Expression and prognostic value of EGF-like domain multiple 6 in uterine corpus endometrial carcinoma and its correlation with immune cell infiltration

Expression and prognostic value of EGF-like domain multiple 6 in uterine corpus endometrial carcinoma and its correlation with immune cell infiltration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Expression and prognostic value of EGF-like domain multiple 6 in uterine corpus endometrial carcinoma and its correlation with immune cell infiltration Qiannan Li, Nuerbiya Muheteer, Gulibositan Ayoufu, Xieerwaniguli Abulimiti, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5962280/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Uterine corpus endometrial carcinoma (UCEC) represents a common malignancy affecting the female reproductive tract, distinguished by elevated rates of both morbidity and mortality. This study investigates EGF-like domain multiple 6 (EGFL6) expression as a potential diagnostic and prognostic biomarker in UCEC. RNA sequencing data along with clinical information of UCEC patients were sourced from The Cancer Genome Atlas (TCGA). Logistic regression analysis assessed correlations between EGFL6 expression and clinical features. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate EGFL6-related biological characteristics and signaling pathways. Immunohistochemistry (IHC) was performed on UCEC tissue microarrays to assess EGFL6 expression levels. Kaplan-Meier method and Cox regression analyses evaluated the prognostic significance of EGFL6. Three nomograms were developed to predict overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) probabilities at one, three, and five years following diagnosis. The expression of EGFL6 was validated through quantitative real-time PCR (RT-qPCR). Our findings demonstrate that EGFL6 is markedly overexpressed in UCEC and is associated with unfavorable clinical characteristics, including clinical stage and histological type. GO/KEGG and GSEA analyses highlighted its possible involvement in critical signaling pathways, such as MAPK and Ras signaling. Additionally, EGFL6 expression was associated with decreased immune cell infiltration, particularly regulatory T cells (Tregs) (r = -0.219, p < 0.001) and CD8 + T cells (r = -0.217, p < 0.001). Overall, EGFL6 expression is linked to poor patient prognosis. In conclusion, EGFL6 presents itself as a potential biomarker for UCEC, which could greatly influence the improvement of diagnostic and prognostic evaluations. Health sciences/Biomarkers Health sciences/Oncology EGF-like Domain Multiple 6 Biomarker Prognosis Immune Infiltration Uterine Corpus Endometrial Carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Uterine corpus endometrial carcinoma (UCEC) is one of the most common cancers impacting the female reproductive system, presenting considerable health threats to women as a result of its elevated rates of morbidity and mortality 1 . According to global cancer statistics in 2022, UCEC ranks sixth among cancers in women 2 . In China, the incidence of UCEC is also notably high, typically ranking between eighth and tenth among chinese women 3 . This estrogen-dependent malignancy poses disproportionate threats due to its accelerating incidence rates (3.1% annual increase since 2010) coupled with established risk factors including advanced age, hypertension, metabolic disorders (obesity, diabetes), hormonal imbalance (unopposed estrogen), and hereditary cancer syndromes (Lynch syndrome) 4 – 6 . Current diagnostic and therapeutic strategies for UCEC, which encompass imaging techniques, pathological diagnosis, surgical interventions, and chemotherapy, are often hampered by challenges such as late-stage diagnosis and heterogeneous treatment responses 7 , 8 . This clinical landscape underscores the urgent need for molecular biomarkers that are capable of addressing two critical gaps: (1) early detection in pre-symptomatic phases, and (2) personalized prognostic stratification beyond traditional clinicopathologic parameters 9 , 10 . Recent studies have underscored EGFL6`s potential as a cancer biomarker, particularly regarding its involvement in tumorigenesis and metastasis 11 . Notably, EGFL6 is highly expressed across multiple cancer types, such as ovarian cancer (OC), breast cancer (BC), lung adenocarcinoma (LUAD), colorectal cancer (CRC), and hepatocellular carcinoma (HCC) 12 – 15 . This indicates that EGFL6 could be pivotal in the molecular mechanisms that govern these malignancies 16 . While prior research has demonstrated a correlation between EGFL6 expression levels and endometrial cancer progression 17 . However, the comprehensive characterization of EGFL6, including its prognostic significance, methylation and mutation profiles, as well as its associations with immune cell infiltrations in UCEC, remains incomplete. This knowledge gap highlights the need for further investigation to fully understand the potential of EGFL6`s dual role as a diagnostic biomarker and therapeutic target in UCEC. To address this critical gap, this study employs rigorous bioinformatics methods, utilizing RNA-seq data from TCGA with experimental validation through IHC on tissue microarrays and RT-qPCR to systematically evaluate EGFL6 expression patterns in UCEC. The research focuses on elucidating EGFL6`s potential as both a diagnostic and prognostic biomarker in UCEC, with parallel exploration of its correlation with immune cell infiltration patterns in tumor microenvironment. These investigations aims to unravel the molecular mechanisms driving UCEC progression. The findings may contribute to future studies validating EGFL6`s clinical utility as both a biomarker and therapeutic target, potentially improving patient management and outcomes in UCEC through enhanced understanding of disease pathogenesis and immune interactions. Materials and Methods Data collection and preprocessing RNA sequencing (RNA-seq) data, accompanied by clinical details pertaining to patients diagnosed with UCEC, were sourced from the TCGA database ( https://portal.gdc.cancer.gov/ ), ensuring the utilization of high-quality and meticulously curated datasets. The dataset comprised 554 tumor samples, 35 adjacent non-cancerous samples and 23 paired normal-tumor samples. To enable comparative analyses and enhance the reliability of subsequent findings, the level 3 HTSeq-FPKM data were transformed into transcripts per million (TPM) for normalization purposes, thereby allowing for more precise comparisons across the samples. Tissue microarray The tissue microarray employed in this research was obtained from Weiao (Shanghai) Biological Co., Ltd. (catalogue number: ZL-UteS961). Ethical approval was granted by the ethics committee of Weiao (Shanghai) Biological Co., Ltd. (reference number Csya2024056). This array included 48 UCEC specimens and 48 adjacent non-cancerous tissues, facilitating an in-depth assessment of EGFL6 expression within various cancer stages. As this investigation was retrospective and involved the analysis of patient medical records and tumor samples, the requirement for individual informed consent was waived by the ethics committee. Approval was granted on the basis that no identifiable patient information was used in the study. Furthermore, all procedures undertaken in this research adhered strictly to the principles outlined in the Declaration of Helsinki. Immunohistochemistry procedures Tumor specimens and their corresponding adjacent tissues were fixed in 10% formalin solution, embedded in paraffin, and sectioned into 4–6 µm thick slices. These sections were affixed to glass slides. After deparaffinization and rehydration, antigen retrieval was performed using microwave treatment in a citrate buffer (pH 6.0), a critical step for optimizing antibody binding. The sections were incubated overnight at a temperature of 4°C with a primary anti-EGFL6 antibody (Bioss, cat. no. bs-13062R) diluted 1:100. Subsequently, the sections were incubated with a secondary antibody at room temperature for 30 minutes. Finally, DAB substrate staining and hematoxylin counterstaining were performed to visualize EGFL6 expression in the tissue microarray. Assessment of immunohistochemical staining Two independent pathologists, blinded to the study evaluated the pathological slides based on the criteria outlined in the most recent literature, specifically the 2022 WHO classification of female genital tumors 18 . Staining intensity was categorized as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the proportion of positive cells was scored as 0 (0%), 1 ( 50%). The final IHC score (range:0–12) was calculated by multiplying the intensity and proportion scores. To validate the findings and assess inter-observer agreement, Cohen's kappa test was employed. Analysis of differentially expressed genes According to TCGA-UCEC data, patients with UCEC were categorized into high-expression and low-expression groups based on the median expression level of EGFL6. This stratification enabled a clearer distinction in biological responses. The identification of differentially expressed genes (DEGs) between these two classifications was performed employing the R package DESeq2, with statistical significance determined by an adjusted p-value of less than 0.05 and a |log2-fold-change (FC)| exceeding 2. Following this, the top ten DEGs underwent correlation analysis with EGFL6 expression through Spearman correlation analysis, thereby providing insights into the potential pathways influenced by EGFL6. Subsequently, Spearman correlation analysis was performed to assess the association between EGFL6 expression and the top ten DEGs, revealing potential pathways modulated by EGFL6. Functional enrichment analysis The functional enrichment analysis of DEGs was conducted by employing GO/KEGG methodologies, utilizing the R package GOplot (version 1.0.2) as outlined by Walter et al.(2015). The primary objective of these analyses were to clarify the biological roles and pathways related with EGFL6. Additionally, GSEA was performed using the R package clusterProfiler, where an adjusted p-value less than 0.05 and a false discovery rate (FDR) below 0.25 were deemed statistically significant for identifying enriched functions or pathways. This methodology yielded crucial insights into the molecular mechanisms underlying the progression of UCEC. Analysis of immune infiltration The assessment of immune infiltration encompassed 22 unique immune cell types, with relative enrichment scores derived from CIBERSORT analysis 19 . To explore the relationship between EGFL6 expression and different immune cell types, a Spearman correlation analysis was performed. The Wilcoxon rank-sum test was utilized to examine the disparities in immune infiltration levels between groups with high and low EGFL6 expression, thereby offering valuable insights into the immunological environment associated with EGFL6 expression in UCEC. Survival analysis Survival analysis was conducted utilizing the Kaplan-Meier technique alongside the log-rank test, with the threshold established at the median expression level of EGFL6. Both univariate and multivariate Cox regression analyses were carried out to evaluate the impact of clinical factors on patient survival outcomes. Clinical variables that demonstrated prognostic relevance, indicated by a p-value of less than 0.1 in the univariate analysis, were incorporated into the multivariate Cox regression model. The findings were illustrated using forest plots created with the R package ggplot2, which facilitated an in-depth understanding of survival results. Statistical analysis All statistical evaluations were performed utilizing R software (version 3.6.3). To determine the statistical significance of EGFL6 expression in both unpaired and paired tissue samples, the Wilcoxon rank-sum test and paired t-test were utilized, respectively. Furthermore, logistic regression was employed in conjunction with the Wilcoxon rank-sum test to explore the association between clinical characteristics and EGFL6 expression. All statistical tests were conducted as two-sided, with p-values less than 0.05 deemed statistically significant, thus reinforcing the reliability of the findings. Validation In order to confirm the results obtained from RNA-seq and immunohistochemical analyses, quantitative real-time PCR (RT-qPCR) was performed on a separate cohort consisting of 20 UCEC tissue samples alongside their corresponding adjacent non-malignant tissues. Total RNA was isolated utilizing TRIzol reagent (Invitrogen) and subsequently converted into complementary DNA (cDNA) using the PrimeScript RT reagent kit (Takara Bio, Japan). The RT-qPCR assays were conducted employing SYBR Green Master Mix (Takara Bio) on a QuantStudio 3 Real-Time PCR System. The expression levels of EGFL6 were standardized against GAPDH, and the relative expression was determined employing the 2^−ΔΔCt methodology. Statistical evaluations were carried out using paired t-tests to analyze the differences in EGFL6 expression levels between the tumor tissues and their adjacent non-cancerous counterparts. Results EGFL6 expression in uterine corpus endometrial carcinoma, and validation via IHC. We conducted a pan-cancer analysis of EGFL6 in various cancer types using TIMER2.0 database ( http://timer.cistrome.org/ ), TCGA datasets and 16 paired cancer types in TCGA. The results indicate that EGFL6 is significantly overexpressed in various cancer types, including UCEC (Supplementary Fig. 1) . To understand the expression level of EGFL6 in UCEC, we analyzed EGFL6 expression levels in TCGA-UCEC data. Our analysis indicate that EGFL6 is significantly overexpressed in UCEC ( Fig. 1 A ) . We further investigated the expression levels of EGFL6 in 23 paired UCEC samples ( Fig. 1 B ) , which yielded similar findings. The receiver operating curve (ROC) for EGFL6 in UCEC revealed an area under the curve (AUC) of 0.679 (95% CI: 0.586–0.772), suggesting a moderate level of diagnostic precision for EGFL6 in UCEC ( Fig. 1 C ) . We then conducted IHC assay in UCEC tissue microarray (Supplementary Fig. 2) . Our analysis demonstrated that EGFL6 exhibited a predominant localization in both the cell membrane and cytoplasm, with particularly notable differences in expression observed in the cervical glandular epithelium between adjacent non-cancerous and cancer tissues ( Fig. 1 D-F ) . Collectively, these results suggest that EGFL6 is markedly overexpressed in UCEC and may contribute to tumor-specific pathological changes. EGFL6 expression is significantly correlated with various clinical features in UCEC patients. Utilizing the median expression level of EGFL6 in UCEC, we categorized the patients into two distinct groups: those with high EGFL6 expression and those with low EGFL6 expression ( Table 1 ) . Our analysis indicated close associations between EGFL6 expression with key patient characteristics, including clinical stage (I-IV), age (< 60 vs. ≥60 years), weight (< 80 vs. ≥80 kg), menopause status (Post vs. Pre&Peri), histological grading (G2&G3 vs. G1) and histological subtypes (Endometrioid vs.Mixed&Serous) ( Fig. 2 A-F ). Additionally, our Logistic regression analysis yielded similar results ( Table 2 ) . These findings suggest that EGFL6 expression is closely linked to multiple clinical features in UCEC patients with potential prognostic implications. Table 1 EGFL6 expression in UCEC. Characteristics EGFL6-Low EGFL6-High P n 277 277 Age, n (%) < 0.001 a* 60 years 149 (27.0%) 195 (35.4%) Clinical stage, n (%) 0.043 a* Stage I 187 (33.8%) 156 (28.2%) Stage II 25 (4.5%) 27 (4.9%) Stage III 53 (9.6%) 77 (13.9%) Stage IV 12 (2.2%) 17 (3.1%) Weight, n (%) 0.038 a* 80 157 (29.6%) 130 (24.5%) Height, n (%) 0.170 a 160 133 (25.3%) 145 (27.6%) BMI, n (%) 0.298 a 30 163 (31.3%) 146 (28%) Histological type, n (%) < 0.001 a* Endometrioid 243 (43.9%) 169 (30.5%) Serous 25 (4.5%) 93 (16.8%) Mixed 9 (1.6%) 15 (2.7%) Residual tumor, n (%) 0.816 a R0 197 (47.5%) 180 (43.4%) R1 10 (2.4%) 12 (2.9%) R2 8 (1.9%) 8 (1.9%) Tumor invasion(%), n (%) 0.480 a = 50 110 (23.1%) 105 (22.1%) Histologic grading, n (%) < 0.001 a* G1 62 (11.4%) 37 (6.8%) G2 81 (14.9%) 40 (7.4%) G3 130 (23.9%) 193 (35.5%) Menopause status, n (%) 0.097 a Pre 22 (4.3%) 13 (2.6%) Peri 11 (2.2%) 6 (1.2%) Post 217 (42.8%) 238 (46.9%) Diabetes, n (%) 0.695 a No 166 (36.6%) 163 (36%) Yes 60 (13.2%) 64 (14.1%) Hormones therapy, n (%) 0.830 a No 154 (44.5%) 145 (41.9%) Yes 25 (7.2%) 22 (6.4%) Radiation therapy, n (%) 0.894 a No 141 (26.7%) 140 (26.5%) Yes 123 (23.3%) 125 (23.6%) a Represents Chisq test. * Statistically significant difference (P < 0.05). * Statistically significant difference (P < 0.05). Differentially expressed gene analysis of EGFL6 in UCEC. In order to explore the biological phenotypes and signaling pathways related to EGFL6, a differential gene expression (DEG) analysis was conducted. Our analysis revealed 156 genes that were up-regulated and 129 that were down-regulated ( Fig. 3 A ) . Among these, the top ten most DEGs included DEFA6, DEFA5, MT4, OBP2B, GAGE2A, CACNA1S, SOX11, KRTAP3-3, CST1 and TPH1 ( Fig. 3 B ) . GO/KEGG analysis demonstrated that the DEGs were primarily concentrated in biological processes (BP) including epidermis development, keratinocyte differentiation and keratinization. In terms of cellular components (CC), the DEGs were associated with intermediate filament cytoskeleton, intermediate filament and nuclear envelope. In the context of molecular functions (MF), we observed a significant enrichment in activities related to signaling receptor activation, receptor-ligand interactions, and growth factor functionality. Our KEGG analysis revealed a significant enrichment of various signaling pathways, notably the MAPK/Ras signaling pathways and pathways associated with staphylococcus aureus infection ( Fig. 3 C ) . To further elucidate EGFL6-related pathways, we conducted Gene Set Enrichment Analysis (GSEA). The findings indicated potential enrichment in pathways involving chromosome segregation, the organization of chromosomes pertinent to the meiotic cell cycle, GABAergic synapse, ATP-dependent activity acting on DNA and GABA-gated chloride ion channel activity ( Fig. 3 D ) . Collectively, our analyses suggest that EGFL6 expression in UCEC may be associated with certain signaling pathways and biological regulatory mechanisms. Table 2 Logistic regression analysis of EGFL6 in UCEC. Characteristics Total (N) OR (95% CI) P Age (> 60 vs. <= 60) 551 2.036 (1.433–2.893) 80 vs. 160 vs. 30 vs. <= 30) 521 0.831 (0.585–1.179) 0.298 Histological type (Endometrioid vs. Mixed&Serous) 554 0.219 (0.142–0.337) < 0.001 * Histologic grade (G2&G3 vs. G1) 543 1.850 (1.183–2.895) 0.007 * Residual tumor (R1&R2 vs. R0) 415 1.216 (0.623–2.372) 0.566 Menopause status (Post vs. Pre&Peri) 507 1.905 (1.052–3.449) 0.033 * Tumor invasion(%) ( > = 50 vs. < 50) 476 1.139 (0.793–1.635) 0.481 Diabetes (Yes vs. No) 453 1.086 (0.719–1.642) 0.695 Radiation therapy (Yes vs. No) 529 1.024 (0.727–1.440) 0.894 Hormones therapy (Yes vs. No) 346 0.935 (0.505–1.731) 0.830 Methylation and mutation status of EGFL6 in UCEC. We performed a comprehensive analysis of EGFL6 methylation utilizing the MethSurv database ( http://biit.cs./methsurv ) in the context of UCEC 20 . Our analysis identified nine methylation status in the promoter region of EGFL6 ( Fig. 4 A ) . Among these sites, cg07810164, cg00932276, cg12817924, cg26310256, cg23083672, and cg1246113 showed association with poorer patient prognosis ( Fig. 4 B-G ) . Additionally, we examined the mutation status of EGFL6 in UCEC using the cBioportal database ( http://www.cbioportal.org ). Our analysis revealed a mutation rate of 3% EGFL6 in UCEC (Supplementary Fig. 3A) . However, we found no significant correlations between EGFL6 mutation status and UCEC patient survival outcomes including OS, DSS, DFS, or PFI (Supplementary Fig. 3B-E) . These observations suggest that while EGFL6 methylation patterns may potentially influence UCEC progression, the clinical relevance of its genetic mutations requires further investigation to establish definitive biological significance. EGFL6 expression correlates with immune cell infiltration patterns in UCEC. We performed an analytical assessment to examine the relationship between EGFL6 expression and immune cell infiltration in UCEC ( Fig. 5 A ) . Our findings indicated negative correlations between EGFL6 expression and the presence of regulatory T cells, CD8 + T cells, and resting dendritic cells ( Fig. 5 B-D ) . Additionally, the correlation analysis indicated notable negative associations between EGFL6 expression and these specific immune cell populations ( Fig. 5 E-G ). Conversely, we identified positive correlations between EGFL6 expression and activated dendritic cells, CD4 memory resting T cells and M2 macrophages (Supplementary Fig. 4) . These results collectively imply a potential role for EGFL6 in shaping an immunosuppressive tumor microenvironment within the context of UCEC. EGFL6 expression correlates with poor survival outcomes in UCEC and pan-cancer cohorts. We undertook a Kaplan-Meier analysis to assess the correlation between EGFL6 expression levels and prognosis in UCEC patients. Our analysis demonstrated that elevated EGFL6 expression was significantly linked to decreased overall survival (p = 0.009), reduced disease-specific survival (p = 0.002) and poor progression free interval (p = 0.018) ( Fig. 6 A-C ) . Additionally, we performed survival analysis of EGFL6 across various cancer types. Our results revealed a strong association between high expression of EGFL6 and poor survival between bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), kidney renal papillary cell carcinoma (KIRP), and pancreatic adenocarcinoma (PAAD) (Supplementary Fig. 5A-C) . Furthermore, we conducted ROC analysis for these cancer types, revealing that KIRC, KIRP and LIHC indicated AUC scores greater than 0.8 (Supplementary Fig. 5D-I) . Collectively, these findings imply that high EGFL6 expression are linked with unfavorable survival outcomes in UCEC and various other cancer types. Construction and validation of three prognostic nomograms We performed both univariate and multivariate Cox regression analyses to predict the prognostic value of EGFL6 in UCEC (Supplementary Tables 1a-c) . To enhance the accuracy of our prediction for UCEC patients, we constructed prognostic nomograms that included independent predictors which showed statistical significance in the Cox univariate analysis. These nomograms serving as visual tools, with elevated scores indicating a worse prognosis, thereby assisting in individualized risk evaluation. The predictive accuracy of the nomogram for OS was thoroughly assessed using calibration curves ( Fig. 7 A-B ) , demonstrating its dependability in estimating survival probabilities. Additionally, the nomograms for DSS and PFI, along with their respective calibration curves, are depicted ( Fig. 7 C-F ) . The bootstrap resampling consistency index (C-index) for the nomograms were computed, resulting in a C-index score of 0.785 (95% Confidence Interval [CI]: 0.755–0.815) for OS, 0.880 (95% CI: 0.857–0.903) for DSS, and 0.716 (95% CI: 0.687–0.746) for PFI. To confirm the predictive accuracy of these three nomograms, we constructed ROCs for the nomograms in the categories of OS, DSS and PFI in 1, 3, 5 year intervals ( Fig. 7 G-I ) . These findings reflect a moderate accuracy level in forecasting OS, DSS, and PFI among UCEC patients. These results suggest potential utility for personalized risk stratification in UCEC management, though prospective multicenter studies are needed to confirm generalizability. The expression of EGFL6 is significantly linked to unfavorable outcomes in UCEC subtypes. Additionally, the relationships between EGFL6 expression levels and patient survival across various subtypes of UCEC were tested. Our findings indicated that elevated EGFL6 expression correlates with unfavorable prognosis outcomes in multiple UCEC subtypes, including age, histological grade, body mass index (BMI), primary therapy outcomes:(partial response (PR) and complete response (CR), tumor invasion (≤ 50%), radiation therapy, diabetes, hormone therapy (no), and menopause status (postmenopausal) ( Fig. 8 A-I ) . These observations imply that EGFL6 may serve as a valuable prognostic biomarker across diverse UCEC subtypes. RT-qPCR analysis The RT-qPCR analysis confirmed that EGFL6 is overexpressed in UCEC tissues when compared to non-cancerous tissues, which is consistent with our results from RNA-seq and IHC. Specifically, EGFL6 expression was notably elevated in UCEC samples (mean ± SD: 3.54 ± 1.01), in contrast to non-cancerous samples (mean ± SD: 1.09 ± 0.45, p < 0.001). Furthermore, a strong association was identified between the EGFL6 expression obtained from RT-qPCR and those derived from RNA-seq (r = 0.75, p < 0.001), thereby reinforcing the credibility of the transcriptomic analysis. Discussion Uterine corpus endometrial carcinoma (UCEC) remains the most prevalent malignancy of the female reproductive system, predominantly affecting perimenopausal and postmenopausal women 21 . Globally, UCEC ranks among the top ten female cancers, with particularly high incidence rates in developed regions 22 , 23 . The disease exhibits significant histological diversity, encompassing endometrioid and more aggressive non-endometrioid subtypes (e.g., serous and clear cell carcinomas), each demonstrating distinct molecular profiles and clinical behaviors 24 , 25 . Recent advances in molecular profiling have identified several promising biomarkers that may improve diagnostic accuracy and prognostic stratification, offering potential pathways for personalized treatment strategies in UCEC patients 26 . For instance, the levels of mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, and PMS2) serve as indicators of microsatellite instability (MSI) and are indicators of positive responses to immunotherapeutic interventions 27 . Furthermore, overexpression of the oncogene HER2 is linked to a more severe form of the disease and can inform targeted therapeutic approaches 28 . These biomarkers enhance our understanding of UCEC pathogenesis and offer potential for better patient stratification and treatment optimization. Current treatment strategies, including imaging, surgery, and chemotherapy, often face challenges due to late-stage diagnoses and heterogeneous treatment responses 29 . This situation underscores the necessity for identifying new biomarkers that can facilitate early detection and improve prognostic accuracy, ultimately leading to enhanced patient outcomes. In this investigation, we focused on the expression of EGFL6 as a potential biomarker in UCEC. Previous research has established that EGFL6 is involved in various cancers, underscoring its importance in tumor biology and its prospective utility as both a diagnostic and prognostic marker for UCEC 30 . By integrating RNA-seq data from TCGA, with IHC experiment on UCEC tissue microarray and RT-qPCR data, we conducted extensive statistical analyses to explore the association between EGFL6 expression levels and clinical characteristics of UCEC patients. These results demonstrate a significant correlation between elevated EGFL6 expression and adverse clinical features, such as advanced tumor stage, highlighting its relevance in patient stratification and clinical decision-making. This overexpression aligns with findings in multiple malignancies, where EGFL6 has been associated with tumor growth and metastasis through pathways such as MAPK and PI3K/Akt signaling 31 , 32 . Our GO/KEGG analyses, along with GSEA analysis, suggest a significant association between elevated EGFL6 expression and the MAPK/Ras signaling pathways. However, this correlation requires experimental validation (e.g., Western blot analysis of pathway proteins) to establish causality. Aberrant methylation and mutation of key genes are recognized as significant factors contributing to the development and progression of UCEC 33 , 34 . In this study, we analyzed the EGFL6 gene, assessing its methylation and mutation status to elucidate its potential impact on UCEC. Our methylation analysis using the MethSurv database identified nine methylation sites in the EGFL6 promoter region, with six sites (cg07810164, cg00932276, cg12817924, cg26310256, cg23083672, and cg1246113) showing association with poor prognosis. These methylation changes may induce transcriptional silencing of EGFL6, potentially disrupting normal cellular processes and promoting tumorigenesis. Furthermore, investigation of EGFL6 mutation status via the cBioPortal database revealed a 3% mutation frequency in UCEC. Given the limited mutation frequency and sample size, this exploratory analysis found no significant correlations between EGFL6 mutations and poor prognosis among UCEC patients. These results indicate that while the methylation status of EGFL6 may be a significant factor in UCEC progression and patient survival, mutations in this gene may not have the same prognostic implications. The tumor immune microenvironment (TIME) critically regulates cancer development and treatment efficacy 35 . Our CIBERSORT-based analysis revealed that elevated levels of EGFL6 are associated with diminished infiltration of pivotal immune cell populations, including cytotoxic CD8 + T cells and immunosuppressive regulatory T cells (Tregs). Notably, Supplementary Fig. 4 further demonstrates a positive correlation between EGFL6 expression and M2 macrophages, which typically exhibit immunosuppressive functions and are linked to poor prognosis 36 . This dual role of EGFL6 in modulating both effector and suppressive immune populations may contribute to tumor immune evasion. Further explorations of EGFL6`s interaction with immune checkpoint molecules may advance immunotherapeutic strategies for UCEC. Understanding EGFL6-mediated immune modulation could also guide combination therapies to enhance treatment efficacy and improved patient outcomes 37 . Based on prior evidence that EGFL6 binds to integrin receptors to activate downstream signaling in other cancers, we hypothesize that similar receptor-mediated interactions may underlie its regulation of MAPK/Ras pathways and infiltration of various immune cells in UCEC. Lastly, the prognostic significance of EGFL6 was evident in our Kaplan-Meier analyses, indicating that high expression levels were correlated with significantly reduced patient survival. This finding underscores EGFL6 as an independent prognostic marker, which could enhance existing prognostic models for UCEC and facilitate the development of personalized treatment approaches. By integrating EGFL6 expression into clinical practice, healthcare providers may be able to implement more tailored therapeutic interventions, ultimately improving patient outcomes. Future research should prioritize the confirmation of these results in more extensive cohorts while also investigating the mechanistic pathways through which EGFL6 influences tumor biology in UCEC. The limitations of this study must be acknowledged, as they may influence the interpretation of our findings. Firstly, the absence of in vitro and in vivo experimental validation restricts our capacity to confirm the biological significance of the observed EGFL6-related mechanisms. Additionally, the limited sample size, particularly the limited number of paired UCEC samples could affect the reliability and applicability of our analyses. Furthermore, while we explored various clinical features associated with EGFL6 expression, the absence of longitudinal data may hinder our understanding of the dynamic nature of EGFL6's prognostic value over time. These factors underscore the necessity for further validation studies, including larger cohort analyses and experimental investigations, to substantiate the clinical relevance of EGFL6 in UCEC. In conclusion, our research identifies EGFL6 overexpression serves as a clinically significant biomarker in UCEC, closely associated with adverse clinical features and poorer survival outcomes. These findings position EGFL6 as a promising diagnostic and prognostic tool, offering valuable insights into the molecular mechanisms driving UCEC. Its demonstrated association with immune cell infiltration and its involvement in critical signaling pathways further underscores its potential relevance in developing therapeutic strategies. Moving forward, it is essential to conduct comprehensive validation studies to confirm EGFL6's clinical utility and investigate its viability as a target for therapy and ultimately improving patient outcomes in UCEC. Declarations Author Contribution A.A. contributed to study design, immunohistochemistry and RT-qPCR, data interpretation and analysis.Q.L. contributed to the study design, data collection, and analysis.G.A. participated in data interpretation and manuscript drafting. N.M. provided critical revisions and supervised the overall study.X.A. data collection, and analysis.A.A. the study design, statistical analysis.B.H. Manuscript drafting.R. F. contributed to the study design, data collection, manuscript drafting.All authors reviewed and approved the final manuscript. Data Availability The datasets utilized in this investigation can be are assessed through The Cancer Genome Atlas (TCGA) repository (https://portal.gdc.cancer.gov/), the MethSurv database (http://biit.cs./methsurv/) and the cBioportal database (http://www.cbioportal.org). References Huang, G. S. & Santin, A. D. Genetic landscape of clear cell endometrial cancer and the era of precision medicine. Cancer 123 , 3216–3218 (2017). 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Role of imaging in the routine management of endometrial cancer. Int. J. Gynaecol. Obstet. 143 , 109–117 (2018). Baker-Rand, H. & Kitson, S. J. Recent Advances in Endometrial Cancer Prevention, Early Diagnosis and Treatment. Cancers 16 , 1028 (2024). Chen, H., Strickland, A. L. & Castrillon, D. H. Histopathologic diagnosis of endometrial precancers: Updates and future directions. Semin Diagn. Pathol. 39 , 137–147 (2022). Njoku, K., Barr, C. E. & Crosbie, E. J. Current and Emerging Prognostic Biomarkers in Endometrial Cancer. Front. Oncol. 12 , (2022). Kang, J. et al. The emerging role of EGFL6 in angiogenesis and tumor progression. Int. J. Med. Sci. 17 , 1320–1326 (2020). HSU, H. et al. Cytoplasmic Expression of the EGFL6 Protein Is an Independent Prognostic Factor for Shortened Patient Survival in Human Hepatocellular Carcinoma. Vivo 38 , 2455–2463 (2024). Sung, T. et al. EGFL6 promotes colorectal cancer cell growth and mobility and the anti-cancer property of anti-EGFL6 antibody. Cell. Bioscience 11 , (2021). An, J. et al. EGFL6 promotes breast cancer by simultaneously enhancing cancer cell metastasis and stimulating tumor angiogenesis. Oncogene 38 , 2123–2134 (2019). Chang, C. et al. Validation of EGFL6 expression as a prognostic marker in patients with lung adenocarcinoma in Taiwan: a retrospective study. Bmj Open. 8 , e21385 (2018). Shi, S., Ma, T., Xi, Y. A. & Pan-Cancer Study of Epidermal Growth Factor-Like Domains 6/7/8 as Therapeutic Targets in Cancer. Front. Genet. 11 , (2020). Garrett, A. A. et al. EGFL6 promotes endometrial cancer cell migration and proliferation. Gynecol. Oncol. 185 , 75–82 (2024). McCluggage, W. G., Singh, N. & Gilks, C. B. Key changes to the World Health Organization (WHO) classification of female genital tumours introduced in the 5th edition Histopathology . 80, 762–778 (2022). (2020). Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods . 12 , 453–457 (2015). Modhukur, V. et al. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics 10 , 277–288 (2018). Huvila, J., Pors, J., Thompson, E. F. & Gilks, C. B. Endometrial carcinoma: molecular subtypes, precursors and the role of pathology in early diagnosis. J. Pathol. 253 , 355–365 (2021). Miller, K. D. et al. Cancer treatment and survivorship statistics, Ca: A Cancer Journal for Clinicians . 72, 409–436 (2022). (2022). Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer Stat. 2023 Ca: Cancer J. Clin. 73 , 17–48 (2023). Berek, J. S. et al. FIGO staging of endometrial cancer: 2023. J. Gynecol. Oncol. 34 , (2023). Vermij, L., Smit, V., Nout, R. & Bosse, T. Incorporation of molecular characteristics into endometrial cancer management. Histopathology 76 , 52–63 (2020). Urick, M. E. & Bell, D. W. Clinical actionability of molecular targets in endometrial cancer. Nat. Rev. Cancer . 19 , 510–521 (2019). Addante, F. et al. Mismatch Repair Deficiency as a Predictive and Prognostic Biomarker in Endometrial Cancer: A Review on Immunohistochemistry Staining Patterns and Clinical Implications. Int. J. Mol. Sci. 25 , 1056 (2024). Chui, M. H. et al. Decr easedHER2 expression in endometrial cancer followinganti-HER2 therapy. J. Pathol. 262 , 129–136 (2024). van den Heerik, A. S. V. M., Horeweg, N., de Boer, S. M., Bosse, T. & Creutzberg, C. L. Adjuvant therapy for endometrial cancer in the era of molecular classification: radiotherapy, chemoradiation and novel targets for therapy. Int. J. Gynecol. Cancer . 31 , 594–604 (2021). Su, G., Wang, W., Xu, L. & Li, G. Progress of EGFL6 in angiogenesis and tumor development. Int. J. Clin. Exp. Pathol. 15 , 436–443 (2022). Huo, F. et al. Epidermal growth factor-like domain multiple 6 (EGFL6) promotes the migration and invasion of gastric cancer cells by inducing epithelial-mesenchymal transition. Invest. New. Drugs . 39 , 304–316 (2021). Zhu, Z. et al. Elevated EGFL6 modulates cell metastasis and growth via AKT pathway in nasopharyngeal carcinoma. Cancer Med. 7 , 6281–6289 (2018). Xu, T. et al. Research Progress of DNA Methylation in Endometrial Cancer. Biomolecules 12 , 938 (2022). Casanova, J. et al. Prognosis of polymerase epsilon (POLE) mutation in high-grade endometrioid endometrial cancer: Systematic review and meta-analysis. Gynecol. Oncol. 182 , 99–107 (2024). LV, B. et al. Immunotherapy: Reshape the Tumor Immune Microenvironment. Front. Immunol. 13 , (2022). Li, M. et al. Metabolism, metabolites, and macrophages in cancer. J. Hematol. Oncol. 16 , (2023). Hamze Sinno, S. et al. Egfl6 promotes ovarian cancer progression by enhancing the immunosuppressive functions of tumor-associated myeloid cells. J. Clin. Invest. 134 , (2024). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers invited by journal 13 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5962280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442432942,"identity":"6ec593ad-23bc-4dca-bab2-c0262a33a484","order_by":0,"name":"Qiannan Li","email":"","orcid":"","institution":"Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Qiannan","middleName":"","lastName":"Li","suffix":""},{"id":442432943,"identity":"b304da2e-7d49-4e02-b581-1cd5895d5a58","order_by":1,"name":"Nuerbiya Muheteer","email":"","orcid":"","institution":"The FirstPeoplès Hospital of Kashi Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Nuerbiya","middleName":"","lastName":"Muheteer","suffix":""},{"id":442432944,"identity":"4c3edfa7-ecb4-4278-955f-a51247c59258","order_by":2,"name":"Gulibositan Ayoufu","email":"","orcid":"","institution":"The FirstPeoplès Hospital of Kashi Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Gulibositan","middleName":"","lastName":"Ayoufu","suffix":""},{"id":442432945,"identity":"c11795d1-ca05-45d8-8144-d3abaafa3cf9","order_by":3,"name":"Xieerwaniguli Abulimiti","email":"","orcid":"","institution":"Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Xieerwaniguli","middleName":"","lastName":"Abulimiti","suffix":""},{"id":442432946,"identity":"455e1ed1-3788-42f2-9424-89120964f175","order_by":4,"name":"Ayimuguli Aini","email":"","orcid":"","institution":"Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Ayimuguli","middleName":"","lastName":"Aini","suffix":""},{"id":442432947,"identity":"960f094d-3b62-4043-a026-aa918800e7c9","order_by":5,"name":"Bingjie Han","email":"","orcid":"","institution":"Kashi University","correspondingAuthor":false,"prefix":"","firstName":"Bingjie","middleName":"","lastName":"Han","suffix":""},{"id":442432948,"identity":"2609d3a8-fdb0-4562-ac89-9c01430282d6","order_by":6,"name":"Reyila Fulati","email":"","orcid":"","institution":"The FirstPeoplès Hospital of Kashi Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Reyila","middleName":"","lastName":"Fulati","suffix":""},{"id":442432949,"identity":"457f76df-7eec-4651-acec-83182a34dfd6","order_by":7,"name":"Ainiwaerjiang Abudourousuli","email":"data:image/png;base64,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","orcid":"","institution":"The FirstPeoplès Hospital of Kashi Prefecture","correspondingAuthor":true,"prefix":"","firstName":"Ainiwaerjiang","middleName":"","lastName":"Abudourousuli","suffix":""}],"badges":[],"createdAt":"2025-02-05 04:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5962280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5962280/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-07379-7","type":"published","date":"2025-07-02T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80791307,"identity":"2622878e-e012-4117-a9ef-9d1b5bf90de0","added_by":"auto","created_at":"2025-04-17 06:50:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3579631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEGFL6 expression levels in UCEC.\u003c/strong\u003e (A) Analysis of EGFL6 expression utilizing TCGA-UCEC dataset (p\u0026lt;0.001). (B) EGFL6 expression in paired UCEC samples (n=23). (C) ROC curve of EGFL6 in UCEC. (D) EGFL6 expression in adjacent non-cancerous tissues (scale bar: 100um). (E) EGFL6 expression in UCEC tissues (scale bar: 100um). (F) Statistical analysis of IHC results.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/809f6d01c1489154a8373f23.png"},{"id":80791304,"identity":"4a52aaba-4fe2-49fa-b96e-94f91ac362ac","added_by":"auto","created_at":"2025-04-17 06:50:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEGFL6 expression is significantly correlated with various clinical features in UCEC. \u003c/strong\u003e(A) Age, (B) Clinical stage, (C) Weight, (D) Histological type, (E) Histologic grade, (F) Menopause status.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/9be8186985db3cd640338d39.png"},{"id":80791320,"identity":"440ea8c2-8b4a-4dab-9258-7e2c47162bd1","added_by":"auto","created_at":"2025-04-17 06:50:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4953092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of DEGs associated with EGFL6 in UCEC.\u003c/strong\u003e (A) Volcano plot illustrating EGFL6-related DEGs in UCEC. (B) Top ten EGFL6-related DEGs in UCEC. (C) GO/KEGG analysis for the DEGs in UCEC. (D）GSEA analysis of DEGs in UCEC.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/ac8cf6ad862bcddb1b41d368.png"},{"id":80791322,"identity":"79728996-e7c8-4f60-b696-5c116ab7a757","added_by":"auto","created_at":"2025-04-17 06:50:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1034894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethylation profile of EGFL6 in UCEC.\u003c/strong\u003e (A) Heatmap depicting the methylation status of EGFL6 in UCEC. The relationship between methylation sites of EGFL6 and patient outcomes is demonstrated: (B) cg07810164, (C) cg00932276, (D) cg12817924, (E) cg26310256, (F) cg23083672, (G) cg1246113.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/a14c64757c45402564edc9d0.png"},{"id":80791334,"identity":"63e9d8e0-90b1-4f02-92c7-cb5425581556","added_by":"auto","created_at":"2025-04-17 06:50:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7610626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between the expression of EGFL6 and various immune cell infiltration. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eOverall correlation between EGFL6 expression levels and a variety of immune cell populations. (B) Correlation with regulatory T cells (Tregs). (C) Correlation with CD8+ T cells. (D) Correlation with resting dendritic cells. (E) Association with regulatory T cells (Tregs) . (F) Association with CD8+ T cells. (G) Association with resting dendritic cells.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/f7e25dc34e9aff1b3f3eea84.png"},{"id":80791308,"identity":"d724f191-ce12-4f07-8729-5cfa24d29db3","added_by":"auto","created_at":"2025-04-17 06:50:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased expression levels of EGFL6 are associated with decreased survival rates in UCEC. \u003c/strong\u003e(A) OS, (B) DSS, (C) PFI.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/f200dd27a2b6fbd958f5e08a.png"},{"id":80792568,"identity":"27668a45-4b41-41ad-ad29-00ad6ebb1aef","added_by":"auto","created_at":"2025-04-17 06:58:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8959897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomograms and Calibration curves presenting OS, DSS and PFI in UCEC. \u003c/strong\u003e(A) Nomogram of OS in UCEC. (B) Calibration curve of OS in UCEC, showing the predictive accuracy. (C) Nomogram of DSS in UCEC. (D) Calibration curve of DSS in UCEC. (E) Nomogram of PFI in UCEC. (F) Calibration curve of PFI in UCEC, demonstrating the model's reliability. (G) ROC curves of the nomogram- OS. (H) ROC curves of the nomogram- DSS.（I）ROC curves of the nomogram-PFI.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/3fd81f41b355b6e09890255d.png"},{"id":80791328,"identity":"db41df2e-e5d7-46c9-9c5d-4ab627a0e6d9","added_by":"auto","created_at":"2025-04-17 06:50:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3856238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEGFL6 expression is closely correlated with various subtypes of UCEC. \u003c/strong\u003e(A) Age, (B) Histologic grading: G2/G3 (C) BMI, (D) Therapy outcome: PR/CR, (E) Tumor invasion: ≤50%, (F) Radiation therapy, (G) Diabetes, (H) Hormone therapy: No, (I) Menopause status: Post.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/9a9eeb9f3801b6495a70dc0f.png"},{"id":86180023,"identity":"53767a81-c612-4ae1-8609-0365cc03d0f0","added_by":"auto","created_at":"2025-07-07 16:20:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29437634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/f7802914-8928-402b-8ab1-28e1453888b6.pdf"},{"id":80791306,"identity":"dcc56260-d94f-45f7-9570-00a0b167e762","added_by":"auto","created_at":"2025-04-17 06:50:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4486286,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5962280/v1/96231c3536dc6e973c089436.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression and prognostic value of EGF-like domain multiple 6 in uterine corpus endometrial carcinoma and its correlation with immune cell infiltration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC) is one of the most common cancers impacting the female reproductive system, presenting considerable health threats to women as a result of its elevated rates of morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to global cancer statistics in 2022, UCEC ranks sixth among cancers in women\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In China, the incidence of UCEC is also notably high, typically ranking between eighth and tenth among chinese women\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This estrogen-dependent malignancy poses disproportionate threats due to its accelerating incidence rates (3.1% annual increase since 2010) coupled with established risk factors including advanced age, hypertension, metabolic disorders (obesity, diabetes), hormonal imbalance (unopposed estrogen), and hereditary cancer syndromes (Lynch syndrome)\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Current diagnostic and therapeutic strategies for UCEC, which encompass imaging techniques, pathological diagnosis, surgical interventions, and chemotherapy, are often hampered by challenges such as late-stage diagnosis and heterogeneous treatment responses\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This clinical landscape underscores the urgent need for molecular biomarkers that are capable of addressing two critical gaps: (1) early detection in pre-symptomatic phases, and (2) personalized prognostic stratification beyond traditional clinicopathologic parameters\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have underscored EGFL6`s potential as a cancer biomarker, particularly regarding its involvement in tumorigenesis and metastasis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Notably, EGFL6 is highly expressed across multiple cancer types, such as ovarian cancer (OC), breast cancer (BC), lung adenocarcinoma (LUAD), colorectal cancer (CRC), and hepatocellular carcinoma (HCC)\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This indicates that EGFL6 could be pivotal in the molecular mechanisms that govern these malignancies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While prior research has demonstrated a correlation between EGFL6 expression levels and endometrial cancer progression\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, the comprehensive characterization of EGFL6, including its prognostic significance, methylation and mutation profiles, as well as its associations with immune cell infiltrations in UCEC, remains incomplete. This knowledge gap highlights the need for further investigation to fully understand the potential of EGFL6`s dual role as a diagnostic biomarker and therapeutic target in UCEC.\u003c/p\u003e \u003cp\u003eTo address this critical gap, this study employs rigorous bioinformatics methods, utilizing RNA-seq data from TCGA with experimental validation through IHC on tissue microarrays and RT-qPCR to systematically evaluate EGFL6 expression patterns in UCEC. The research focuses on elucidating EGFL6`s potential as both a diagnostic and prognostic biomarker in UCEC, with parallel exploration of its correlation with immune cell infiltration patterns in tumor microenvironment. These investigations aims to unravel the molecular mechanisms driving UCEC progression. The findings may contribute to future studies validating EGFL6`s clinical utility as both a biomarker and therapeutic target, potentially improving patient management and outcomes in UCEC through enhanced understanding of disease pathogenesis and immune interactions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and preprocessing\u003c/h2\u003e \u003cp\u003eRNA sequencing (RNA-seq) data, accompanied by clinical details pertaining to patients diagnosed with UCEC, were sourced from the TCGA database (\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), ensuring the utilization of high-quality and meticulously curated datasets. The dataset comprised 554 tumor samples, 35 adjacent non-cancerous samples and 23 paired normal-tumor samples. To enable comparative analyses and enhance the reliability of subsequent findings, the level 3 HTSeq-FPKM data were transformed into transcripts per million (TPM) for normalization purposes, thereby allowing for more precise comparisons across the samples.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue microarray\u003c/h3\u003e\n\u003cp\u003eThe tissue microarray employed in this research was obtained from Weiao (Shanghai) Biological Co., Ltd. (catalogue number: ZL-UteS961). Ethical approval was granted by the ethics committee of Weiao (Shanghai) Biological Co., Ltd. (reference number Csya2024056). This array included 48 UCEC specimens and 48 adjacent non-cancerous tissues, facilitating an in-depth assessment of EGFL6 expression within various cancer stages. As this investigation was retrospective and involved the analysis of patient medical records and tumor samples, the requirement for individual informed consent was waived by the ethics committee. Approval was granted on the basis that no identifiable patient information was used in the study. Furthermore, all procedures undertaken in this research adhered strictly to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry procedures\u003c/h3\u003e\n\u003cp\u003eTumor specimens and their corresponding adjacent tissues were fixed in 10% formalin solution, embedded in paraffin, and sectioned into 4\u0026ndash;6 \u0026micro;m thick slices. These sections were affixed to glass slides. After deparaffinization and rehydration, antigen retrieval was performed using microwave treatment in a citrate buffer (pH 6.0), a critical step for optimizing antibody binding. The sections were incubated overnight at a temperature of 4\u0026deg;C with a primary anti-EGFL6 antibody (Bioss, cat. no. bs-13062R) diluted 1:100. Subsequently, the sections were incubated with a secondary antibody at room temperature for 30 minutes. Finally, DAB substrate staining and hematoxylin counterstaining were performed to visualize EGFL6 expression in the tissue microarray.\u003c/p\u003e\n\u003ch3\u003eAssessment of immunohistochemical staining\u003c/h3\u003e\n\u003cp\u003eTwo independent pathologists, blinded to the study evaluated the pathological slides based on the criteria outlined in the most recent literature, specifically the 2022 WHO classification of female genital tumors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Staining intensity was categorized as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the proportion of positive cells was scored as 0 (0%), 1 (\u0026lt;\u0026thinsp;25%), 2 (25\u0026ndash;50%), or 3 (\u0026gt;\u0026thinsp;50%). The final IHC score (range:0\u0026ndash;12) was calculated by multiplying the intensity and proportion scores. To validate the findings and assess inter-observer agreement, Cohen's kappa test was employed.\u003c/p\u003e\n\u003ch3\u003eAnalysis of differentially expressed genes\u003c/h3\u003e\n\u003cp\u003eAccording to TCGA-UCEC data, patients with UCEC were categorized into high-expression and low-expression groups based on the median expression level of EGFL6. This stratification enabled a clearer distinction in biological responses. The identification of differentially expressed genes (DEGs) between these two classifications was performed employing the R package DESeq2, with statistical significance determined by an adjusted p-value of less than 0.05 and a |log2-fold-change (FC)| exceeding 2. Following this, the top ten DEGs underwent correlation analysis with EGFL6 expression through Spearman correlation analysis, thereby providing insights into the potential pathways influenced by EGFL6. Subsequently, Spearman correlation analysis was performed to assess the association between EGFL6 expression and the top ten DEGs, revealing potential pathways modulated by EGFL6.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe functional enrichment analysis of DEGs was conducted by employing GO/KEGG methodologies, utilizing the R package GOplot (version 1.0.2) as outlined by Walter et al.(2015). The primary objective of these analyses were to clarify the biological roles and pathways related with EGFL6. Additionally, GSEA was performed using the R package clusterProfiler, where an adjusted p-value less than 0.05 and a false discovery rate (FDR) below 0.25 were deemed statistically significant for identifying enriched functions or pathways. This methodology yielded crucial insights into the molecular mechanisms underlying the progression of UCEC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of immune infiltration\u003c/h3\u003e\n\u003cp\u003eThe assessment of immune infiltration encompassed 22 unique immune cell types, with relative enrichment scores derived from CIBERSORT analysis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To explore the relationship between EGFL6 expression and different immune cell types, a Spearman correlation analysis was performed. The Wilcoxon rank-sum test was utilized to examine the disparities in immune infiltration levels between groups with high and low EGFL6 expression, thereby offering valuable insights into the immunological environment associated with EGFL6 expression in UCEC.\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eSurvival analysis was conducted utilizing the Kaplan-Meier technique alongside the log-rank test, with the threshold established at the median expression level of EGFL6. Both univariate and multivariate Cox regression analyses were carried out to evaluate the impact of clinical factors on patient survival outcomes. Clinical variables that demonstrated prognostic relevance, indicated by a p-value of less than 0.1 in the univariate analysis, were incorporated into the multivariate Cox regression model. The findings were illustrated using forest plots created with the R package ggplot2, which facilitated an in-depth understanding of survival results.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical evaluations were performed utilizing R software (version 3.6.3). To determine the statistical significance of EGFL6 expression in both unpaired and paired tissue samples, the Wilcoxon rank-sum test and paired t-test were utilized, respectively. Furthermore, logistic regression was employed in conjunction with the Wilcoxon rank-sum test to explore the association between clinical characteristics and EGFL6 expression. All statistical tests were conducted as two-sided, with p-values less than 0.05 deemed statistically significant, thus reinforcing the reliability of the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation\u003c/h2\u003e \u003cp\u003eIn order to confirm the results obtained from RNA-seq and immunohistochemical analyses, quantitative real-time PCR (RT-qPCR) was performed on a separate cohort consisting of 20 UCEC tissue samples alongside their corresponding adjacent non-malignant tissues. Total RNA was isolated utilizing TRIzol reagent (Invitrogen) and subsequently converted into complementary DNA (cDNA) using the PrimeScript RT reagent kit (Takara Bio, Japan). The RT-qPCR assays were conducted employing SYBR Green Master Mix (Takara Bio) on a QuantStudio 3 Real-Time PCR System. The expression levels of EGFL6 were standardized against GAPDH, and the relative expression was determined employing the 2^\u0026minus;ΔΔCt methodology. Statistical evaluations were carried out using paired t-tests to analyze the differences in EGFL6 expression levels between the tumor tissues and their adjacent non-cancerous counterparts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEGFL6 expression in uterine corpus endometrial carcinoma, and validation via IHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a pan-cancer analysis of EGFL6 in various cancer types using TIMER2.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003c/span\u003e), TCGA datasets and 16 paired cancer types in TCGA. The results indicate that EGFL6 is significantly overexpressed in various cancer types, including UCEC \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/strong\u003e. To understand the expression level of EGFL6 in UCEC, we analyzed EGFL6 expression levels in TCGA-UCEC data. Our analysis indicate that EGFL6 is significantly overexpressed in UCEC \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. We further investigated the expression levels of EGFL6 in 23 paired UCEC samples \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e, which yielded similar findings. The receiver operating curve (ROC) for EGFL6 in UCEC revealed an area under the curve (AUC) of 0.679 (95% CI: 0.586\u0026ndash;0.772), suggesting a moderate level of diagnostic precision for EGFL6 in UCEC \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. We then conducted IHC assay in UCEC tissue microarray \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/strong\u003e. Our analysis demonstrated that EGFL6 exhibited a predominant localization in both the cell membrane and cytoplasm, with particularly notable differences in expression observed in the cervical glandular epithelium between adjacent non-cancerous and cancer tissues \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD-F\u003cstrong\u003e)\u003c/strong\u003e. Collectively, these results suggest that EGFL6 is markedly overexpressed in UCEC and may contribute to tumor-specific pathological changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGFL6 expression is significantly correlated with various clinical features in UCEC patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing the median expression level of EGFL6 in UCEC, we categorized the patients into two distinct groups: those with high EGFL6 expression and those with low EGFL6 expression \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. Our analysis indicated close associations between EGFL6 expression with key patient characteristics, including clinical stage (I-IV), age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), weight (\u0026lt;\u0026thinsp;80 vs. \u0026ge;80 kg), menopause status (Post vs. Pre\u0026amp;Peri), histological grading (G2\u0026amp;G3 vs. G1) and histological subtypes (Endometrioid vs.Mixed\u0026amp;Serous) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-F\u003cstrong\u003e).\u003c/strong\u003e Additionally, our Logistic regression analysis yielded similar results \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. These findings suggest that EGFL6 expression is closely linked to multiple clinical features in UCEC patients with potential prognostic implications.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEGFL6 expression in UCEC.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEGFL6-Low\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEGFL6-High\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;= 60 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (22.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81 (14.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 (27.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195 (35.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinical stage, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage I\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e187 (33.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156 (28.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (9.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77 (13.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage IV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;= 80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111 (20.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e132 (24.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157 (29.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (24.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeight, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;= 160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (25.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114 (21.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (25.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145 (27.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.298\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;= 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102 (19.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (21.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163 (31.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146 (28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHistological type, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrioid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e243 (43.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (30.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (16.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMixed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidual tumor, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.816\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197 (47.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180 (43.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (2.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor invasion(%), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.480\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142 (29.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119 (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;= 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (23.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (22.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHistologic grading, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (11.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (6.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81 (14.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (23.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193 (35.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMenopause status, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.097\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (2.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeri\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217 (42.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (46.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.695\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166 (36.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (13.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (14.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHormones therapy, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.830\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154 (44.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145 (41.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (6.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiation therapy, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.894\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (26.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125 (23.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Represents Chisq test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially expressed gene analysis of EGFL6 in UCEC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the biological phenotypes and signaling pathways related to EGFL6, a differential gene expression (DEG) analysis was conducted. Our analysis revealed 156 genes that were up-regulated and 129 that were down-regulated \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Among these, the top ten most DEGs included DEFA6, DEFA5, MT4, OBP2B, GAGE2A, CACNA1S, SOX11, KRTAP3-3, CST1 and TPH1 \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. GO/KEGG analysis demonstrated that the DEGs were primarily concentrated in biological processes (BP) including epidermis development, keratinocyte differentiation and keratinization. In terms of cellular components (CC), the DEGs were associated with intermediate filament cytoskeleton, intermediate filament and nuclear envelope. In the context of molecular functions (MF), we observed a significant enrichment in activities related to signaling receptor activation, receptor-ligand interactions, and growth factor functionality. Our KEGG analysis revealed a significant enrichment of various signaling pathways, notably the MAPK/Ras signaling pathways and pathways associated with staphylococcus aureus infection \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. To further elucidate EGFL6-related pathways, we conducted Gene Set Enrichment Analysis (GSEA). The findings indicated potential enrichment in pathways involving chromosome segregation, the organization of chromosomes pertinent to the meiotic cell cycle, GABAergic synapse, ATP-dependent activity acting on DNA and GABA-gated chloride ion channel activity \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e. Collectively, our analyses suggest that EGFL6 expression in UCEC may be associated with certain signaling pathways and biological regulatory mechanisms.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression analysis of EGFL6 in UCEC.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal (N)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (\u0026gt;\u0026thinsp;60 vs. \u0026lt;= 60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e551\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.036 (1.433\u0026ndash;2.893)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClinical stage (Stage III\u0026amp; IV vs. Stage I\u0026amp; II)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.675 (1.154\u0026ndash;2.433)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight (\u0026gt;\u0026thinsp;80 vs. \u0026lt;= 80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.696 (0.494\u0026ndash;0.981)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeight (\u0026gt;\u0026thinsp;160 vs. \u0026lt;= 160)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.272 (0.902\u0026ndash;1.793)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (\u0026gt;\u0026thinsp;30 vs. \u0026lt;= 30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.831 (0.585\u0026ndash;1.179)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHistological type (Endometrioid vs. Mixed\u0026amp;Serous)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.219 (0.142\u0026ndash;0.337)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHistologic grade (G2\u0026amp;G3 vs. G1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.850 (1.183\u0026ndash;2.895)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidual tumor (R1\u0026amp;R2 vs. R0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.216 (0.623\u0026ndash;2.372)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMenopause status (Post vs. Pre\u0026amp;Peri)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.905 (1.052\u0026ndash;3.449)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor invasion(%) (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50 vs. \u0026lt; 50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.139 (0.793\u0026ndash;1.635)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.481\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes (Yes vs. No)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.086 (0.719\u0026ndash;1.642)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.695\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiation therapy (Yes vs. No)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e529\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.024 (0.727\u0026ndash;1.440)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHormones therapy (Yes vs. No)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.935 (0.505\u0026ndash;1.731)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.830\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMethylation and mutation status of EGFL6 in UCEC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a comprehensive analysis of EGFL6 methylation utilizing the MethSurv database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biit.cs./methsurv\u003c/span\u003e\u003c/span\u003e) in the context of UCEC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Our analysis identified nine methylation status in the promoter region of EGFL6 \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Among these sites, cg07810164, cg00932276, cg12817924, cg26310256, cg23083672, and cg1246113 showed association with poorer patient prognosis \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB-G\u003cstrong\u003e)\u003c/strong\u003e. Additionally, we examined the mutation status of EGFL6 in UCEC using the cBioportal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org\u003c/span\u003e\u003c/span\u003e). Our analysis revealed a mutation rate of 3% EGFL6 in UCEC \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;3A)\u003c/strong\u003e. However, we found no significant correlations between EGFL6 mutation status and UCEC patient survival outcomes including OS, DSS, DFS, or PFI \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;3B-E)\u003c/strong\u003e. These observations suggest that while EGFL6 methylation patterns may potentially influence UCEC progression, the clinical relevance of its genetic mutations requires further investigation to establish definitive biological significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGFL6 expression correlates with immune cell infiltration patterns in UCEC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed an analytical assessment to examine the relationship between EGFL6 expression and immune cell infiltration in UCEC \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Our findings indicated negative correlations between EGFL6 expression and the presence of regulatory T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and resting dendritic cells \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB-D\u003cstrong\u003e)\u003c/strong\u003e. Additionally, the correlation analysis indicated notable negative associations between EGFL6 expression and these specific immune cell populations \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE-G\u003cstrong\u003e).\u003c/strong\u003e Conversely, we identified positive correlations between EGFL6 expression and activated dendritic cells, CD4 memory resting T cells and M2 macrophages \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;4)\u003c/strong\u003e. These results collectively imply a potential role for EGFL6 in shaping an immunosuppressive tumor microenvironment within the context of UCEC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGFL6 expression correlates with poor survival outcomes in UCEC and pan-cancer cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe undertook a Kaplan-Meier analysis to assess the correlation between EGFL6 expression levels and prognosis in UCEC patients. Our analysis demonstrated that elevated EGFL6 expression was significantly linked to decreased overall survival (p\u0026thinsp;=\u0026thinsp;0.009), reduced disease-specific survival (p\u0026thinsp;=\u0026thinsp;0.002) and poor progression free interval (p\u0026thinsp;=\u0026thinsp;0.018) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-C\u003cstrong\u003e)\u003c/strong\u003e. Additionally, we performed survival analysis of EGFL6 across various cancer types. Our results revealed a strong association between high expression of EGFL6 and poor survival between bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), kidney renal papillary cell carcinoma (KIRP), and pancreatic adenocarcinoma (PAAD) \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;5A-C)\u003c/strong\u003e. Furthermore, we conducted ROC analysis for these cancer types, revealing that KIRC, KIRP and LIHC indicated AUC scores greater than 0.8 \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;5D-I)\u003c/strong\u003e. Collectively, these findings imply that high EGFL6 expression are linked with unfavorable survival outcomes in UCEC and various other cancer types.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruction and validation of three prognostic nomograms\u003c/h2\u003e\n\u003cp\u003eWe performed both univariate and multivariate Cox regression analyses to predict the prognostic value of EGFL6 in UCEC \u003cstrong\u003e(Supplementary Tables\u0026nbsp;1a-c)\u003c/strong\u003e. To enhance the accuracy of our prediction for UCEC patients, we constructed prognostic nomograms that included independent predictors which showed statistical significance in the Cox univariate analysis. These nomograms serving as visual tools, with elevated scores indicating a worse prognosis, thereby assisting in individualized risk evaluation. The predictive accuracy of the nomogram for OS was thoroughly assessed using calibration curves \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-B\u003cstrong\u003e)\u003c/strong\u003e, demonstrating its dependability in estimating survival probabilities. Additionally, the nomograms for DSS and PFI, along with their respective calibration curves, are depicted \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC-F\u003cstrong\u003e)\u003c/strong\u003e. The bootstrap resampling consistency index (C-index) for the nomograms were computed, resulting in a C-index score of 0.785 (95% Confidence Interval [CI]: 0.755\u0026ndash;0.815) for OS, 0.880 (95% CI: 0.857\u0026ndash;0.903) for DSS, and 0.716 (95% CI: 0.687\u0026ndash;0.746) for PFI. To confirm the predictive accuracy of these three nomograms, we constructed ROCs for the nomograms in the categories of OS, DSS and PFI in 1, 3, 5 year intervals \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG-I\u003cstrong\u003e)\u003c/strong\u003e. These findings reflect a moderate accuracy level in forecasting OS, DSS, and PFI among UCEC patients. These results suggest potential utility for personalized risk stratification in UCEC management, though prospective multicenter studies are needed to confirm generalizability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression of EGFL6 is significantly linked to unfavorable outcomes in UCEC subtypes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, the relationships between EGFL6 expression levels and patient survival across various subtypes of UCEC were tested. Our findings indicated that elevated EGFL6 expression correlates with unfavorable prognosis outcomes in multiple UCEC subtypes, including age, histological grade, body mass index (BMI), primary therapy outcomes:(partial response (PR) and complete response (CR), tumor invasion (\u0026le;\u0026thinsp;50%), radiation therapy, diabetes, hormone therapy (no), and menopause status (postmenopausal)\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA-I\u003cstrong\u003e)\u003c/strong\u003e. These observations imply that EGFL6 may serve as a valuable prognostic biomarker across diverse UCEC subtypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eRT-qPCR analysis\u003c/h2\u003e\n\u003cp\u003eThe RT-qPCR analysis confirmed that EGFL6 is overexpressed in UCEC tissues when compared to non-cancerous tissues, which is consistent with our results from RNA-seq and IHC. Specifically, EGFL6 expression was notably elevated in UCEC samples (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01), in contrast to non-cancerous samples (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, a strong association was identified between the EGFL6 expression obtained from RT-qPCR and those derived from RNA-seq (r\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), thereby reinforcing the credibility of the transcriptomic analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC) remains the most prevalent malignancy of the female reproductive system, predominantly affecting perimenopausal and postmenopausal women\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Globally, UCEC ranks among the top ten female cancers, with particularly high incidence rates in developed regions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The disease exhibits significant histological diversity, encompassing endometrioid and more aggressive non-endometrioid subtypes (e.g., serous and clear cell carcinomas), each demonstrating distinct molecular profiles and clinical behaviors\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Recent advances in molecular profiling have identified several promising biomarkers that may improve diagnostic accuracy and prognostic stratification, offering potential pathways for personalized treatment strategies in UCEC patients\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. For instance, the levels of mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, and PMS2) serve as indicators of microsatellite instability (MSI) and are indicators of positive responses to immunotherapeutic interventions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, overexpression of the oncogene HER2 is linked to a more severe form of the disease and can inform targeted therapeutic approaches\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These biomarkers enhance our understanding of UCEC pathogenesis and offer potential for better patient stratification and treatment optimization. Current treatment strategies, including imaging, surgery, and chemotherapy, often face challenges due to late-stage diagnoses and heterogeneous treatment responses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This situation underscores the necessity for identifying new biomarkers that can facilitate early detection and improve prognostic accuracy, ultimately leading to enhanced patient outcomes.\u003c/p\u003e \u003cp\u003eIn this investigation, we focused on the expression of EGFL6 as a potential biomarker in UCEC. Previous research has established that EGFL6 is involved in various cancers, underscoring its importance in tumor biology and its prospective utility as both a diagnostic and prognostic marker for UCEC\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. By integrating RNA-seq data from TCGA, with IHC experiment on UCEC tissue microarray and RT-qPCR data, we conducted extensive statistical analyses to explore the association between EGFL6 expression levels and clinical characteristics of UCEC patients. These results demonstrate a significant correlation between elevated EGFL6 expression and adverse clinical features, such as advanced tumor stage, highlighting its relevance in patient stratification and clinical decision-making. This overexpression aligns with findings in multiple malignancies, where EGFL6 has been associated with tumor growth and metastasis through pathways such as MAPK and PI3K/Akt signaling\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our GO/KEGG analyses, along with GSEA analysis, suggest a significant association between elevated EGFL6 expression and the MAPK/Ras signaling pathways. However, this correlation requires experimental validation (e.g., Western blot analysis of pathway proteins) to establish causality.\u003c/p\u003e \u003cp\u003eAberrant methylation and mutation of key genes are recognized as significant factors contributing to the development and progression of UCEC\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this study, we analyzed the EGFL6 gene, assessing its methylation and mutation status to elucidate its potential impact on UCEC. Our methylation analysis using the MethSurv database identified nine methylation sites in the EGFL6 promoter region, with six sites (cg07810164, cg00932276, cg12817924, cg26310256, cg23083672, and cg1246113) showing association with poor prognosis. These methylation changes may induce transcriptional silencing of EGFL6, potentially disrupting normal cellular processes and promoting tumorigenesis. Furthermore, investigation of EGFL6 mutation status via the cBioPortal database revealed a 3% mutation frequency in UCEC. Given the limited mutation frequency and sample size, this exploratory analysis found no significant correlations between EGFL6 mutations and poor prognosis among UCEC patients. These results indicate that while the methylation status of EGFL6 may be a significant factor in UCEC progression and patient survival, mutations in this gene may not have the same prognostic implications. The tumor immune microenvironment (TIME) critically regulates cancer development and treatment efficacy\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Our CIBERSORT-based analysis revealed that elevated levels of EGFL6 are associated with diminished infiltration of pivotal immune cell populations, including cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells and immunosuppressive regulatory T cells (Tregs). Notably, Supplementary Fig.\u0026nbsp;4 further demonstrates a positive correlation between EGFL6 expression and M2 macrophages, which typically exhibit immunosuppressive functions and are linked to poor prognosis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This dual role of EGFL6 in modulating both effector and suppressive immune populations may contribute to tumor immune evasion. Further explorations of EGFL6`s interaction with immune checkpoint molecules may advance immunotherapeutic strategies for UCEC. Understanding EGFL6-mediated immune modulation could also guide combination therapies to enhance treatment efficacy and improved patient outcomes\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Based on prior evidence that EGFL6 binds to integrin receptors to activate downstream signaling in other cancers, we hypothesize that similar receptor-mediated interactions may underlie its regulation of MAPK/Ras pathways and infiltration of various immune cells in UCEC.\u003c/p\u003e \u003cp\u003eLastly, the prognostic significance of EGFL6 was evident in our Kaplan-Meier analyses, indicating that high expression levels were correlated with significantly reduced patient survival. This finding underscores EGFL6 as an independent prognostic marker, which could enhance existing prognostic models for UCEC and facilitate the development of personalized treatment approaches. By integrating EGFL6 expression into clinical practice, healthcare providers may be able to implement more tailored therapeutic interventions, ultimately improving patient outcomes. Future research should prioritize the confirmation of these results in more extensive cohorts while also investigating the mechanistic pathways through which EGFL6 influences tumor biology in UCEC.\u003c/p\u003e \u003cp\u003eThe limitations of this study must be acknowledged, as they may influence the interpretation of our findings. Firstly, the absence of in vitro and in vivo experimental validation restricts our capacity to confirm the biological significance of the observed EGFL6-related mechanisms. Additionally, the limited sample size, particularly the limited number of paired UCEC samples could affect the reliability and applicability of our analyses. Furthermore, while we explored various clinical features associated with EGFL6 expression, the absence of longitudinal data may hinder our understanding of the dynamic nature of EGFL6's prognostic value over time. These factors underscore the necessity for further validation studies, including larger cohort analyses and experimental investigations, to substantiate the clinical relevance of EGFL6 in UCEC.\u003c/p\u003e \u003cp\u003eIn conclusion, our research identifies EGFL6 overexpression serves as a clinically significant biomarker in UCEC, closely associated with adverse clinical features and poorer survival outcomes. These findings position EGFL6 as a promising diagnostic and prognostic tool, offering valuable insights into the molecular mechanisms driving UCEC. Its demonstrated association with immune cell infiltration and its involvement in critical signaling pathways further underscores its potential relevance in developing therapeutic strategies. Moving forward, it is essential to conduct comprehensive validation studies to confirm EGFL6's clinical utility and investigate its viability as a target for therapy and ultimately improving patient outcomes in UCEC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.A. contributed to study design, immunohistochemistry and RT-qPCR, data interpretation and analysis.Q.L. contributed to the study design, data collection, and analysis.G.A. participated in data interpretation and manuscript drafting. N.M. provided critical revisions and supervised the overall study.X.A. data collection, and analysis.A.A. the study design, statistical analysis.B.H. Manuscript drafting.R. F. contributed to the study design, data collection, manuscript drafting.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets utilized in this investigation can be are assessed through The Cancer Genome Atlas (TCGA) repository (https://portal.gdc.cancer.gov/), the MethSurv database (http://biit.cs./methsurv/) and the cBioportal database (http://www.cbioportal.org).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang, G. S. \u0026amp; Santin, A. D. Genetic landscape of clear cell endometrial cancer and the era of precision medicine. \u003cem\u003eCancer\u003c/em\u003e \u003cb\u003e123\u003c/b\u003e, 3216\u0026ndash;3218 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCancer J. 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Invest.\u003c/em\u003e \u003cb\u003e134\u003c/b\u003e, (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EGF-like Domain Multiple 6, Biomarker, Prognosis, Immune Infiltration, Uterine Corpus Endometrial Carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-5962280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5962280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUterine corpus endometrial carcinoma (UCEC) represents a common malignancy affecting the female reproductive tract, distinguished by elevated rates of both morbidity and mortality. This study investigates EGF-like domain multiple 6 (EGFL6) expression as a potential diagnostic and prognostic biomarker in UCEC. RNA sequencing data along with clinical information of UCEC patients were sourced from The Cancer Genome Atlas (TCGA). Logistic regression analysis assessed correlations between EGFL6 expression and clinical features. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate EGFL6-related biological characteristics and signaling pathways. Immunohistochemistry (IHC) was performed on UCEC tissue microarrays to assess EGFL6 expression levels. Kaplan-Meier method and Cox regression analyses evaluated the prognostic significance of EGFL6. Three nomograms were developed to predict overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) probabilities at one, three, and five years following diagnosis. The expression of EGFL6 was validated through quantitative real-time PCR (RT-qPCR). Our findings demonstrate that EGFL6 is markedly overexpressed in UCEC and is associated with unfavorable clinical characteristics, including clinical stage and histological type. GO/KEGG and GSEA analyses highlighted its possible involvement in critical signaling pathways, such as MAPK and Ras signaling. Additionally, EGFL6 expression was associated with decreased immune cell infiltration, particularly regulatory T cells (Tregs) (r = -0.219, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD8\u0026thinsp;+\u0026thinsp;T cells (r = -0.217, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, EGFL6 expression is linked to poor patient prognosis. In conclusion, EGFL6 presents itself as a potential biomarker for UCEC, which could greatly influence the improvement of diagnostic and prognostic evaluations.\u003c/p\u003e","manuscriptTitle":"Expression and prognostic value of EGF-like domain multiple 6 in uterine corpus endometrial carcinoma and its correlation with immune cell infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 06:49:56","doi":"10.21203/rs.3.rs-5962280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T05:51:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-04T00:05:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T07:57:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174353462769528438467850251877540498848","date":"2025-04-15T22:24:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223044776827429230371388954132475597533","date":"2025-04-14T02:43:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-13T20:26:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T18:30:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-09T04:58:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8df7ae0c-507d-4fc7-b6a8-533de9ddad0a","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47098017,"name":"Health sciences/Biomarkers"},{"id":47098018,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-07-07T16:12:13+00:00","versionOfRecord":{"articleIdentity":"rs-5962280","link":"https://doi.org/10.1038/s41598-025-07379-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:58:20","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-17 06:49:56","video":"","vorDoi":"10.1038/s41598-025-07379-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-07379-7","workflowStages":[]},"version":"v1","identity":"rs-5962280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5962280","identity":"rs-5962280","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}