interactions. Violin plots were utilized to systematically compare the distributions of immune cell abundance between EC and ANT. This integrated visualization approach delineates the spatial and temporal heterogeneity of key immune cell subsets, revealing their differential infiltration signatures. 4.4 Transcriptomic profiling of clinical samples RNA extraction and quality control Frozen EC tumors and ANT samples were pulverized by liquid nitrogen grinding, followed by total RNA extraction using QIAzol Lysis Reagent (Qiagen). RNA concentration and purity (OD260/280 ratio: 1.8–2.2) were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). RNA integrity was assessed via agarose gel electrophoresis (Biowest Agarose) and quantified using an Agilent 5300 Fragment Analyzer, with RNA quality numbers (RQN) greater than 6.5 required for downstream processing. Library preparation and high-throughput sequencing RNA samples that met the criteria for library construction (total RNA ≥ 1 µg and concentration ≥ 30 ng/µL) were processed using the Illumina® Stranded mRNA Prep kit. The workflow included the following steps: (1) mRNA enrichment through Oligo(dT) bead-based selection, (2) fragmentation and synthesis of double-stranded cDNA, (3) end repair and adapter ligation, and (4) PCR amplification. Purified libraries were quantified using a Qubit 4.0 fluorometer (Thermo Fisher Scientific) and amplified via bridge amplification on a c-Bot system. Sequencing was performed on an Illumina Nova-Seq X-Plus platform. Transcriptome assembly and primary data analysis Raw sequencing data underwent quality trimming to remove adapter sequences, low-quality bases (Phred score < 20), reads with more than 10% ambiguous nucleotides (N), and short fragments (less than 20 bp). High-quality reads were aligned to the Homo sapiens reference genome (GRCh38, Ensembl release 105; https://asia.ensembl.org ), achieving alignment rates of 95.73–97.41%. Transcript-level expression quantification was performed using RSEM (RNA-Seq by Expectation-Maximization), and differential expression analysis was conducted with DESeq2, setting significance thresholds at P < 0.05 and |log2FC|≥1. Immune signature gene screening and functional enrichment Immune cell signature gene sets were retrieved from the Molecular Signatures Database (MSigDB; https://www.gsea-msigdb.org ) and curated using R/Bioconductor packages. Key immune cell signatures were intersected with sequencing-derived differentially expressed genes (DEGs) to identify immune-related DEGs. These signature genes were subjected to Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis on the Majorbio Cloud Platform ( https://cloud.majorbio.com ), with false discovery rate (FDR) correction (q < 0.05) to elucidate their biological functions and regulatory networks. 4.5 Immunohistochemistry (IHC) Formalin-fixed, paraffin-embedded (FFPE) EC tissues and paired ANT samples were sectioned at 3 µm thickness. Two slides per case were baked at 70°C for 3 hours. Immunohistochemical staining was performed on the BenchMark GX automated stainer. Deparaffinization was achieved using 10× dewaxing buffer (75°C, 16 min), followed by high-temperature antigen retrieval with cell conditioning solution (95–100°C, 64 min). Endogenous peroxidase activity was inhibited with OptiView Peroxidase Inhibitor (37°C, 4 min). Primary antibody incubation utilized rabbit anti-human CD11c polyclonal antibody (1:100 dilution) and CD117 monoclonal antibody (36°C, 32 min). Signal detection was sequentially performed using hapten-conjugated HQ Universal Linker, HRP Multimer (37°C, 8 min each), DAB chromogen with hydrogen peroxide (37°C, 8 min), and OptiView Copper enhancer (37°C, 4 min). Nuclear counterstaining was conducted with hematoxylin (37°C, 14 min), followed by bluing reagent application (37°C, 4 min). Sections were dehydrated, cleared, mounted, and microscopically analyzed for immunohistochemical evaluation. Quantitative analysis Positive immunoreactivity was defined as cytoplasmic or nuclear staining with chromogenic intensity graded as weak (light yellow), moderate (brownish-yellow), or strong (tan). Five non-overlapping high cellularity regions were selected under a low-power field (×100 magnification), and positively stained cells were quantified in high-power fields (×400 magnification). The mean value of positive cells across five fields was calculated. Two board-certified pathologists independently; a third senior pathologist adjudicated discrepancies and evaluated all slides, scoring 10% in positive cell percentages. 4.6 Statistical methods Data analysis was performed using SPSS version 27.0 and GraphPad Prism version 9.5. Continuous variables that followed a normal distribution were reported as mean ± standard deviation (SD), while categorical data were presented as frequencies. Comparisons between groups were conducted using a two-tailed Student’s t-test, with statistical significance set at P < 0.05. Declarations Ethics All public datasets were utilized in accordance with the database access policies and the original study's ethics approvals. This study was approved by the Ethics Committee of Dalian Women's and Children's Medical Centre (Group) (Approval No. FEJT-KY-2024-201). Our study adhered to the principles outlined in the Declaration of Helsinki. Funding This study was supported by the Applied Basic Research Program of Liaoning Province (Grant No. 2023JH2/101300096). Conflict of Interest The authors declare no conflicts of interest. Acknowledgements We acknowledge Majorbio (https://www.majorbio.com) for providing the bioinformatics analysis platform. Data availability All data generated or analyzed during this study are included in this article and its supplementary material files. Further enquiries can be directed to the corresponding author. Supplementary data Supplementary Table 1 has been uploaded to the supplementary materials. Author Contribution Mengru Zhang wrote the main manuscript text， Mengru Zhang and Junhuan Wang prepared figures1-4. All authors reviewed the manuscript. References Crosbie, E. J. et al. Endometrial Cancer[J] Lancet , 399 (10333): 1412–1428. (2022). Siegel, R. L. et al. Cancer statistics, 2023[J]. CA: A Cancer Journal for Clinicians, 73(1): 17–48. (2023). Lu, K. H. & Broaddus, R. R. 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Troester, M. A. et al. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: A study from The Cancer Genome Atlas[J]. Npj Breast Cancer . 2 (1), 16007 (2016). Ethics. All public datasets. were utilized in accordance with the database access policies and the original study's ethics approvals. This study was approved by the Ethics Committee of Dalian Women's and Children's Medical Centre (Group) (Approval No. FEJT-KY-2024-201). Our study adhered to the principles outlined in the Declaration of Helsinki. Additional Declarations No competing interests reported. Supplementary Files Table1.doc Table2.doc SupplementaryTable1.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7470090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":576437476,"identity":"013946ff-e051-454e-bfc7-1385701b149c","order_by":0,"name":"Mengru Zhang","email":"","orcid":"","institution":"Dalian Women and Children Medical Center (Group)","correspondingAuthor":false,"prefix":"","firstName":"Mengru","middleName":"","lastName":"Zhang","suffix":""},{"id":576437477,"identity":"f88378c8-ccb3-411e-8e4e-b20c38060ae4","order_by":1,"name":"Junhuan Wang","email":"","orcid":"","institution":"Qingdao 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14:53:34","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138823,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/39859394fce9e6db524f9c95.html"},{"id":100695092,"identity":"56dd3b11-4e23-4f19-b384-b5d6f984e453","added_by":"auto","created_at":"2026-01-20 14:50:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":545462,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell infiltration landscape of EC in GEO and TCGA databases. (A) Relative proportions of 22 immune cell subtypes in the GSE106191 dataset, with red indicating high abundance and blue denoting low abundance. (B) Spearman correlation matrix of immune cell subtypes in the GSE106191 dataset (red: positive correlation; blue: negative correlation). (C-D) Bar plots comparing immune cell distributions between EC and normal tissues in the GSE106191 (C) and TCGA-UCEC (D) datasets. (E-F) Violin plots demonstrating differential immune cell abundance between EC (red) and normal tissues (blue) in the GSE106191 (E) and TCGA-UCEC (F) cohorts.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/0fcc5db2664b00b4a5ba702c.jpg"},{"id":100695115,"identity":"2fe094ff-b51f-4034-91ba-768c019d9453","added_by":"auto","created_at":"2026-01-20 14:51:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199323,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and functional enrichment analysis of MCs signature genes. (A) Differential expression box plots of MCs' characteristic genes (boxes represent the median and interquartile range, whiskers extend to the minimum and maximum values of the data). (B) GO functional enrichment: The x-axis displays the top 15 significantly enriched GO terms (ranked by adjusted P-value), while the y-axis indicates enrichment significance. Colors differentiate functional categories: biological processes (BP), cellular components (CC), and molecular functions (MF). (C) KEGG pathway enrichment: The y-axis lists the top 15 significantly enriched pathway names, and the x-axis represents the rich factor (the ratio of enriched genes to the total number of annotated genes in the pathway). Dot size corresponds to the number of genes within the pathway, and color intensity reflects the range of adjusted P-values.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/f1847c1bed3a94f11221407e.jpg"},{"id":100695155,"identity":"10b59409-84c9-472f-a020-b4b06c078dca","added_by":"auto","created_at":"2026-01-20 14:51:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":605248,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and functional enrichment analysis of DCs signature genes. (A) Differential expression box plots of DCs characteristic genes. (B) GO functional enrichment: The x-axis displays the top 10 significantly enriched GO terms, while the y-axis indicates enrichment significance. (C) KEGG pathway enrichment: The x-axis represents the rich factor, and the y-axis lists the top 15 significantly enriched pathway names.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/663fbf9bc6a938aacf5c0697.jpg"},{"id":100695231,"identity":"3a0f8c3d-a712-4cff-9d98-b096ae56687e","added_by":"auto","created_at":"2026-01-20 14:52:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":516866,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression patterns of CD117 (A) and CD11c (C) were evaluated in EC tumor（T）and adjacent non-tumorous tissue(ANT) using IHC staining. The intergroup differences in CD117-positive cell density (B) and CD11c-positive cell density (D) were statistically analyzed through bar-scatter plots. Statistical significance is denoted as follows: *p \u0026lt; 0.05; **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/0bda111c2a09b10986004609.jpg"},{"id":103984837,"identity":"79c251ac-6f60-4fed-9005-e71c2a9ad278","added_by":"auto","created_at":"2026-03-05 10:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2882438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/e577f9c4-bbfd-4f0a-bd04-cc00c3464db7.pdf"},{"id":100695156,"identity":"b160406e-c5dd-4a42-acd0-de0738e58c93","added_by":"auto","created_at":"2026-01-20 14:51:25","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14861,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/5d81703cb3834f2734de53de.doc"},{"id":100695091,"identity":"7c998a7e-d883-4aff-8d81-b658a5d25258","added_by":"auto","created_at":"2026-01-20 14:50:49","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17267,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.doc","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/6f70a4afa828ebd71cd74a9f.doc"},{"id":100695016,"identity":"5fb87085-69b5-4225-8961-69d2c27e2301","added_by":"auto","created_at":"2026-01-20 14:49:57","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17517,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7470090/v1/356d6606a90761a16290aed6.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Clinical Validation of Key Immune Cells in Endometrial Cancer: A Focus on Mast Cells and Dendritic Cells","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEndometrial cancer (EC) represents one of the most prevalent malignancies in the female reproductive system, with \u003cem\u003eThe Lancet\u003c/em\u003e reporting a lifetime risk of approximately 3% for women\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Its incidence and mortality rates demonstrate an annual increase of ~\u0026thinsp;1%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, effective early screening and diagnostic strategies remain clinically unavailable to address the escalating burden of EC\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Current therapeutic approaches, including surgery, radiotherapy, chemotherapy, and hormonal therapy, have achieved partial control of the disease; however, they do not provide significant improvements in long-term survival rates or quality of life\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Emerging immunotherapies show promise in extending survival benefits for patients with EC. Although substantial challenges persist in elucidating immune mechanisms, identifying critical immune cell-related gene pathways, and discovering therapeutic targets during EC pathogenesis.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME), comprising tumor cells, immune cells, stromal components, and the extracellular matrix, serves as a critical niche for neoplastic proliferation and metastasis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Immune cells within TME exhibit functional duality, executing immunosurveillance while paradoxically facilitating immune escape mechanisms\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Despite recent breakthroughs in EC microenvironment research, significant knowledge gaps remain regarding the dynamic evolution of key immune cell populations, their regulatory mechanisms in tumorigenesis/progression, and prognostic implications \u0026ndash; necessitating systematic investigation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdvancements in biomedical databases provide unprecedented opportunities for TME exploration. The Gene Expression Omnibus (GEO) database utilizes microarray technology for known gene expression profiling [8], whereas the Cancer Genome Atlas (TCGA) employs RNA sequencing (RNA-seq) to characterize both annotated and novel transcripts\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. These repositories offer comprehensive genomic profiles, clinicopathological data, and high-throughput sequencing resources that facilitate EC research.\u003c/p\u003e \u003cp\u003eCIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), a computational deconvolution algorithm developed by Stanford researchers and published in Nature Methods (2015), enables the precise quantification of immune cell infiltration in heterogeneous tissue samples\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This method leverages cell-type-specific gene expression signatures to infer immune cell proportions using linear support vector regression, establishing itself as a gold standard bioinformatics tool for immune cell profiling\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe functional dynamics of critical immune cell populations within the EC microenvironment\u0026mdash;particularly their spatiotemporal regulation of tumor-immune interactions and mechanistic contributions to immunotherapeutic responses\u0026mdash;constitute a pivotal research frontier in oncological immunology. Elucidating these processes holds transformative potential for developing innovative immunotherapeutic strategies against EC.\u003c/p\u003e \u003cp\u003eThis study leverages gene expression datasets from the GEO and TCGA databases to characterize 22 immune cell subpopulations using the CIBERSORT algorithm, aiming to identify pivotal immune cells in EC. Transcriptome analysis of clinical specimens was performed via RNA sequencing (RNA-seq) to delineate the expression profiles, biological roles, and regulatory signaling pathways of signature genes associated with these key immune cells in EC. Concurrently, immunohistochemical (IHC) validation was performed to quantify the spatial distribution of key immune cells and assess their correlations with clinicopathological parameters.\u003c/p\u003e \u003cp\u003eBy integrating multi-omics, multi-layer, and multi-angle analytical approaches, this work provides a novel perspective for understanding the immune microenvironment of EC, offering a novel conceptual framework for discovering immune cell-derived biomarkers and advancing targeted immunotherapeutic strategies.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Identification of key immune cells via integrated analysis of GEO and TCGA databases\u003c/h2\u003e \u003cp\u003eTo delineate the heterogeneity of immune microenvironments in EC tissues and normal tissues, compositional analysis of 22 immune cell subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) revealed elevated proportions of activated dendritic cells, activated mast cells, and M0 macrophages in tumor tissues, alongside reduced frequencies of activated NK cells, resting mast cells, and na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells. Bar plots from the GSE106191 and TCGA-UCEC datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D) further illustrated distinct immune cell distribution patterns between groups.\u003c/p\u003e \u003cp\u003eImmune cell correlation heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) delineated a complex regulatory network between the subpopulations: resting mast cells exhibited positive correlations with na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.55) and activated NK cells (r\u0026thinsp;=\u0026thinsp;0.67), but negative correlations with activated mast cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.43) and plasma cells (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.44). Significant positive associations were observed between na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells and activated NK cells (r\u0026thinsp;=\u0026thinsp;0.60), follicular helper T cells and M1 macrophages (r\u0026thinsp;=\u0026thinsp;0.68), and CD8\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.60). These correlations suggest potential co-regulatory dynamics, where positive interactions imply synchronized abundance fluctuations, while negative correlations indicate antagonistic relationships.\u003c/p\u003e \u003cp\u003eIntegrated analysis of GSE106191 and TCGA-UCEC datasets demonstrated a significantly lower abundance of resting mast cells (GSE: P\u0026thinsp;=\u0026thinsp;0.007; TCGA: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a higher abundance of activated dendritic cells (GSE: P\u0026thinsp;=\u0026thinsp;0.035; TCGA: P\u0026thinsp;=\u0026thinsp;0.002) in EC tissues compared to normal tissues. TCGA data further identified 10 immune subsets enriched in EC, including M0 macrophages and inactivated mast cells (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F), suggesting their potential roles in pro-tumorigenic microenvironment remodeling.\u003c/p\u003e \u003cp\u003eBy cross-validating both datasets, we identified resting mast cells and activated dendritic cells as key immune cells due to their statistically significant differences in abundance in EC. However, given technical limitations in the experimental detection of cellular activation states, mast cells (MCs) and dendritic cells (DCs) were prioritized as critical immune populations for subsequent functional validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sequencing data quality control and differential expression analysis\u003c/h2\u003e \u003cp\u003eTranscriptome sequencing of 19 clinical specimens generated 123.09 Gb of high-quality raw data (Supplementary Table\u0026nbsp;1), with all samples meeting stringent quality thresholds: sequencing depth\u0026thinsp;\u0026ge;\u0026thinsp;6.03 Gb, Q30\u0026thinsp;\u0026gt;\u0026thinsp;93.88%, and GC content ranging from 46.23% to 53.11%. Post-quality control analysis identified 40,165 expressed genes and 191,742 transcripts. Differential expression analysis using DESeq2 (threshold: log2(fold change)\u0026thinsp;\u0026ge;\u0026thinsp;1, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed 5,998 differentially expressed genes (DEGs), comprising 3,455 upregulated and 2,543 downregulated genes. These findings underscore significant transcriptional heterogeneity between EC tissues and paired ANT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Signature gene expression and functional enrichment analysis of MCs\u003c/h2\u003e \u003cp\u003eAmong 17 MCs-associated signature genes, nine significantly differentially expressed genes (DEGs) were identified through sequencing data. Box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed the downregulation of CMA1, CTSG, ITGA9, ADAMTS3, CPA3, and EGR3 in EC tissues, while CPM, HSPA6, and S100A4 exhibited no significant expression changes.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis of MCs signature DEGs was performed using GO and KEGG databases. GO analysis highlighted enrichment in biological processes related to protein processing and maturation, angiotensin maturation, peptide hormone processing, and signal receptor ligand precursor processing. Cellular components were primarily localized to the extracellular matrix, extracellular region, vesicles, secretory granules, and exosomes. Molecular functions were enriched in peptidase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eKEGG pathway analysis demonstrated significant enrichment in the renin-angiotensin system, cell adhesion molecules, protein processing in the endoplasmic reticulum, neutrophil extracellular trap formation, estrogen signaling pathway, viral carcinogenesis, and endocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Signature gene expression and functional enrichment analysis of DCs\u003c/h2\u003e \u003cp\u003eAmong 35 signature genes related to DCs, nine differentially expressed genes (DEGs) were identified through sequencing data analysis. The boxplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) revealed that CAPG, CCNA1, and TNFAIP2 were significantly upregulated in EC tissues, while CD302, SIGLEC5, and SNURF were downregulated. In contrast, TNFSF14, SLAMF9, and UBD exhibited no significant differential expression.\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis of DEGs in DCs-associated genes demonstrated the following patterns: GO biological processes were predominantly enriched in positive regulation of T cell chemotaxis, regulation of T cell chemotaxis, myeloid dendritic cell differentiation, positive regulation of lymphocyte chemotaxis, and activation of myeloid dendritic cells. Cellular components were primarily associated with cyclin A1-CDK2 complex and cyclin A2-CDK2 complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eKEGG pathway analysis highlighted significant enrichment in viral carcinogenesis, human T-cell leukemia virus type 1 (HTLV-1) infection, transcriptional misregulation in cancer, cell cycle, cellular senescence, cytokine-cytokine receptor interaction, AMPK signaling pathway, progesterone-mediated oocyte maturation, pathways in cancer, and NF-κB signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immunohistochemical staining using CD117 and CD11c for MCs and DCs\u003c/h2\u003e \u003cp\u003eThe IHC results demonstrated that CD117-positive signals were localized in the cytoplasm of MCs, exhibiting characteristic brownish granular cytoplasmic staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). CD11c-positive signals were distributed on the cell membrane and cytoplasm of DCs, with distinct brownish-yellow positivity observed in 66% (33/50) of EC specimens. In contrast, ANT displayed only faint yellowish weak positivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Quantitative analysis revealed that the CD117-positive cell density in EC tissues was 12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15 cells per high-power field (HP), significantly lower than that in ANT (17.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83/HP, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the CD11c-positive cell density in EC tissues (4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22/HP) was markedly higher compared to ANT (1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19/HP, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential expression of CD117 and CD11c between EC tissues and ANT(x\u0026thinsp;\u0026plusmn;\u0026thinsp;s).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCD117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eCD11c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 The expression levels of CD117 and CD11c and their correlations with clinicopathological characteristics were analyzed.\u003c/h2\u003e \u003cp\u003eCD117-positive cell density demonstrated statistically significant differences concerning BMI, FIGO stage, and lymph node metastasis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, patients with advanced-stage (III- IV) disease exhibited a higher CD117-positive cell density (16.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36 cells/HP) compared to those with early-stage (I\u0026ndash;II) disease (11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82 cells/HP). Similarly, the CD117-positive cell density was elevated in patients with positive lymph node metastasis (16.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77 cells/HP) versus their negative counterparts (11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83 cells/HP). Notably, a BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 kg/m\u0026sup2; was associated with increased CD117-positive cell density (14.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.90 cells/HP) relative to a BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2; (8.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97 cells/HP). However, CD117 expression showed no significant correlation with patient age, depth of myometrial invasion, or menopausal status (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor CD11c-positive cell density, statistically significant differences were observed concerning FIGO stage and lymph node metastasis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Advanced-stage (III- IV) patients displayed higher CD11c-positive cell density (5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59 cells/HPF) compared to early-stage (I\u0026ndash;II) patients (3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50 cells/HPF). Similarly, the CD11c-positive cell density was significantly elevated in lymph node metastasis-positive patients (6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51 cells/HPF) versus negative patients (3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40 cells/HPF). In contrast, CD11c expression demonstrated no significant association with patient age, depth of myometrial invasion, BMI, or menopausal status (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between CD117, CD11c expression in EC tissues and clinicopathological features (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCD117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eCD11c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u0026thinsp;\u0026plusmn;\u0026thinsp;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ex\u0026thinsp;\u0026plusmn;\u0026thinsp;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII-VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(continued) Relationship between CD117, CD11c expression in EC tissues and clinicopathological features (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCD117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eCD11c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u0026thinsp;\u0026plusmn;\u0026thinsp;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ex\u0026thinsp;\u0026plusmn;\u0026thinsp;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMyometrial Invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.03\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.11\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study analyzed the composition, intercellular correlations, and relative abundance differences of immune cells in EC using gene expression data from GEO and TCGA databases, employing the CIBERSORT algorithm and R software. The findings revealed a dynamic subpopulation imbalance between MCs and DCs in EC tissues: resting MCs exhibited lower relative abundance than normal tissues, while activated DCs showed elevated abundance. Both cell types demonstrated specific regulatory associations with immune subpopulations, such as CD4⁺ T and plasma cells.\u003c/p\u003e \u003cp\u003eUnder physiological homeostasis, MCs primarily contribute to immune surveillance, inflammatory responses, and tissue repair\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, MCs display complex dual roles dynamically regulated by tumor type and progression stage. Tumor-associated MCs drive angiogenesis, stromal remodeling, and immune evasion by releasing pro-angiogenic factors, matrix metalloproteinases, and immunosuppressive cytokines\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Conversely, they may suppress tumor growth via cytotoxic granule release, DCs/T-cell immune activation, and secretion of anti-angiogenic mediators like prostaglandin D2. This functional heterogeneity is particularly pronounced in solid tumors\u0026mdash;for instance, MC infiltration correlates with both enhanced tumor invasiveness and improved prognosis in breast cancer. In contrast, in prostate cancer, MCs predominantly promote angiogenesis and reduce the risk of recurrence\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs pivotal antigen-presenting cells, DCs initiate adaptive immunity by activating anti-tumor T-cell responses\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, their functionality is frequently suppressed by TME-derived inhibitory factors and immunosuppressive cells, such as myeloid-derived suppressor cells, leading to immune tolerance\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Distinct DC subsets\u0026mdash;including conventional DC1 (cDC1), DC2 (cDC2), and plasmacytoid DCs (p-DCs)\u0026mdash;exhibit marked heterogeneity in tumor immunomodulation, with their roles finely regulated by tumor-specific signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the necessity of elucidating the mechanistic roles of MCs and DCs in EC progression, which may inform novel immunotherapeutic strategies targeting TME reprogramming, immune checkpoint modulation, or subset-specific functional enhancement.\u003c/p\u003e \u003cp\u003eThe study identified 17 MC signature genes, with nine significantly differentially expressed genes (DEGs) selected through sequencing data analysis. CMA1, CPA3, CTSG, ITGA9, ADAMTS3, and EGR3 exhibited downregulation in EC tissues, consistent with a reduced abundance of resting MCs. Functional enrichment analysis of MC-related DEGs revealed their predominant involvement in angiotensin maturation and extracellular matrix regulation via GO analysis. KEGG pathway analysis highlighted enrichment in the renin-angiotensin system and cell adhesion molecule pathways. Recent studies have demonstrated that localized RAS activation promotes EC cell proliferation via angiotensin II receptor type 1 (AGTR1)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, suggesting that MC-derived angiotensin-converting enzyme may serve as a therapeutic target\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMCs may influence TME and angiogenesis by regulating the expression of extracellular matrix components and Cell adhesion molecules. Shi et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e demonstrated that CMA1 is highly expressed in gastric cancer tissues and correlates with poor patient prognosis. In contrast, Xie et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003ereported reduced CMA1 expression in colorectal cancer (CRC), where resting MCs (characterized by low CMA1 expression) are diminished, while activated MCs (expressing elevated TPSAB1, CPA3, and KIT) are enriched. Activated MCs in CRC may exert antitumor effects through the KITLG-KIT axis. These findings suggest that CMA1 downregulation in EC could reflect a shift toward an activated MC phenotype, which may suppress tumor progression via cytokine secretion and immune cell activation. Chan et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e identified CTSG downregulation in CRC, where its low expression is associated with adverse clinical outcomes. Mechanistically, CTSG inhibits CRC cell proliferation and induces apoptosis by suppressing the Akt/mTOR/Bcl2 signaling pathway. Similarly, Hua et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003erevealed that CTSG downregulation in head and neck squamous cell carcinoma (HNSC) promotes tumor cell proliferation and migration via hyperactivation of the JAK2/STAT3 pathway. In EC, CTSG downregulation is hypothesized to modulate JAK2/STAT3 signaling, potentially reversing its tumor-suppressive effects on proliferation and migration. These insights position CTSG as a promising diagnostic biomarker and therapeutic target in EC.\u003c/p\u003e \u003cp\u003eOther MCs-associated genes, including CPA3, ITGA9, EGR3, and ADAMTS3, contribute to diverse oncogenic processes such as immune regulation, cell migration, tumor invasion, and tissue remodeling across malignancies\u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that MCs in EC may engage in immune surveillance, tumor suppression, and multifaceted biological regulation. However, the functional heterogeneity of MCs across tumor types\u0026mdash;driven by distinct TME dynamics and pathogenesis\u0026mdash;necessitates further experimental validation to elucidate their precise roles in EC progression.\u003c/p\u003e \u003cp\u003eThrough sequencing data analysis of 35 DCs signature genes, nine differentially expressed genes (DEGs) were identified. CAPG, CCNA1, and TNFAIP2 exhibited significantly elevated expression levels in EC tissues, consistent with the decreased abundance of activated DCs. Functional enrichment analysis of DCs signature gene DEGs and GO analysis demonstrated predominant involvement in biological processes such as regulating T cell chemotaxis and immune-related pathways. KEGG pathway analysis highlighted significant enrichment in critical signaling pathways, including cytokine-cytokine receptor interaction, AMPK signaling pathway, and NF-κB signaling pathway, suggesting their potential roles in modulating DCs activation and immune microenvironment dynamics.\u003c/p\u003e \u003cp\u003eDCs may influence the immune microenvironment of EC by modulating T-cell chemotaxis and cytokine secretion, potentially contributing to immune evasion mechanisms. Zhao et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003edemonstrated that CAPG knockdown suppresses colorectal cancer (CRC) cell proliferation by upregulating the P53 pathway while concurrently promoting apoptosis and ferroptosis. Long et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003efurther revealed that CAPG potentiates gastric cancer cell proliferation, migration, invasion, and metastasis via activation of the Wnt/β-catenin signaling pathway. CCNA1 (Cyclin A1), a critical regulator of the G1/S phase transition, facilitates cell cycle progression by activating CDK1/CDK2. Da Silva et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003eidentified CCNA1 overexpression in papillary thyroid carcinoma. At the same time, Jiang et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e established its pro-tumorigenic role in gastric cancer, where it promotes proliferation, cell cycle progression, and migration while inhibiting apoptosis, acting as a downstream transcriptional target of RNF6. TNFAIP2, a tumor necrosis factor α (TNF-α)-inducible protein, participates in inflammatory responses, apoptosis, angiogenesis, and tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Ren et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e elucidated that TNFAIP2 drives angiogenesis in triple-negative breast cancer through the Rac1-ERK-AP1-HIF1α signaling axis, with combinatorial inhibition of ERK (e.g., trametinib) and VEGFR (e.g., lapatinib) significantly suppressing tumor growth and vascularization in preclinical models.\u003c/p\u003e \u003cp\u003eThese findings suggest that DCs in EC may orchestrate tumor progression by regulating cell proliferation/apoptosis dynamics, cell cycle checkpoints, and angiogenic processes. However, validation through in vitro functional assays and in vivo animal models remains essential to substantiate these mechanistic hypotheses.\u003c/p\u003e \u003cp\u003eBased on preliminary insights into the characteristic gene expression and tumor-related functions of MCs and DCs, this study further employed IHC staining to analyze these two cell types' expression and spatial distribution in tissue samples while exploring their correlation with clinicopathological features.\u003c/p\u003e \u003cp\u003eThe IHC results demonstrated that CD117-positive cells exhibited a diffuse and heterogeneously distributed pattern within the EC stromal compartment, with lower density than adjacent non-cancerous tissue(ANT). This observation aligns with findings from Mao et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, who reported similar MCs distribution patterns in colorectal cancer, where normal mucosal tissues displayed higher MCs density than tumor regions. This consistency may reflect tumor microenvironment (TME) remodeling, where the physiological milieu of adjacent tissues potentially supports MCs' survival and proliferation. In contrast, intratumoral hypoxia, nutrient competition, and tumor-derived inhibitory factors (e.g., immunosuppressive cytokines) may suppress MCs recruitment and growth\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the elevated MCs presence in ANT might signify a host immune defense mechanism against tumorigenesis, mediated through cytokine release and bioactive molecules to inhibit tumor progression. However, this protective response appears to be attenuated within the tumor core during disease progression\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding CD11c-positive cells, distinct brown-yellow staining (indicative of strong positivity) was observed in the tumor stroma of 66% (33/50) of EC samples, whereas ANT exhibited only faint yellowish staining (weak positivity). Quantitative analysis revealed a significantly higher density of CD11c\u0026thinsp;+\u0026thinsp;cells in EC tissues compared to ANT samples. Notably, 34% (17/50) of EC samples showed no detectable staining, which could stem from multiple technical and biological factors. Insufficient antibody specificity might impede accurate antigen recognition, particularly given the heterogeneity in DCs' subset-specific surface markers. Suboptimal tissue fixation, inadequate antigen retrieval, or improper antibody dilution ratios may compromise staining efficacy. These findings underscore the need for protocol optimization in future studies, including validation of antibody specificity, standardization of antigen retrieval conditions, and incorporation of complementary markers to delineate DCs subsets precisely.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the expression of CD117 and CD11c in EC tissues and their correlation with clinicopathological characteristics. CD117\u0026thinsp;+\u0026thinsp;cell density is significantly associated with body mass index (BMI), FIGO stage, and lymph node metastasis (LNM). Specifically, higher CD117\u0026thinsp;+\u0026thinsp;cell density was observed in patients with BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 kg/m\u0026sup2;, advanced FIGO stages (III-IV), or LNM-positive status. This finding aligns with the study by Goba et al.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, which reported elevated CD117\u0026thinsp;+\u0026thinsp;cell density in normal prostate tissues compared to prostate cancer tissues. The inverse relationship between BMI and CD117\u0026thinsp;+\u0026thinsp;cell density may reflect altered immune-metabolic crosstalk in obesity, where dysfunctional adipose tissue releases pro-inflammatory cytokines, induces chronic low-grade inflammation, and disrupts insulin signaling pathways, thereby modulating MCs recruitment and activation.\u003c/p\u003e \u003cp\u003eRegarding CD11c\u0026thinsp;+\u0026thinsp;DCs, our data revealed that their density correlated positively with the advanced FIGO stage (III-IV) and LNM. However, conflicting evidence exists across malignancies. For instance, Lee et al.\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e demonstrated that high CD11c expression in triple-negative breast cancer was linked to higher histological grade but independent of pathological T stage, LNM, or lymphovascular invasion. Similarly, Minkov et al.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e observed CD11c\u0026thinsp;+\u0026thinsp;cell density association with tumor size (T stage) in non-small cell lung cancer. However, there is no correlation with LNM, distant metastasis, clinical stage, or tumor differentiation. These discrepancies underscore the context-dependent roles of DC subsets within distinct tumor microenvironments.\u003c/p\u003e \u003cp\u003eCurrent research on MCs and DCs in EC remains limited. While our study draws parallels from their roles in other malignancies (e.g., TNBC, gastrointestinal stromal tumors, and NSCLC), the observed inconsistencies in immune cell-clinicopathological correlations may stem from sample size limitations, tumor heterogeneity, or technical variability in immune cell quantification. Future investigations should employ advanced technologies such as single-cell RNA sequencing (scRNA-seq) or multiparametric flow cytometry to delineate MCs/DCs subpopulations precisely, map their spatial distribution, and characterize functional states to address these challenges. Such efforts will enhance our understanding of their mechanistic contributions to EC progression and refine precision immunotherapeutic strategies targeting these cells.\u003c/p\u003e \u003cp\u003eWhile this study has delineated the spatial distribution of MCs and DCs in EC and their potential mechanistic roles, critical gaps remain in understanding their functional states, spatial interactions, and dynamic remodeling within TME. Although bioinformatics analysis, transcriptomic sequencing, and IHC have been employed to explore the biological functions of MCs/DCs and their involvement in signaling pathways, the lack of single-cell resolution data limits insights into cellular heterogeneity and intercellular cross-talk. Future studies should incorporate functional assays and animal models to validate these findings and unravel the molecular regulatory networks driving EC progression. Furthermore, integrating EC molecular subtyping (e.g., POLE-mutated, microsatellite instability-high MSI-H, copy-number low/high) could refine the interpretation of immune cell dynamics. For instance, MSI-H tumors often exhibit heightened immune infiltration and responsiveness to immune checkpoint inhibitors, whereas copy-number high subtypes may display immunosuppressive TME features\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Stratifying MCs/DCs phenotypes and functional states across these subtypes would clarify their context-dependent roles and identify subtype-specific therapeutic vulnerabilities.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Acquisition of transcriptomic data from public databases\u003c/h2\u003e \u003cp\u003eTranscriptomic datasets for EC were retrieved from the Gene Expression Omnibus database (GEO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Cancer Genome Atlas database (TCGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GEO dataset GSE106191\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e contains 64 EC tumors and 33 normal tissues, while the TCGA-UCEC dataset comprises 554 EC tumors and 35 normal tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Collection of clinical samples\u003c/h2\u003e \u003cp\u003eTranscriptomic sequencing: Tissue specimens were collected from 13 EC patients undergoing hysterectomies at Dalian Maternal and Child Health Hospital from January 2023 to December 2024. Under sterile conditions, gynecologic surgeons and pathologists aseptically excised tumor samples (n\u0026thinsp;=\u0026thinsp;13) and paired adjacent non-tumorous tissue (ANT; n\u0026thinsp;=\u0026thinsp;6) samples, defined as histologically confirmed normal endometrium located more than 2 cm from the tumor margins\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Intraoperative specimens (5 \u0026times; 5 \u0026times; 2 mm\u0026sup3;) were immediately immersed in 1.5 mL RNA-store Stabilization Reagent and incubated at 4\u0026deg;C for 24 hours before being transferred to -80\u0026deg;C for long-term storage.\u003c/p\u003e \u003cp\u003eAn IHC cohort was established using 50 archived formalin-fixed paraffin-embedded (FFPE) EC tissues from the Department of Pathology (2022\u0026ndash;2024), with 50 tumor tissues as the experimental group and 15 pathologically confirmed ANT samples as controls. Written informed consent was obtained from all participants, and the study protocol (Ethics Approval: FEJT-KY-2024-201) was approved by the Institutional Ethics Committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Bioinformatics data processing\u003c/h2\u003e \u003cp\u003eTranscriptomic datasets GSE106191 (microarray) and TCGA-UCEC (RNA-seq) underwent systematic preprocessing using R software (version 4.3.1) to ensure analytical rigor. For GSE106191, probes with ambiguous genomic mapping were filtered to retain uniquely annotated gene targets, followed by cross-sample normalization to establish comparable expression baselines. The TCGA-UCEC dataset underwent gene-level deduplication, retaining entries with the highest transcripts per million (TPM) values. This pipeline generated expression matrices (GSE106191_normalized, TCGA-UCEC_TPM) suitable for downstream comparative analyses.\u003c/p\u003e \u003cp\u003eThe CIBERSORT deconvolution algorithm and the LM22 signature matrix (comprising 22 immune cell types) were applied to both preprocessed datasets for estimating immune cell fractions. A permutation analysis with 1,000 iterations was performed, and samples with a CIBERSORT P-value of less than 0.05 were retained to ensure analytical robustness.\u003c/p\u003e \u003cp\u003eHierarchical clustering heatmaps were generated to visualize immune cell compositional patterns, complemented by correlation matrices to elucidate intercellular interactions. Violin plots were utilized to systematically compare the distributions of immune cell abundance between EC and ANT. This integrated visualization approach delineates the spatial and temporal heterogeneity of key immune cell subsets, revealing their differential infiltration signatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Transcriptomic profiling of clinical samples\u003c/h2\u003e \u003cp\u003eRNA extraction and quality control\u003c/p\u003e \u003cp\u003eFrozen EC tumors and ANT samples were pulverized by liquid nitrogen grinding, followed by total RNA extraction using QIAzol Lysis Reagent (Qiagen). RNA concentration and purity (OD260/280 ratio: 1.8\u0026ndash;2.2) were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). RNA integrity was assessed via agarose gel electrophoresis (Biowest Agarose) and quantified using an Agilent 5300 Fragment Analyzer, with RNA quality numbers (RQN) greater than 6.5 required for downstream processing.\u003c/p\u003e \u003cp\u003eLibrary preparation and high-throughput sequencing\u003c/p\u003e \u003cp\u003eRNA samples that met the criteria for library construction (total RNA\u0026thinsp;\u0026ge;\u0026thinsp;1 \u0026micro;g and concentration\u0026thinsp;\u0026ge;\u0026thinsp;30 ng/\u0026micro;L) were processed using the Illumina\u0026reg; Stranded mRNA Prep kit. The workflow included the following steps: (1) mRNA enrichment through Oligo(dT) bead-based selection, (2) fragmentation and synthesis of double-stranded cDNA, (3) end repair and adapter ligation, and (4) PCR amplification. Purified libraries were quantified using a Qubit 4.0 fluorometer (Thermo Fisher Scientific) and amplified via bridge amplification on a c-Bot system. Sequencing was performed on an Illumina Nova-Seq X-Plus platform.\u003c/p\u003e \u003cp\u003eTranscriptome assembly and primary data analysis\u003c/p\u003e \u003cp\u003eRaw sequencing data underwent quality trimming to remove adapter sequences, low-quality bases (Phred score\u0026thinsp;\u0026lt;\u0026thinsp;20), reads with more than 10% ambiguous nucleotides (N), and short fragments (less than 20 bp). High-quality reads were aligned to the Homo sapiens reference genome (GRCh38, Ensembl release 105; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), achieving alignment rates of 95.73\u0026ndash;97.41%. Transcript-level expression quantification was performed using RSEM (RNA-Seq by Expectation-Maximization), and differential expression analysis was conducted with DESeq2, setting significance thresholds at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC|\u0026ge;1.\u003c/p\u003e \u003cp\u003eImmune signature gene screening and functional enrichment\u003c/p\u003e \u003cp\u003eImmune cell signature gene sets were retrieved from the Molecular Signatures Database (MSigDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and curated using R/Bioconductor packages. Key immune cell signatures were intersected with sequencing-derived differentially expressed genes (DEGs) to identify immune-related DEGs. These signature genes were subjected to Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis on the Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.majorbio.com\u003c/span\u003e\u003cspan address=\"https://cloud.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with false discovery rate (FDR) correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to elucidate their biological functions and regulatory networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Immunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eFormalin-fixed, paraffin-embedded (FFPE) EC tissues and paired ANT samples were sectioned at 3 \u0026micro;m thickness. Two slides per case were baked at 70\u0026deg;C for 3 hours. Immunohistochemical staining was performed on the BenchMark GX automated stainer. Deparaffinization was achieved using 10\u0026times; dewaxing buffer (75\u0026deg;C, 16 min), followed by high-temperature antigen retrieval with cell conditioning solution (95\u0026ndash;100\u0026deg;C, 64 min). Endogenous peroxidase activity was inhibited with OptiView Peroxidase Inhibitor (37\u0026deg;C, 4 min). Primary antibody incubation utilized rabbit anti-human CD11c polyclonal antibody (1:100 dilution) and CD117 monoclonal antibody (36\u0026deg;C, 32 min). Signal detection was sequentially performed using hapten-conjugated HQ Universal Linker, HRP Multimer (37\u0026deg;C, 8 min each), DAB chromogen with hydrogen peroxide (37\u0026deg;C, 8 min), and OptiView Copper enhancer (37\u0026deg;C, 4 min). Nuclear counterstaining was conducted with hematoxylin (37\u0026deg;C, 14 min), followed by bluing reagent application (37\u0026deg;C, 4 min). Sections were dehydrated, cleared, mounted, and microscopically analyzed for immunohistochemical evaluation.\u003c/p\u003e \u003cp\u003eQuantitative analysis\u003c/p\u003e \u003cp\u003ePositive immunoreactivity was defined as cytoplasmic or nuclear staining with chromogenic intensity graded as weak (light yellow), moderate (brownish-yellow), or strong (tan). Five non-overlapping high cellularity regions were selected under a low-power field (\u0026times;100 magnification), and positively stained cells were quantified in high-power fields (\u0026times;400 magnification). The mean value of positive cells across five fields was calculated. Two board-certified pathologists independently; a third senior pathologist adjudicated discrepancies and evaluated all slides, scoring 10% in positive cell percentages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Statistical methods\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS version 27.0 and GraphPad Prism version 9.5. Continuous variables that followed a normal distribution were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical data were presented as frequencies. Comparisons between groups were conducted using a two-tailed Student\u0026rsquo;s t-test, with statistical significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics\u003c/h2\u003e\n\u003cp\u003eAll public datasets were utilized in accordance with the database access policies and the original study's ethics approvals. This study was approved by the Ethics Committee of Dalian Women's and Children's Medical Centre (Group) (Approval No. FEJT-KY-2024-201). Our study adhered to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eFunding \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;This study was supported by the Applied Basic Research Program of Liaoning Province (Grant No. 2023JH2/101300096).\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;We acknowledge Majorbio (https://www.majorbio.com) for providing the bioinformatics analysis platform.\u003c/p\u003e\n\u003ch2\u003eData availability \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and its supplementary material files. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003ch2\u003eSupplementary data \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSupplementary Table 1 has been uploaded to the supplementary materials.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMengru Zhang wrote the main manuscript text， Mengru Zhang and Junhuan Wang prepared figures1-4. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrosbie, E. J. et al. \u003cem\u003eEndometrial Cancer[J] Lancet\u003c/em\u003e, \u003cb\u003e399\u003c/b\u003e(10333): 1412\u0026ndash;1428. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel, R. L. et al. Cancer statistics, 2023[J]. CA: A Cancer Journal for Clinicians, 73(1): 17\u0026ndash;48. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, K. H. \u0026amp; Broaddus, R. R. Endometrial Cancer[J]. \u003cem\u003eN. 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R. et al. \u003cem\u003eDostarlimab for Primary Advanced or Recurrent Endometrial Cancer[J]\u003c/em\u003e (N engl j Med, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Cannon, B. A. \u0026amp; Colombo, I. The evolving role of immune checkpoint inhibitors in cervical and endometrial Cancer[J/OL]. \u003cem\u003eCancer Drug Resist.\u003c/em\u003e, (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, A., Guo, H. \u0026amp; Long, Z. Integrative Analysis of Differently Expressed Genes Reveals a 17-Gene Prognosis Signature for Endometrial Carcinoma[J]. \u003cem\u003eBiomed. Res. Int.\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e (1), 4804694 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroester, M. A. et al. DNA defects, epigenetics, and gene expression in cancer-adjacent breast: A study from The Cancer Genome Atlas[J]. \u003cem\u003eNpj Breast Cancer\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e (1), 16007 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEthics.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAll public datasets. were utilized in accordance with the database access policies and the original study's ethics approvals. This study was approved by the Ethics Committee of Dalian Women's and Children's Medical Centre (Group) (Approval No. FEJT-KY-2024-201). Our study adhered to the principles outlined in the Declaration of Helsinki.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial cancer, CIBERSORT algorithm, Mast cells, Dendritic cells, Tumor microenvironment, Immunohistochemistry","lastPublishedDoi":"10.21203/rs.3.rs-7470090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7470090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eIdentify key immune cells in endometrial cancer (EC) using bioinformatics and validate findings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm analyzed 22 immune cell subtypes based on GEO and TCGA datasets. Transcriptomics was performed on 13 EC tumors and 6 adjacent non-tumorous tissues (ANT). Immunohistochemistry (IHC) was conducted on 50 EC and 15 ANT samples, correlating with clinical features.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMast cells (MCs) and dendritic cells (DCs) were dysregulated. Transcriptomics identified nine signature genes differentially expressed for MCs and DCs. MC-specific genes (e.g., CMA1, CTSG, CPA3) were downregulated in EC, enriched in secretory granule function and pathways like renin-angiotensin system. DC-associated genes (e.g., CAPG, CCNA1, TNFAIP2) were upregulated, enriched in chemotaxis and cytokine interactions. IHC confirmed significantly reduced MC marker CD117 in EC, correlating with higher BMI, advanced FIGO stage, and metastasis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, DC marker CD11c was elevated, associated with advanced stage and metastasis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings identify MCs and DCs as pivotal immune cells in endometrial carcinoma, with MC suppression and DC-driven pro-tumorigenic activity showing significant correlations with advanced clinicopathological features. These immune subsets and their associated signature genes may serve as prognostic biomarkers and therapeutic targets for remodeling the EC microenvironment.\u003c/p\u003e","manuscriptTitle":"Identification and Clinical Validation of Key Immune Cells in Endometrial Cancer: A Focus on Mast Cells and Dendritic Cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 12:20:28","doi":"10.21203/rs.3.rs-7470090/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"902b2875-d655-4eb8-b039-1f8cf261dfe1","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61328250,"name":"Health sciences/Biomarkers"},{"id":61328251,"name":"Biological sciences/Cancer"},{"id":61328252,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61328253,"name":"Biological sciences/Immunology"},{"id":61328254,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-05T10:10:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 12:20:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7470090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7470090","identity":"rs-7470090","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}