Construction of breast cancer-related gene prognostic signature in endometrial cancer

Construction of breast cancer-related gene prognostic signature in endometrial cancer | 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 Research Article Construction of breast cancer-related gene prognostic signature in endometrial cancer Junling Xu, Hualing Zhang, Xiaochen Qin, Kaili Zhang, Tianjiao He, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4332299/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Breast cancer (BC) and endometrial cancer (EC) both originate from sex hormone-dependent organs, yet their interaction mechanisms remain unclear. This study aims to explore the common genetic and molecular characteristics between BC and EC, predicting their potential roles in EC treatment and prognosis evaluation. Methods Data on BC and EC were retrieved from The Cancer Genome Atlas Program (TCGA) and the International Cancer Genome Consortium (ICGC) databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify shared genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. Single-factor Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were employed to identify potential breast cancer-related genes (BCRGs), and a prognostic risk scoring system was developed. Additionally, we examined the relationship between risk groups and clinicopathological features, immune infiltration, tumor mutation burden, and drug sensitivity. Results A total of 367 breast cancer-related DEGs were identified in EC, and 113 potentially prognostic DEGs were screened. From these, 11 key BCRGs significantly associated with the overall survival rate of EC patients were identified. Patients in the low-risk group exhibited longer overall survival (OS) compared to those in the high-risk group. Additionally, significant differences in clinical characteristics, tumor immune cell infiltration, somatic mutations, and drug sensitivity were observed between risk groups, with the low-risk group showing a higher likelihood of benefiting from immunotherapy. Conclusion The risk score established in this study demonstrates prognostic ability, potentially aiding in identifying patients who may benefit from immunotherapy and targeted therapy after breast cancer diagnosis. endometrial carcinoma breast carcinoma risk model prognosis TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction Endometrial cancer (EC) ranks as the most prevalent gynecologic malignancy in developed nations[ 1 ]. Due to the escalating rates of obesity, both the incidence of EC and disease-related mortality are steadily rising[ 2 , 3 ]. Annually, more than 65,000 new cases are reported in the United States. In 2023, around 66,200 new cases of EC and 13,030 deaths were recorded. Histologically, EC is categorized into two types: estrogen-dependent (Type I) and non-estrogen-dependent (Type II), depending on grading, gene expression patterns, and histological type. Type I endometrioid adenocarcinoma, as the most common subtype, is mainly associated with excessive exposure to estrogen, especially estrogen not inhibited by progesterone[ 4 ]. Notably, similar to endometrial cancer, estrogen significantly influences the onset and progression of breast cancer. Both endometrial cancer and breast cancer are classified as "estrogen-dependent tumors". Breast cancer (BC) is the predominant malignancy affecting women, with estimated 43.8 million cases reported globally over a five-year span[ 5 ]. A previous research demonstrated that approximately 10% of BC patients develop a secondary cancer within a decade post-initial diagnosis[ 6 ]. Breast cancer encompasses diverse biomarkers, delineating various subtypes based on immunohistochemical expression, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Patients who test positive ER and PR often demonstrate positive reactions to endocrine therapies like tamoxifen (TAM), a drug widely utilized in treating ER-positive breast cancer. Tamoxifen displays anti-estrogenic properties in breast tissue, yet functions as an estrogen agonist within the endometrium. Increasing evidence suggests that among BC survivors, there is a substantially elevated risk of subsequent EC, and receiving tamoxifen treatment raises the likelihood of adverse prognosis in endometrial cancer[ 7 , 8 ]. However, research indicates that endometrial cancer incidence remains elevated in ER-negative breast cancer patients who do not use tamoxifen. Women previously diagnosed with breast cancer b and later diagnosed with endometrial cancer have a higher susceptibility to develop type II cancer, and this tendency remains unaffected by tamoxifen exposure[ 9 – 11 ]. Additionally, TAM may not adversely affect the prognosis of EC after ER + breast cancer[ 12 , 13 ]. This implies that endometrial cancer subsequent to breast cancer may stem from intrinsic abnormalities, such as genetic factors and gene mutations[ 14 ]. Therefore, exploring shared biomarkers and potential mechanisms linked to breast cancer can enhance our comprehension of endometrial cancer. In this study, datasets from TCGA and ICGC were used to explore the correlation between gene expression disparities in EC and EC, as well as their potential impact on the diagnosis and prognosis of EC. We examined analyses of differential gene expression and weighted gene co-expression network analysis (WGCNA) to pinpoint Breast Cancer-related Genes (BCRGs). Subsequently, a Breast Cancer-related Genes Risk Score (BCRGRS) was developed for endometrial cancer patients, and its utility was assessed for survival evaluation and immunotherapy. The results indicate that BCRGRS shows promise as a prognostic biomarker for patients receiving immunotherapy, aiding in screening and treatment, providing a basis for exploring more effective personalized treatment options. 2 Materials and Methods 2.1 Dataset Download and Process RNA-Seq expression profiles and clinical data for BC were obtained from The Cancer Genome Atlas Program (TCGA), comprising 113 normal samples and 1118 tumor samples. Transcriptome and clinical data for EC were gathered from TCGA (35 normal samples and 554 EC tissue samples), designated as the training set. Similarly, data from the International Cancer Genome Consortium (ICGC) (23 normal samples and 513 EC tissue samples), served as the test set. All datasets underwent normalization using the "voom" function within the "limma" R package. 2.2 Identification of DEGs The screening of differentially expressed genes (DEGs) from the TCGA-BRCA and TCGA-UCEC datasets was facilitated using the "limma" R package, filtered according to the threshold of adjusted P value 1. The expression patterns of DEGs were visualized using the "ggplot2" R package for volcano plots and the "pheatmap" R package for heatmaps. 2.3 Weighted Gene Co-Expression Network Analysis Weighted Gene Co-Expression Network Analysis (WGCNA) clusters genes by their expression patterns, identifies functional modules, and pinpoints potential biomarkers or therapeutic targets through gene set correlations and associations with phenotypes. Our study utilized the "WGCNA" R package to construct gene co-expression networks for both BC and EC. Initially, over 60,000 genes were obtained from TCGA sequencing data. Observing the majority of these genes exhibited no differential expression across samples, so we opted to select the top 5,000 genes based on their variance for WGCNA analysis. Before the analysis commenced, the Hclust function in R was utilized for hierarchical clustering to identify and exclude outlier samples. Subsequently, the "pickSoftThreshold" function determined an optimal soft power β (ranging from 1 to 20), ensuring a scale-free network. Then, Pearson correlation analysis and the soft threshold β were employed to construct the adjacency matrix, which was transformed into a topological overlap matrix (TOM). The co-expression modules were determined through the gene hierarchical clustering tree. Finally, the clinically relevant modules were determined by calculating the module characteristic genes (ME) and their correlation with clinical features. For UCEC, the soft threshold β was set at 4, and for BRCA, it was set at 7. Other parameters included networkType=" unsigned ", minModuleSize=30, mergeCutHeight=0.25 and deepSplit=2. 2.4 Identification of shared genes and pathway enrichment Venn diagrams were utilized to analyze the overlap between DEGs and genes identified by WGCNA. Overlapping genes were considered to be breast cancer-related genes (BCRGs) shared with endometrial cancer. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to unravel the biological processes and pathways associated with these BCRGs. A significance level of P < 0.05 was applied for assessment. Table 1 Clinical pathological parameters of EC Characteristic Alive Dead p Missing N 457 91 Age (Q1, Q3) < 0.001 0.6 63 (56, 71) 67 (60, 74.5) BMI (Q1, Q3) 0.194 5.7 32.49 (26.62, 39.45) 31.23 (25.56, 36.61) Race (%) 0.82 5.8 Asian 18 (4.2) 2 (2.2) White 309 (72.4) 65 (73.1) Black or African American 90 (21.1) 19 (21.4) American Indian or Alaska Native 3 (0.7) 1 (1.1) Native Hawaiian or Other Pacific Islander 7 (1.6) 2 (2.2) Prior malignancy (%) 0.901 0 No 417 (91.2) 84 (92.3) Yes 40 (8.8) 7 (7.7) Stage (%) < 0.001 0 I 310 (67.8) 32 (35.2) II 43 (9.4) 9 (9.9) III 91 (19.9) 33 (36.2) IV 13 (2.9) 17 (18.7) Grade, n (%) < 0.001 3.5 G1 95 (21.5) 2 (2.3) G2 107 (24.2) 12 (13.8) G3 235 (53.2) 67 (77.0) High Grade 5 (1.1) 6 (6.9) MSI status (%) 0.051 2.4 MSS 259 (58.1) 64 (71.9) MSI_L 38 (8.5) 5 (5.6) MSI_H 149 (33.4) 20 (22.5) Pharmaceutical therapy (%) 0.005 4.4 No 270 (61.8) 39 (44.8) Yes 167 (38.2) 48 (55.2) Radiation therapy (%) 0.653 4.6 No 216 (49.5) 46 (52.9) Yes 220 (50.5) 41 (47.1) 2.5 The Prognostic Values of the shared breast cancer-related gene To ascertain the key genes linked to EC patients' prognosis, we searched the prognosis and clinicopathological data of 550 UCEC patients in the TCGA database. Clinical characteristics are presented in Table 1. Univariate Cox regression analysis identified prognostic-related BCRGs, and the impact of overall survival (OS) in EC patients was assessed through Kaplan-Meier survival analysis. Criteria for significance were set at p < 0.05. 2.6 Construction and validation of the breast cancer-related risk signature In the TCGA cohort, we used Lasso-Cox regression model through "glmnet" R package to identify optimal prognostic biomarkers among BCRGs. Through 10-fold cross-validation, 11 BCRGs with non-zero coefficients were selected. The TCGA dataset served as the training set, whereas the ICGC dataset was utilized as an independent test set. Subsequently, the regression coefficients obtained from multivariate Cox regression analysis of the 11 BCRGs in the training set were utilized to construct BCRGRS. The risk score formula was defined as follows: Risk score=∑ I Expression of gene (i) × Coefficient of gene (i) The expression level of gene (i) is the expression value of gene (i) in each patient, and the coefficient of gene (i) is the regression coefficient of gene (i). Patients in the TCGA-UCEC were categorized into low-risk and high-risk groups according to the median risk score. Kaplan-Meier analysis was then conducted to compare their overall survival (OS). To evaluate the accuracy of the Cox regression model, ROC curves were generated at 1, 3, and 5 years using the "survival", "survminer", and "timeROC" R packages, which was subsequently validated in the ICGC cohort. Histograms were used to depict the risk score along with other individual prognostic signatures. The performance of the prognostic prediction model was evaluated using the area under the ROC curve (AUC), calculated with the "pROC" R package, which was then compared with that of other individual prognostic biomarkers. Additionally, we combined clinical information with risk scores for analysis, and performed the analyses of univariate and multivariate Cox regression to assess if BCRGRS could function as an independent prognostic indicator. 2.7 Establishment of a nomogram nomogram integrating risk scores and clinicopathological features was constructed using the "rms" R package to predict the 1-, 3-, and 5-year survival rates of EC patients. Calibration curves were used to assess the alignment between predicted and observed survival outcomes. 2.8 Somatic Mutation Analysis Somatic copy number alterations (SCNA) for TCGA-UCEC were obtained using the "biolinks" R package, and mutation annotation format (MAF) analysis was performed using the "maftools" R package. OncoPrint maps were then generated to visualize the mutation frequency of the top 20 genes in each BCRGRS group. The TMB for each patient was computed based on the somatic mutation data. Stratified survival analysis was performed to investigate the correlation between TMB score and BCRGRS, considering the potential of TMB in predicting immunotherapy response. We utilized the cbioportal online tool (https://www.cbioportal.org) to obtain the microsatellite instability (MSI) status and four molecular subtypes of EC patients. Subsequently, we analyzed the disparities in MSI status and molecular subtyping between the two groups, as well as the variation in BCRGRS among MSI and molecular subtyping subgroups. 2.9 Immune Landscape Analysis The ESTIMATE method is frequently used to deduce the cellular composition of the tumor microenvironment by calculating the proportion of stromal and immune cells within tumor tissues. We calculated the stromal score, immune score, and ESTIMATE score with "ESTIMATE" R package for TCGA-UCEC samples. The single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltrating immune cell and immune-related pathways among the two risk groups. Additionally, we analyzed the variance in immune checkpoint genes levels. Subsequently, we utilized a series of predictors of immune checkpoint response, including the Tumor Immune Dysfunction and Exclusion (TIDE) score, Tumor Microenvironment (TME) score, and Immunophenotype Score (IPS), to evaluate the effectiveness of immunotherapy. TCIA (https://tcia.at/home) provides data on tumor-infiltrating lymphocyte (TIL) cell composition and the response of 20 types of solid cancers within the TCGA to checkpoint blockade immunotherapy. Samples of endometrial cancer (EC) retrieved from TICA provide a dependable indication of response to cytotoxic anti-CTLA-4 and anti-PD-1 antibodies through the Immunophenotype Score (IPS), wherein a heightened IPS score signifies enhanced immunogenicity. The TIDE score predicts immunotherapy response, derived from the primary mechanism of tumor immune evasion. The Mvigor210 cohort comprises clinical and gene expression profile data from patients with metastatic urothelial carcinoma who underwent treatment with anti-PD-L1 medications, sourced from the IMvigor210 CoreBiologies R package. the GSE91061 dataset contains information on patients with melanoma who underwent treatments involving anti-PD-1 and anti-CTLA4 therapies. Raw files from the IMvigor210 and GSE91061 datasets underwent conversion to transcripts per million (TPM) and log2-transformed. Patients from both cohorts were categorized into two groups according to their response to immunotherapy: progressive disease/stable disease (PD/SD) and partial response/complete response (PR/CR). The risk scores were computed, and its effect on the effectiveness and prognosis of PD-L1 inhibitors and CTLA4 inhibitors was assessed. 2.10 Drug sensitive analysis To further evaluation of BCRGRS's predictive capacity in EC treatment response, the sensitivity of each sample to chemotherapy was predicted using cancer drug sensitivity genomics (GDSC) (https://www.cancerrxgene.org/). we computed the half maximal inhibitory concentration (IC50) for conventional chemotherapy drugs and targeted therapy drugs in both two risk groups by the "pRRophetic" R package. Genes known to interact with drugs were obtained from the Drug Gene Interaction Database (DGIdb, https://dgidb.org/). The complex interaction network between key genes and drugs, referred to as the drug-gene interaction network, was visualized and analyzed utilizing Cytoscape (version 3.9.1). 2.11 Statistical analysis R software (version 4.2.2) was utilized for all statistical analyses in our study. The Wilcoxon test was utilized to calculate and compare the differences among normal and tumor tissues, as well as among BCRGRS groups. The log-rank test along with Kaplan-Meier curves were employed to assess differential survival time between the two risk groups. A significance level set at p < 0.05. 3 Results 3.1 Identification of DEGs Using the "limma" package, we found 4151 DEGs in TCGA-UCEC, comprising 2029 up-regulated and 2122 down-regulated. For TCGA-BRCA, there were 3953 DEGs, with 1847 genes up-regulated and 2106 genes down-regulated. Volcano plots depicted the expression patterns of these DEGs across normal and cancerous samples (Figure 1A, B). 1832 differentially expressed genes were detected from the overlap of UCEC and BRCA, including 924 up-regulated and 908 down-regulated (Figure 1C). 3.2 WGCNA network construction and module identification To identify the key modules most relevant to clinical characteristics, we performed WGCNA on the TCGA cohort. No samples were excluded from TCGA-UCEC and TCGA-BRCA based on outlier values determined through sample clustering. To validate the scale-free network, calculations were conducted for the scale-free fitting index and mean connectivity. The soft threshold β was 4 for UCEC and 7 for BRCA. The gene clustering trees are illustrated in Figure 2A and 2C. 12 gene modules were obtained from the co-expression network constructed. Among these, the yellow module exhibited the strongest positive correlated with EC (r = 0.55, P < 0.001), whereas the turquoise module displayed the most pronounced negative correlated with EC (r = -0.7, P < 0.001) (Figure 2B). Similarly, for breast cancer, the expression matrix was also divided into 12 modules, with the yellow module (r = 0.48, P < 0.001) and the turquoise module (r = -0.69, P < 0.001) showed the most significant associated with BC (Figure 2D). The overlapping regions between key modules in endometrial cancer and breast cancer were depicted using a Venn diagram, from which 531 essential genes were identified, including 250 positively correlated genes and 279 negatively correlated genes (Figure 2E). 3.3 Identification of shared genes and pathway enrichment analyses There were 366 shared genes overlapped between those identified through WGCNA and DEGs. These genes, associated with both endometrial and breast cancers, were defined as breast cancer-related genes (BCRGs), represent potential crosstalk between the two diseases (Figure 3A). Analyses of GO and KEGG enrichments were conducted on these 366 BCRGs to investigate shared regulatory pathways (Figure 3B, 3C). 3.4 Construction of BCRGs models related to EC prognosis To determine BCRGs linked to the prognosis of EC patients, we initially screened 142 differentially expressed BCRGs using univariate regression analysis (Figure 4A). Subsequently, 11 genes with predictor significance were selected from these BCRGs through LASSO regression analysis (Figure 4B, C), including ATAD2, CDKN2A, E2F1, GGH, MTHFD2, NPR1, NR3C1, PAMAR1, SRPX, STXBP1, and TTK. Combined with the regression coefficient (Figure 4D), the risk score of the BCRGs (BCRGRS) is formulated as follows: Risk Score=(-0.05867*ATAD2) + (0.08699*CDKN2A) + (0.12431*E2F1) + (0.22998*GGH) + (0.17093*MTHFD2) + (0.07313*NPR1) + (0.11242*NR3C1) + (-0.16649*PAMR1) + (0.15211*SRPX) + (0.10530*STXBP1）+(0.02786*TTK). Among them, ATAD2, CDKN2A, E2F1, GGH, MTHFD2, and TTK exhibited high expression levels in EC, while NPR1, NR3C1, PAMAR1, SRPX, and STXBP1 showed low expression levels (Figure 4E). To validate the universality of BCRGs, we utilized the ICGC cohort (Figure 4F). Kaplan-Meier analysis indicated that, with the exception of PAMAR1, the remaining 10 BCRGs were correlated with an adverse prognosis in EC (Figure 5A-K). 3.5 Validation of the BCRGRS risk model The 550 EC samples were stratified into the low-risk group (LRG) and the high-risk group (HRG), according to the median score derived from the risk scoring formula. HRG patients exhibited higher mortality rates and shorter survival times compared to LRG patients, indicating that the overall survival (OS) among two risk groups had a significant disparity. The heatmap depicted the differential expression levels of the 11 BCRGs across distinct risk subgroups (Figure 6A). Kaplan-Meier analysis revealed a markedly lower survival rate among HRG patients compared to LRG patients (Figure 6C, log-rank p < 0.001). The AUC for predicting the 1-, 3-, and 5- years OS was 0.749, 0.742, and 0.756, respectively (Figure 6E). To assess the robustness of the final model across different populations, 505 EC patients from the ICGC served as the validation set. Each patient's BCRGs were computed using the established formula, and subsequently categorized into LRG and HRG group. Compared with the HRG, the LRG exhibited better prognosis in the ICGC dataset (Figure 6B, D, F), affirming the high predictive effectiveness of our model for EC patient prognosis. 3.6 Clinical relevance of the BCRGs The forest plot illustrated the association between the expression levels of 11 BCRGs and OS (Figure 7A, B). In univariate regression analysis, all but one (PMAR1) of the 10 BCRGs were found to significantly enhance the risk of worse outcomes, of which some markers appeared statistically insignificant in multivariate Cox analysis (P>0.05), suggesting potential interactions with other markers affecting the outcome. The predictive model's efficacy was evaluated using the C-index of the regression model and the ROC curve. In comparison to various single-factor models, the combined model exhibited a superior C-index (Figure 7C). Similarly, the AUC of the combined model surpassed that of single genes in both TCGA and ICGC datasets, indicating superior prognostic prediction efficiency (Figure 7D, E). The above findings underscore that accurate prognostic predictions require the inclusion of these 11 BCRGs. Subsequently, we investigated the relationship between the two groups and clinical features (Figure8A, B). The analysis revealed associations between age, stage, grade, obesity, and BCRGRS (P< 0.05) (Figure8C). Given the significant association between BCRGRS and UCEC aggressiveness, the analyses univariate and multivariate regression were conducted to evaluate BCRGRS as an independent prognostic indicator. The results showed that age, stage, grade, and BCRGRS independently predicted the prognosis of EC patients. (Figure 8D, E). Additionally, elevated risk scores were associated with poorer survival outcomes across various ages, grades, and stages (Figure8F). From the above results, age, stage, grade, and risk score were identified as predictive signatures of EC patient survival rates, and a nomogram was developed to forecast survival probability at 1-, 3-, and 5- years intervals (Figure 9A). The C-index curve based on the change of different variables over time showed that the prediction model performed best compared with other clinical characteristics (Figure 9B). The accuracy of the prediction model is verified by the 1- 3-, and 5- years of Calibration curves (Figure 9C-E). The Time-ROC analysis revealed high prognostic ability of clinical characteristics and risk scores (Figure 9F-H), with the prediction model exhibiting high ROC values (1-year=0.850, 3-year=0.856, 5-year=0.884), indicating its high sensitivity and specificity in prognostic evaluation. 3.7 The profile of immune infiltration in the two BCRGs subgroups Increasing evidence suggests the pivotal role of infiltrating immune cells in shaping the tumor microenvironment (TME). We explored the relationship between the two risk groups and TME using the "ESTIMATE" R package. The result shows that the LRG exhibited higher stroma score, immune score, and estimated score, coupled with lower tumor purity (Figure 10A, B), suggesting elevated immune and stromal cell presence in the LRG, while the HRG contained more tumor cells, which explains why the prognosis of the LRG was better. Subsequently, the immune function and pathway analysis of the two subgroups were evaluated using the "ssGSEA" algorithm. For immune infiltrating cells, the LRG exhibited a significantly higher count of anti-tumor immune cells, including macrophages, neutrophils, CD8+ T cells, and T helper cells, TILs compared to the HRG. The immune function, such as C-C chemokine receptor (CCR), immune checkpoints, human leukocyte antigen (HLA), T cell co-inhibition, T cell co-stimulation, and type II interferon (IFN) responses were notably elevated in the LRG (Figure 10C, D). The levels of 31 immune checkpoint genes also showed variation in the two groups (Figure 10E). Immunotherapy markers commonly utilized in clinic such as PDCD1 and CTLA-4, which are prevalent in clinical trials, exhibited notably higher expression levels in the LRG, indicating a potential immunotherapeutic response in these patients. The immunogenic potential of the two groups was evaluated through Immune Phenotype Scoring (IPS) analysis. In the LRG group, CTLA4_negative_PD1_negative, CTLA4_positive_PD1_negative, and CTLA4_positive_PD1_positve scored higher (Figure 11A). HRG patients exhibited a greater immune escape potential in the TIDE score, suggesting potentially reduced efficacy of immune checkpoint inhibitor therapy (ICI) (Figure 11B, C). above findings imply that LRG patients may exhibit improved response to immunotherapy. Furthermore, we aimed to explore if BCRGRS could serve as predictor of immunotherapy response in EC. However, there is a lack of published datasets of EC patients receiving immunotherapy. Therefore, we included the urothelial carcinoma dataset treated with anti-PD-L1 treatment (IMvigor210) and the malignant melanoma dataset treated with anti-PD-L1 and anti-CTLA4 treatment (GSE91061) as external validation cohorts. The median value was employed to determine the optimal cutoff value of BCRGRS, dividing patients into high and low BCRGRS subgroups, respectively. Patients with the high BCRGRS group exhibited a poorer prognosis in comparison to those with the low BCRGRS group by Kaplan-Meier curves (Figure 12A, B). ROC analysis was subsequently conducted, yielding an area under the ROC curve of 0.645 in IMvigor210 and 0.807 in GSE91061 (Figure 12C, D). Concerning treatment response, patients with the low BCRGRS subtype exhibited a higher objective response rate than those with high BCRGRS subtype. Compared with patients with stable disease or progression (SD/PD), those with complete or partial remission (CR/PR) had significantly lower BCRGRS values, with CR/PR being notably higher in the low BCRGRS subtype than in the high BCRGRS subtype (30.9% vs 14.8%, Figure 12E). while the risk scores showed no significant differences between the CR/PR group and the SD/PD group in GSE91061, patients with the high BCRGRS subtype still demonstrate poor survival outcomes (Figure 12F). The KM curve showed survival times associated with different treatment responses (Figure 12G, H). assess the predictive capabilities of BCRGRS values for immunotherapy benefits (Figure 12I, J). In addition, the association between BCRGRS and various immune types, including IC and TC, was examined. BCRGRS levels in the IC2 subtype were found to be lower compared to those in IC0 and IC1. The BCRGRS of TC1 group was lower than that of the other two groups. The immune inflammatory subtype exhibited lower BCRGRS levels than both the immune desert and immune exclusion subtypes, indicating that the low BCRGRS subtypes responded better to immunotherapy than the high BCRGRS subtypes. This explains why EC patients with low BCRGRS values experienced better survival outcomes compared to those with higher BCRGRS values. Therefore, patients in the LRG group gained greater benefits from immunotherapy compared to those in the HRG group. 3.8 Mutation landscape associated with BCRGs risk scores We compared somatic mutations across two risk groups.TP53 (62%) mutations were more frequently in HRG patients (Figure 13A), whereas PTEN (89%), ARID1A (59%), and PIK3CA (54%) displayed higher mutation rates in LRG patients (Figure 13B). LRG exhibited higher TMB compared to the HRG (p = 0.025) (Figure 13C), and a negative linear correlation was observed among the BCRGs scores and TMB level. (R = -0.19, p <0.001, Figure 13D). MSI analysis showed that MSI-H patients had lower BCRGs scores compared to MSS patients, with a notably higher occurrence of MSI-H within the LRG (Figure 13E). Subsequent analysis based on four TCGA molecular classifications indicated statistically significant differences in BCRGs scores among groups (Figure 13F). 3.9 Drug sensitive analysis We analyzed the chemotherapy response in two patient groups, calculating the IC50 for each sample based on BCRGRS. Our findings indicate that HRG was generally more responsive to most drugs, except cisplatin, paclitaxel, and olaparib (tamoxifen P = 0.046, vincristine P < 0.001, gemcitabine P < 0.01, Niraparib P < 0.001, Talazoparib P < 0.001), while the LRG group was more sensitive to docetaxel (p< 0.001) (Figure 14A). T To identify potential therapeutic options, we conducted drug predictions using the DGIdb database, resulting in 197 drugs targeting five genes (CDKN2A, E2F1, NPR1, NR3C1, TTK), and subsequently constructed a prognostic gene-drug network (Figure 14B). Discussion Breast cancer (BC) and endometrial cancer (EC) are two common malignant tumors in women often arising from "unresistant" estrogen stimulation. Endocrine therapy, comprising both estrogen receptor inhibitors and aromatase inhibitors (AI), is widely administered to patients with hormone receptor-positive BC worldwide. Tamoxifen (TAM) reduces the risk of BC recurrence and contralateral BC, but there are some side effects, primarily its proliferative impact on the endometrium, leading to EC[15]. Studies indicate that, 5-year adjuvant TAM therapy increased the risk of EC by 2.4 times compared with patients without adjuvant therapy[16, 17]. Even after adjusting for confounding factors (including age, BMI, diabetes, hypertension, dyslipidemia, PCOS, and GnRH agonist therapy), the use of TAM after BC still increased the risk of EC by about 4 times. Additionally, the risk of EC rises with prolonged TAM therapy duration. Patients who used TAM for 10 years face a 1.5%-3.2% increased risk compared to 5-year users[18]. However, it has been reported that a persistently high EC incidence in HR-negative BC patients who did not use TAM[19, 20]. A genome-based analysis found that EC patients reveals no significant differences in EC occurrence between TAM-exposed and non-exposed patients[11]. Furthermore, EC patients with a prior BC history were characterized by higher grades and more type II cancers than estrogen-dependent type I cancers[19, 21, 22], implying factors beyond TAM contribute, possibly stemming from shared genetic backgrounds or mutation characteristics. In our study, we used TCGA and ICGC genome expression data to establish 11 gene prognostic features of BC-related EC patients, aiming to discover promising biomarkers and therapeutic targets for EC prognosis and treatment. Initially, differentially expressed BCRGs were identified in EC through differential analysis and WGCNA. Subsequently, the analyses of GO enrichment and KEGG pathway revealed their association with processes, including mitosis, nuclear division, chromosome segregation, and cell cycle regulation. DEGs were mainly enriched associated with the cell cycle, motor proteins, oocyte meiosis, and p53 signaling, suggesting their potential therapeutic implications in tumor division. 11 key genes were identified through survival analysis, LASSO, and Cox analysis, including ATAD2, CDKN2A, E2F1, GGH, MTHFD2, NPR1, NR3C1, PAMAR1, SRPX, STXBP1, and TTK. These genes showed significant associations with EC patient overall survival rates based on Kaplan-Meier analysis. ATAD2 is recognized as a gene responsive to estrogen and androgen in hormone-dependent cancer cells, functioning as a transcriptional co-regulator of ER and AR[23, 24]. In EC, its expression significantly increases compared to normal endometrium and precancerous lesions, and this elevation correlates with a poor prognosis[25]. TAD2 overexpression is closely associated with upregulation of genes linked to increased proliferation, indicating its role as either an upstream mediator or a co-factor in proliferation regulation. Similar regulatory effects are also observed in breast cancer[26]. CDKN2A functions as a negative regulator of the cell cycle by inhibiting cyclin-dependent kinases[27, 28]. Hypermethylation of CDKN2A is a common epigenetic aberration in various cancers, including BC and EC[29]. The E2F1 transcription factor family orchestrates the transition from G1 to S phase in the cell cycle by activating target genes through transcription[30, 31], playing a pivotal role cell proliferation regulation[32, 33]. Studies have demonstrated its direct correlation with unfavorable prognosis in breast cancer[34], ovarian cancer[35], and other cancer types. Studies have shown upregulation of E2F1 in EC tissues[36], and the mechanism may potentially linked to signaling pathways such as CDK4/RB/E2Fs axis[37]. Cell metabolism is upregulated in cancer cells to support tumor growth and metastasis, and GGH and MTHFD2 are both enzymes involved in folate metabolism. GGH's dysregulation affects DNA methylation and gene expression, impacting crucial biological pathways such as cell cycle regulation, development, and proliferation[38]. Elevated GGH expression has been observed in invasive breast cancer[39], ERG-negative prostate cancer[39] and colon cancer[40], compared with adjacent non-cancerous tissues, correlating with unfavorable prognoses and clinical outcomes. Additionally, Heightened expression of GGH can diminish the sensitivity of cancer cells to chemotherapeutic agents such as 5-fluorouracil, methotrexate, pemetrexed, and carboplatin[41, 42]. The one-carbon folate cycle is pivotal in cancer metabolism, facilitating the synthesis of nucleotides and amino acids that are essential for rapid cellular proliferation[43]. The one-carbon metabolic enzyme MTHFD2, crucial for nucleic acid synthesis, is encoded by the nucleus and operates within the mitochondria[44, 45]. While widely upregulated during embryogenesis, its expression in normal adult tissues is generally low or absent. MTHFD2 is markedly overexpressed in various tumors, and its expression level is associated with the progression of malignant tumors. Proposed mechanisms include P53 inactivation[46], oxidative stress[47, 48], anti-inflammatory immunity[49], and DNA damage repair[50, 51]. E Extensively studied in breast cancer, MTHFD2 is closely linked to its occurrence and poor prognosis[52-54]. its potential molecular mechanisms in EC remain unclear. Neuropilin-1 (NRP1), a transmembrane protein widely expressed in cancer cells, plays crucial roles in tumor proliferation, migration, and invasion[55, 56] by stimulating multiple growth factor receptors such as VEGF and EGF[55, 57]. NRP1 also modulates immune cell function within the tumor microenvironment, affecting the host's response to cancer[58]. Its expression in EC correlates with invasion ability and tumor grade[59], highlighting its potential as both a diagnostic and therapeutic target[60]. NR3C1, a key regulator of glucocorticoid action, is involved in several cellular processes, including tumor proliferation and differentiation, particularly when bound to glucocorticoids[61]. Extensive research has demonstrated that the upregulation of NR3C1 promotes tumor proliferation, metastasis, and drug resistance, such as triple-negative breast cancer, ovarian cancer[62, 63]. PAMR1, which contains a peptidase domain that contributes to muscle regeneration, was initially identified as downregulated in the muscles from mice with Duchenne muscular dystrophy (DMD). Recognized as a muscle protease vital for regeneration, PAMR1 is predominantly expressed in various tissues, including normal skeletal muscle and brain[64, 65]. In human malignant tumors, PAMR1 expression correlates with favorable cervical cancer prognoses[66]. PAMR1 has been shown to inhibit the growth of breast cancer cells and is frequently absent in breast cancer samples (20.8%-58.3%)[67, 68] . This absence has led to its classification as a potential tumor suppressor in breast cancer, often being suppressed due to promoter hypermethylation in breast cancer tissues[69]. However, the role of PAMAR1 in the occurrence and progression of EC remains unexplored. SRPX , a transmembrane protein composed of 464 amino acids and 3 sushi domains, was originally identified as a pathogenic gene in patients with X-linked retinoic syndrome[70]. It functions as a tumor suppressor gene with pro-apoptotic function, and has been observed to be downregulated in various human tumor cells and tissues[71] . Mouse gene knockout studies have further demonstrated its pro-apoptotic function[72]. Srpx knockout mice exhibit tumor development in around 30% of cases, encompassing lymphoma, lung cancer, and liver cancer[73]. Synapse binding protein 1 (STXBP1) primarily regulates the fusion of intracellular granular membranes and various exocytosis processes[74], i including vesicle fusion, initiation, docking, and membrane fusion. This protein is vital in eliminating tumor cells and facilitating granular cell membrane fusion[75]. Although exocytosis being prevalent in both normal and tumor cells, the expression and function of STXBP1 in tumor progression have been scarcely investigated. In lung adenocarcinoma, upregulated STXBP1 expression correlates with poor prognosis[76]. Threonine and tyrosine kinase (TTK), alternatively referred to as unipolar spindle 1 (Mps1), acts as a spindle assembly checkpoint, guaranteeing the precise segregation of chromosomes into daughter cells[77]. Apart from the testes and placenta, TTK is rarely detected in normal tissues[78]. However, high levels of TTK have been observed in various human malignancies, including breast cancer[79], ovarian cancer[80], pancreatic cancer[81] correlating with poor prognosis. Knockdown of TTK expression or treatment with TTK inhibitors can inhibit tumor growth by suppressing cell proliferation and invasion, leading to significant survival benefits[82-84]. In ovarian cancer, downregulation of TTK can sensitize cisplatin-resistant cells to cisplatin therapy[85], potentially positioning TTK as a therapeutic target in cancer treatment. Studies indicate that TTK expression is increased in EC, associated with poor prognosis, positively correlated with high TNM stage, and involved in immune infiltration. This suggests its promising role as a diagnostic biomarker for distinguishing EC tissue from normal tissue[86]. Additionally, TTK inhibitor (NTRC0066-0) has demonstrated significant inhibition of EC cells growth[87]. These findings underscore the close association of identified key genes with tumor occurrence and progression, primarily through cell cycle regulation, which is consistent with GO results. Nevertheless, additional research is necessary to clarify their specific mechanisms in modulating EC. Combining biomarkers in the risk model can improve predictive capacity and expedite the formulation of personalized treatment strategies, surpassing the efficacy of single clinical biomarkers. BCRGRS, constructed from these 11 genes, emerged as a significant independent prognostic indicator for OS. Utilizing both TCGA and ICGC datasets, BCRGRS categorized patients into HRG and LRG by median value. LRG patients exhibited higher survival rates, whereas those in the HRG group had poorer survival outcomes. Specifically, the median OS of patients in the LRG group significantly exceeded that of patients in the HRG group. Univariate and multivariate Cox regression analyses confirmed BCRGRS as an independent prognostic factor. Although the survival curves in the ICGC cohort potentially intersect at a 30% risk of death, this does not imply superior survival rates for 30% of patients in the LRG group compared to those in the HRG group. At the intersection, 6 patients in the LRG group were still alive, whereas only 4 remained in the HRG group. Thus, a 30% risk of death does not equate to 30% of patients deceased. As the last patient in the LRG group succumbed, the LRG curve dropped to 0, unavoidably intersecting with the HRG curve. Extension of follow-up duration, with surviving patients in the LRG group, might preclude curve intersection. Additionally, our data revealed higher BCRGs scores in EC patients aged over 65 years, obese individuals, and those with the increase of EC FIGO stage and tumor grade, BCRGs scores were also higher. A nomogram combining age, stage, grade, and BCRGRS to validate its strong ability for predict EC prognosis, i suggesting the potential utility of BCRGRS as a clinical biomarker. The tumor microenvironment (TME) plays a significant role in the occurrence, progression, and metastasis of malignant tumors. Comprising tumor cells, stromal cells, endothelial cells, immune cells, and extracellular matrix components produced by tumor-associated cells, the TME modulates immune responses, facilitating immune evasion and fostering tumor cell tolerance, ultimately impacting tumor pathogenesis. Identifying dependable prognostic indicators and immunotherapy targets is imperative in elucidating the dynamics of EC. Compared with other immune systems, the endometrial immune system exhibits distinctive characteristics, mainly manifested in two aspects: it prevents infection and defends against various bacteria and viruses, while also facilitating allogeneic embryo transplantation[88]. Upon comparing the immune cell infiltration and activation pathways between the LRG and HRG groups, we observed a general decrease in the infiltration of immune cells and a decrease in the activity of immune-related pathways in the HRG group. Extensive studies have demonstrated that substantial T cell infiltration, particularly by cytotoxic CD8 T cells, is associated with a favorable prognosis. CD8 T cells can eliminate tumor cells through the release of cytotoxic molecules such as granzyme and perforin[89, 90], and secrete IFN-γ to induce tumor ferroptosis[91], which holds prognostic value for EC[92, 93]. Furthermore, BCRGRS shows promise in guiding immunotherapy selection for EC patients. Studies indicates that the approval of immune checkpoint inhibitors, such as dostarlimab or pembrolizumab (PD-1 inhibitors), benefits approximately 20-30% of 20-30% of patients with advanced EC[94]. Our study revealed that BCRGRS and several immune checkpoint genes, including PD-L1 and CTLA-4, showed higher expression in the LRG group, suggesting potential benefits from anti-PD-1 and anti-CTLA-4 therapy for patients belonging to this group. TIDE predicts the response to immune checkpoint inhibitors (ICIs) by simulating two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high cytotoxic T lymphocyte (CTL) infiltration, and the prevention of T cell infiltration in tumors with low CTL levels[95]. A positive correlation exists between the TIDE score and the possibility of tumor immune evasion, indicating that patients with higher TIDE scores may not benefit from ICI therapy. In our study, the LRG group exhibited lower TIDE scores and higher T cell dysfunction scores, whereas the HRG group displayed higher TIDE scores and increased T cell rejection scores. Consequently, HRG patients are more prone to T cell dysfunction and rejection when receiving immunotherapy, possibly leading to immune evasion as a consequence of T cell rejection. These patients typically exhibit a poor response to ICIs. In contrast, patients may benefit more from ICIs treatment. Furthermore, analysis of four different Immune Phenotype Scores (IPS) in the TCIA database revealed higher sensitivity in the LRG group, indicating that these patients might derive greater benefits from PD-1 and CTLA-4 inhibitors. External validation datasets, including urothelial carcinoma patients treated with anti-PD-L1 and malignant melanoma patients treated with anti-PD-1/CTLA-4, further substantiated the effectiveness of BCRGRS in predicting immunotherapy responses. In both immunotherapy cohorts, HRG patients exhibited lower OS and a higher incidence of disease progression following immunotherapy. This evidence suggests that BCRGRS can be considered an effective biomarker to predict response to immunotherapy. n 2013, the TCGA Alliance initially categorized the prognosis of EC into four categories: POLE hypermutation, microsatellite instability hypermutation (MSIH), low copy number (CNL), and high copy number (CNH)[96]. Gene-based detection is increasingly pivotal in EC treatment, dividing the prognosis into various risk categories and guiding surgical and adjuvant therapies. In our analysis of gene mutations across various BCRGRS subgroups, missense variants emerged as the most prevalent, followed by nonsense variants and frameshift deletions. TP53 and PIK3CA mutations occurred more frequently in HRG, while PTEN, PIK3, and ARIDI mutations were more prevalent in LRG. TP53 mutations, the most common genetic events in cancer, are associated with malignant tumor invasion and poor prognosis[97, 98]. It affects the cancer cell cycle via the p53/TGFß signaling pathway. PIK3CA may promote tumor proliferation through the PI3K-AKT signaling pathway[99]. Consequently, HRG patients, who typically exhibit high levels of TP53 and PIK3CA mutations, face a worse prognosis compared to LRG patients with lower frequencies of these mutations, which is consistent with our findings. Some studies have demonstrated that tumor mutational burden (TMB) can reflect tumor neoantigen potential and is closely linked to DNA repair defects, predicting immunotherapy efficacy across various tumors. Patients with mismatch repair defects (dMMR) and high microsatellite instability (MSI-H) typically exhibit higher TMB[99, 100]. Treatment with immune checkpoint inhibitors often leads to improved response and survival benefits in patients with high TMB[101, 102]. Our study observed that BCRGRS scores were negatively correlated with TMB, with MSI-H patients having the lowest scores, aligning with our immune correlation analysis results. This suggests that LRG patients with EC combined with TMB-H and MSI-H may have a greater chance of responding positively to immunotherapy. Based on molecular typing system assessment, although there is no significant difference in POLE hypermutation scores and the three molecular subtypes, the proportion of POLE hypermutation with the best prognosis is higher than that of HRG, while the opposite is true for CNH with the worst prognosis. could function as an adjunctive tool for molecular typing in immunotherapy and as predictive markers, enhancing the accurate evaluation of EC patients' immune microenvironment and treatment effects. The main limitations of our study were as follows. Firstly, due to data limitations, our study cohorts are sourced from different public datasets, inevitably leading to intra-tumor or intra-patient tumor heterogeneity. Secondly, owing to time constraints, although we identified the survival impact of related genes shared between EC and BC in EC patients, there has been no experimental validation or clinical trials conducted to date. The underlying mechanisms behind these phenomena remain unclear, necessitating further experimental evidence. Thirdly, the robustness of the prognostic model still needs verification and explanation through large-scale prospective studies and functional mechanism experiments. Currently, more work needs to be done to identify common biomarkers focusing on endometrial cancer and breast cancer. Conclusions By conducting differential gene analysis and performing comprehensive analysis of BC and EC datasets by WGCNA, we identified 11 gene features with prognostic significance for EC. Based on the risk scores of the 11 genes, along with age, stage and grade, we constructed the BCRGRS model and proved its efficacy as a prognostic indicator for EC. The BCRGRS model offers novel insights for predicting EC survival and guiding treatment decisions. Declarations Ethics approval and consent to participate Not applicable Availability of data and materials The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article. Authors’ Contribution Junling Xu conceived and designed the present study. Hualing Zhang, Xiaochen Qin, Kaili Zhang and Tianjiao He acquire analysis and interpret the data. Xiaoyi Ma and Yun Su wrote the manuscript. Yanci Che check and revised the manuscript. All authors contributed to the article and approved the submitted version. Funding No funding was received for conducting this study. Competing interests The authors declare no competing interests. References M.C. Liu, A.B. Gardner, J.E. Wolford, K.S. 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Penault-Llorca, Implementing TMB measurement in clinical practice: considerations on a ssay requirements, ESMO Open, 4 e000442. T.A. Chan, M. Yarchoan, E. Jaffee, C. Swanton, S.A. Quezada, A. Stenzinger, S. Peters, Development of tumor mutation burden as an immunotherapy biomarker: ut ility for the oncology clinic, Ann Oncol, 30 44-56. D. Sha, Z. Jin, J. Budczies, K. Kluck, A. Stenzinger, F.A. Sinicrope, Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors, Cancer Discov, 10 (2020) 1808-1825. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4332299","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299959733,"identity":"dc5c840f-caca-4f56-a76e-24a7507548e5","order_by":0,"name":"Junling Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junling","middleName":"","lastName":"Xu","suffix":""},{"id":299959734,"identity":"ba75ad73-71e3-45b3-83a9-c81e7b2b0d49","order_by":1,"name":"Hualing 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1","display":"","copyAsset":false,"role":"figure","size":183739,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression of Genes Analysis. \u003cstrong\u003e(A, B)\u003c/strong\u003e Volcano plots displaying DEGs in TCGA-UCEC\u003cstrong\u003e(A) \u003c/strong\u003eand TCGA-BRCA\u003cstrong\u003e(B)\u003c/strong\u003e. \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram illustrating the common DEGs between UCEC and BRCA.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/e1318d82034177f713491990.jpg"},{"id":56278674,"identity":"dc5e8f4f-33d8-4186-a2c1-8eb4f083bef8","added_by":"auto","created_at":"2024-05-10 20:34:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372953,"visible":true,"origin":"","legend":"\u003cp\u003eModules associated with clinical characteristics of EC and BC were identified using WGCNA. \u003cstrong\u003e(A, C) \u003c/strong\u003eDepicts the cluster dendrograms for co-expression genes in EC\u003cstrong\u003e(A)\u003c/strong\u003e and BC\u003cstrong\u003e(C)\u003c/strong\u003e. \u003cstrong\u003e(B, D)\u003c/strong\u003e Display the heatmaps showing the relationships of modules to traits in EC\u003cstrong\u003e(B)\u003c/strong\u003e and BC\u003cstrong\u003e(D)\u003c/strong\u003e. \u003cstrong\u003e(E) \u003c/strong\u003eIllustrates the Venn diagram featuring the shared genes between the EC modules and BC modules.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/c430d944d2e234965de9fed5.jpg"},{"id":56278120,"identity":"df56dac7-ed9a-4292-8e93-ccd20a56f569","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293659,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of BCRGs\u003cstrong\u003e. (A) \u003c/strong\u003eVenn diagram of 366 key genes were determined through the intersection of key modules genes and DEGs. \u003cstrong\u003e(B) \u003c/strong\u003eGO enrichment analysis of biological process, cellular composition, and molecular function.\u003cstrong\u003e (C) \u003c/strong\u003eKEGG pathway analysis.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/1e01559b0a4148cfe1e4579e.jpg"},{"id":56278124,"identity":"b7a7e4d0-8451-422c-a36a-e6a3db5a3126","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":820773,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the BCRGRS in TCGA.\u003cstrong\u003e (A) \u003c/strong\u003eUnivariate regression analysis revealed 142 BCRGs linked to survival (p value\u0026lt;0.01). \u003cstrong\u003e(B) \u003c/strong\u003e10-fold cross-validation determine the confidence index at each lambda value via LASSO Cox regression.\u003cstrong\u003e (C) \u003c/strong\u003e11 genes determined by lasso algorithm, with vertical dotted lines indicating the optimal values of the minimum and the 1-SE criteria. \u003cstrong\u003e(D)\u003c/strong\u003e The coefficients of the 11 predictors in the risk model. \u003cstrong\u003e(E, F)\u003c/strong\u003e Expression levels of prognostic BCRGs in normal and EC samples from TCGA \u003cstrong\u003e(E)\u003c/strong\u003e and ICGC\u003cstrong\u003e (F)\u003c/strong\u003e. ns: not significant. *: p value \u0026lt; 0.05, **: p value \u0026lt; 0.01, ***: p value \u0026lt; 0.001, and ****: p value \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/e71719ed23278279fa6ff59c.jpg"},{"id":56278126,"identity":"5eb9b14c-1173-4073-ba04-5aa65ef1d079","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":252513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-K) \u003c/strong\u003eThe Kaplan–Meier curves of OS of 11-gene prognostic signature (p \u0026lt;0.01).\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/37ee8ce4e40fcd5e76eb0893.jpg"},{"id":56278676,"identity":"6508feab-5e9f-439c-98db-db43b91cbad8","added_by":"auto","created_at":"2024-05-10 20:34:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":614337,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluating the prognostic significance of the BCRGRS model among TCGA and ICGC. \u003cstrong\u003e(A, B)\u003c/strong\u003ePatients with two risk groups in TCGA \u003cstrong\u003e(A) \u003c/strong\u003eand ICGC \u003cstrong\u003e(B)\u003c/strong\u003e. Scatter diagram about the BCRGRS and survival status of patient, alongside a heatmap of 11 prognostic molecules. \u003cstrong\u003e(C, D)\u003c/strong\u003e The Kaplan–Meier curves of OS between two groups in the TCGA \u003cstrong\u003e(C)\u003c/strong\u003e and ICGC \u003cstrong\u003e(D)\u003c/strong\u003e. \u003cstrong\u003e(E, F)\u003c/strong\u003eTime-dependent ROC curves at 1, 3 and 5 years in the TCGA \u003cstrong\u003e(E)\u003c/strong\u003e and ICGC \u003cstrong\u003e(F)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/b954479af1938cf8159a4ac4.jpg"},{"id":56278128,"identity":"7855e3fc-8375-4f58-9622-36d478cb33f0","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":295321,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the BCRGRS. \u003cstrong\u003e(A, B) \u003c/strong\u003eForest plots of the relationships between the 11 prognostic molecules and overall survival (OS) in univariate regression\u003cstrong\u003e (A)\u003c/strong\u003e and multivariate regression analyses \u003cstrong\u003e(B)\u003c/strong\u003e. \u003cstrong\u003e(C)\u003c/strong\u003e The C-index of the BCRGRS and 11 single factors Cox models. \u003cstrong\u003e(D, E)\u003c/strong\u003e ROC analyses for the BCRGRS and 11 prognostic molecules\u003cstrong\u003e \u003c/strong\u003ein TCGA\u003cstrong\u003e(D)\u003c/strong\u003e and ICGC\u003cstrong\u003e(E)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/4672d18894c7cf345c7c9586.jpg"},{"id":56278121,"identity":"d300a87a-2b07-465d-b9d0-0edcb0c6cec7","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":598862,"visible":true,"origin":"","legend":"\u003cp\u003eClinical characteristics of distinct subgroups in EC. \u003cstrong\u003e(A)\u003c/strong\u003e The heatmap of clinicopathological parameters for two risk groups. \u003cstrong\u003e(B)\u003c/strong\u003e A Sankey plot outlines the relationships among the risk groups, clinicopathological parameters and survival status.\u003cstrong\u003e (C) \u003c/strong\u003eViolin plots for the associations between risk score and age, BMI, prior malignancy, grade, stage, pharmaceutical, radiation. \u003cstrong\u003e(D, E) \u003c/strong\u003eUnivariate and multivariate regression analyses evaluate the prognostic capacity of risk score and clinical features. \u003cstrong\u003e(F)\u003c/strong\u003e Stratified analyses of with age, grade, stage through Kaplan-Meier curves.\u003c/p\u003e","description":"","filename":"floatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/67787da624151c02f49af42a.jpg"},{"id":56278122,"identity":"355867d0-0dfa-4742-9367-544976d30a57","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":352868,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and examination of nomogram. \u003cstrong\u003e(A)\u003c/strong\u003e A nomogram designed to predict the 1-, 3-, and 5- years OS probabilities for EC patients.\u003cstrong\u003e (B) \u003c/strong\u003eA time-dependent C-index plot of the nomogram and other clinical characteristics.\u003cstrong\u003e (C-E)\u003c/strong\u003e 1-, 3-, and 5- years of calibration plots for the actual and predicted OS rates. \u003cstrong\u003e(F-H)\u003c/strong\u003eMulti-ROC analysis of the nomogram relative to other clinical factors at 1-, 3-, and 5- years.\u003c/p\u003e","description":"","filename":"floatimage9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/8fe9630d2bbcdcf5539292c9.jpg"},{"id":56278675,"identity":"60b10305-c78b-4727-8b50-bbc7aa26c93a","added_by":"auto","created_at":"2024-05-10 20:34:55","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":432656,"visible":true,"origin":"","legend":"\u003cp\u003eThe landscape of tumor immune microenvironment and the immune response to ICI therapy across EC subgroups in EC. \u003cstrong\u003e(A, B) \u003c/strong\u003eBox plots and scatter diagrams display the scores for stromal cells, immune cells, estimate, and tumor purity. \u003cstrong\u003e(C, D)\u003c/strong\u003e The difference in immune infiltrating cells and function calculated by ssGSEA. \u003cstrong\u003e(E) \u003c/strong\u003eDifferential expression of 31 immune checkpoint molecules was observed in the two prognostic groups. ns: not significant. *: p value \u0026lt; 0.05, **: p value \u0026lt; 0.01, ***: p value \u0026lt; 0.001, and ****: p value \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/3063b6875d66736d43d827e1.jpg"},{"id":56278129,"identity":"c93ef2d0-0583-493f-b47a-a957d56503f3","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":261036,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of immunotherapy response across high- and low- risk groups. \u003cstrong\u003e(A)\u003c/strong\u003e Violin plots for the IPS score. \u003cstrong\u003e(B, C)\u003c/strong\u003e Box plots and scatter diagrams of the scores for TIDE, T-cell exclusion and dysfunction.\u003c/p\u003e","description":"","filename":"floatimage11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/d2aa56b6516102931035979b.jpg"},{"id":56278127,"identity":"39e356d1-1626-4eed-b474-73226ea1e334","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":395784,"visible":true,"origin":"","legend":"\u003cp\u003eThe effectiveness of BCRGRS undergoinganti-PD-L1 and CTLA4 therapy in the IMvigor210 and GSE91061 cohorts. \u003cstrong\u003e(A, B)\u003c/strong\u003eKaplan-Meier curves depict OS for high and low BCRGRS subgroups. \u003cstrong\u003e(C, D) \u003c/strong\u003eROC analysis of BCRGR for the IMvigor210 and GSE91061 cohorts. \u003cstrong\u003e\u0026nbsp;(E, F)\u003c/strong\u003e Box plots and bar graphs illustrate the immunotherapy response rates across two BCRGRS subgroups in IMvigor210 and GSE91061 cohorts. \u003cstrong\u003e(G, H)\u003c/strong\u003e Kaplan–Meier curves of the different treatment responses in IMvigor210 and GSE91061 cohort. \u003cstrong\u003e(I, J)\u003c/strong\u003eTime-ROC analysis of BCRGRS on OS at 6-, 12-, and 18-month intervals in the IMvigor210 and GSE91061 cohorts. \u003cstrong\u003e(K) \u003c/strong\u003eDistribution of immune subtypes in different BCRGRS subgroups was illustrated by the violin plot.\u003c/p\u003e","description":"","filename":"floatimage12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/9c6764c9cf09977928839180.jpg"},{"id":56278125,"identity":"046823fb-d3f1-4af4-b4ea-d6a48d9cf106","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":526486,"visible":true,"origin":"","legend":"\u003cp\u003eMutation profiles and molecular characteristics among different subgroups. \u003cstrong\u003e(A, B)\u003c/strong\u003e The top 10 most frequently mutated genes in the EC samples of high- \u003cstrong\u003e(A)\u003c/strong\u003e and low- \u003cstrong\u003e(B)\u003c/strong\u003erisk groups. \u003cstrong\u003e(C, D)\u003c/strong\u003e Box plots and scatter diagrams compare the TMB score and BCRGRS in two groups. \u003cstrong\u003e(E)\u003c/strong\u003e Violin plot and Bar graph illustrate the correlation between risk scores and MSI status. \u003cstrong\u003e(F)\u003c/strong\u003eViolin plot and Bar graph showed the relationship between risk score and molecular subtypes.\u003c/p\u003e","description":"","filename":"floatimage13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/cd2f2a26b68f7aa9b964ff42.jpg"},{"id":56278130,"identity":"29a6620a-c297-4913-8c48-1f2b62048179","added_by":"auto","created_at":"2024-05-10 20:26:55","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":533131,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis. \u003cstrong\u003e(A)\u003c/strong\u003e IC50 for nine common chemotherapy drugs were compared between the two risk groups.\u003cstrong\u003e (B)\u003c/strong\u003e The network of drugs and prognostic genes.\u003c/p\u003e","description":"","filename":"floatimage14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/1da99c24a973cca67aa30342.jpg"},{"id":62346284,"identity":"e578a34a-83e6-4370-8506-3e6672be484a","added_by":"auto","created_at":"2024-08-13 07:25:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6846073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4332299/v1/5d72b0e5-8a21-45be-bd21-217c371e084b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of breast cancer-related gene prognostic signature in endometrial cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEndometrial cancer (EC) ranks as the most prevalent gynecologic malignancy in developed nations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to the escalating rates of obesity, both the incidence of EC and disease-related mortality are steadily rising[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Annually, more than 65,000 new cases are reported in the United States. In 2023, around 66,200 new cases of EC and 13,030 deaths were recorded. Histologically, EC is categorized into two types: estrogen-dependent (Type I) and non-estrogen-dependent (Type II), depending on grading, gene expression patterns, and histological type. Type I endometrioid adenocarcinoma, as the most common subtype, is mainly associated with excessive exposure to estrogen, especially estrogen not inhibited by progesterone[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, similar to endometrial cancer, estrogen significantly influences the onset and progression of breast cancer. Both endometrial cancer and breast cancer are classified as \"estrogen-dependent tumors\". Breast cancer (BC) is the predominant malignancy affecting women, with estimated 43.8\u0026nbsp;million cases reported globally over a five-year span[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A previous research demonstrated that approximately 10% of BC patients develop a secondary cancer within a decade post-initial diagnosis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Breast cancer encompasses diverse biomarkers, delineating various subtypes based on immunohistochemical expression, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Patients who test positive ER and PR often demonstrate positive reactions to endocrine therapies like tamoxifen (TAM), a drug widely utilized in treating ER-positive breast cancer. Tamoxifen displays anti-estrogenic properties in breast tissue, yet functions as an estrogen agonist within the endometrium. Increasing evidence suggests that among BC survivors, there is a substantially elevated risk of subsequent EC, and receiving tamoxifen treatment raises the likelihood of adverse prognosis in endometrial cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, research indicates that endometrial cancer incidence remains elevated in ER-negative breast cancer patients who do not use tamoxifen. Women previously diagnosed with breast cancer b and later diagnosed with endometrial cancer have a higher susceptibility to develop type II cancer, and this tendency remains unaffected by tamoxifen exposure[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, TAM may not adversely affect the prognosis of EC after ER\u0026thinsp;+\u0026thinsp;breast cancer[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This implies that endometrial cancer subsequent to breast cancer may stem from intrinsic abnormalities, such as genetic factors and gene mutations[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, exploring shared biomarkers and potential mechanisms linked to breast cancer can enhance our comprehension of endometrial cancer.\u003c/p\u003e \u003cp\u003eIn this study, datasets from TCGA and ICGC were used to explore the correlation between gene expression disparities in EC and EC, as well as their potential impact on the diagnosis and prognosis of EC. We examined analyses of differential gene expression and weighted gene co-expression network analysis (WGCNA) to pinpoint Breast Cancer-related Genes (BCRGs). Subsequently, a Breast Cancer-related Genes Risk Score (BCRGRS) was developed for endometrial cancer patients, and its utility was assessed for survival evaluation and immunotherapy. The results indicate that BCRGRS shows promise as a prognostic biomarker for patients receiving immunotherapy, aiding in screening and treatment, providing a basis for exploring more effective personalized treatment options.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Dataset Download and Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-Seq expression profiles and clinical data for BC were obtained from The Cancer Genome Atlas Program (TCGA), comprising 113 normal samples and 1118 tumor samples. Transcriptome and clinical data for EC were gathered from TCGA (35 normal samples and 554 EC tissue samples), designated as the training set. Similarly, data from the International Cancer Genome Consortium (ICGC) (23 normal samples and 513 EC tissue samples), served as the test set. All datasets underwent normalization using the \u0026quot;voom\u0026quot; function within the \u0026quot;limma\u0026quot; R package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Identification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe screening of differentially expressed genes (DEGs) from the TCGA-BRCA and TCGA-UCEC datasets was facilitated using the \u0026quot;limma\u0026quot; R package, filtered according to the threshold of adjusted P value \u0026lt; 0.05 and |log FC| \u0026gt; 1. The expression patterns of DEGs were visualized using the \u0026quot;ggplot2\u0026quot; R package for volcano plots and the \u0026quot;pheatmap\u0026quot; R package for heatmaps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Weighted Gene Co-Expression Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeighted Gene Co-Expression Network Analysis (WGCNA) clusters genes by their expression patterns, identifies functional modules, and pinpoints potential biomarkers or therapeutic targets through gene set correlations and associations with phenotypes. Our study utilized the \u0026quot;WGCNA\u0026quot; R package to construct gene co-expression networks for both BC and EC. Initially, over 60,000 genes were obtained from TCGA sequencing data. Observing the majority of these genes exhibited no differential expression across samples, so we opted to select the top 5,000 genes based on their variance for WGCNA analysis. Before the analysis commenced, the Hclust function in R was utilized for hierarchical clustering to identify and exclude outlier samples. Subsequently, the \u0026quot;pickSoftThreshold\u0026quot; function determined an optimal soft power \u0026beta; (ranging from 1 to 20), ensuring a scale-free network. Then, Pearson correlation analysis and the soft threshold \u0026beta; were employed to construct the adjacency matrix, which was transformed into a topological overlap matrix (TOM). The co-expression modules were determined through the gene hierarchical clustering tree. Finally, the clinically relevant modules were determined by calculating the module characteristic genes (ME) and their correlation with clinical features. For UCEC, the soft threshold \u0026beta; was set at 4, and for BRCA, it was set at 7. Other parameters included networkType=\u0026quot; unsigned \u0026quot;, minModuleSize=30, mergeCutHeight=0.25 and deepSplit=2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Identification of shared genes and pathway enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Venn diagrams were utilized to analyze the overlap between DEGs and genes identified by WGCNA. Overlapping genes were considered to be breast cancer-related genes (BCRGs) shared with endometrial cancer. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to unravel the biological processes and pathways associated with these BCRGs. A significance level of P \u0026lt; 0.05 was applied for assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Clinical pathological parameters of EC\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDead\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Q1, Q3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e63 (56, 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e67 (60, 74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (Q1, Q3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e32.49 (26.62, 39.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e31.23 (25.56, 36.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e18 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e309 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e65 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e90 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e19 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eNative Hawaiian or Other Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e7 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrior malignancy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e417 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e84 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e40 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e7 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e310 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e32 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e43 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e9 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e91 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e33 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e13 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e17 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e95 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e107 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e12 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e235 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e67 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eHigh Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSI status (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e259 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e64 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eMSI_L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e38 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e5 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eMSI_H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e149 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e20 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmaceutical therapy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e270 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e39 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e167 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e48 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation therapy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e216 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e46 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.859399684044234%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e220 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.85308056872038%\" valign=\"top\"\u003e\n \u003cp\u003e41 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.007898894154819%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 The Prognostic Values of the shared breast cancer-related gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To ascertain the key genes linked to EC patients\u0026apos; prognosis, we searched the prognosis and clinicopathological data of 550 UCEC patients in the TCGA database. Clinical characteristics are presented in Table 1. Univariate Cox regression analysis identified prognostic-related BCRGs, and the impact of overall survival (OS) in EC patients was assessed through Kaplan-Meier survival analysis. Criteria for significance were set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Construction and validation of the breast cancer-related risk signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the TCGA cohort, we used Lasso-Cox regression model through \u0026quot;glmnet\u0026quot; R package to identify optimal prognostic biomarkers among BCRGs. Through 10-fold cross-validation, 11 BCRGs with non-zero coefficients were selected. The TCGA dataset served as the training set, whereas the ICGC dataset was utilized as an independent test set. Subsequently, the regression coefficients obtained from multivariate Cox regression analysis of the 11 BCRGs in the training set were utilized to construct BCRGRS. The risk score formula was defined as follows:\u003c/p\u003e\n\u003cp\u003eRisk score=\u0026sum;\u003csub\u003eI\u003c/sub\u003e Expression of gene (i) \u0026times; Coefficient of gene (i)\u003c/p\u003e\n\u003cp\u003eThe expression level of gene (i) is the expression value of gene (i) in each patient, and the coefficient of gene (i) is the regression coefficient of gene (i). Patients in the TCGA-UCEC were categorized into low-risk and high-risk groups according to the median risk score. Kaplan-Meier analysis was then conducted to compare their overall survival (OS). To evaluate the accuracy of the Cox regression model, ROC curves were generated at 1, 3, and 5 years using the \u0026quot;survival\u0026quot;, \u0026quot;survminer\u0026quot;, and \u0026quot;timeROC\u0026quot; R packages, which was subsequently validated in the ICGC cohort. Histograms were used to depict the risk score along with other individual prognostic signatures. The performance of the prognostic prediction model was evaluated using the area under the ROC curve (AUC), calculated with the \u0026quot;pROC\u0026quot; R package, which was then compared with that of other individual prognostic biomarkers. Additionally, we combined clinical information with risk scores for analysis, and performed the analyses of univariate and multivariate Cox regression to assess if BCRGRS could function as an independent prognostic indicator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Establishment of a nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enomogram integrating risk scores and clinicopathological features was constructed using the \u0026quot;rms\u0026quot; R package to predict the 1-, 3-, and 5-year survival rates of EC patients. Calibration curves were used to assess the alignment between predicted and observed survival outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Somatic Mutation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSomatic copy number alterations (SCNA) for TCGA-UCEC were obtained using the \u0026quot;biolinks\u0026quot; R package, and mutation annotation format (MAF) analysis was performed using the \u0026quot;maftools\u0026quot; R package. OncoPrint maps were then generated to visualize the mutation frequency of the top 20 genes in each BCRGRS group. The TMB for each patient was computed based on the somatic mutation data. Stratified survival analysis was performed to investigate the correlation between TMB score and BCRGRS, considering the potential of TMB in predicting immunotherapy response. \u0026nbsp;We utilized the cbioportal online tool (https://www.cbioportal.org) to obtain the microsatellite instability (MSI) status and four molecular subtypes of EC patients. Subsequently, we analyzed the disparities in MSI status and molecular subtyping between the two groups, as well as the variation in BCRGRS among MSI and molecular subtyping subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Immune Landscape Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ESTIMATE method is frequently used to deduce the cellular composition of the tumor microenvironment by calculating the proportion of stromal and immune cells within tumor tissues. We calculated the stromal score, immune score, and ESTIMATE score with \u0026quot;ESTIMATE\u0026quot; R package for TCGA-UCEC samples. The single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltrating immune cell and immune-related pathways among the two risk groups. Additionally, we analyzed the variance in immune checkpoint genes levels. Subsequently, we utilized a series of predictors of immune checkpoint response, including the Tumor Immune Dysfunction and Exclusion (TIDE) score, Tumor Microenvironment (TME) score, and Immunophenotype Score (IPS), to evaluate the effectiveness of immunotherapy. TCIA (https://tcia.at/home) provides data on tumor-infiltrating lymphocyte (TIL) cell composition and the response of 20 types of solid cancers within the TCGA to checkpoint blockade immunotherapy. Samples of endometrial cancer (EC) retrieved from TICA provide a dependable indication of response to cytotoxic anti-CTLA-4 and anti-PD-1 antibodies through the Immunophenotype Score (IPS), wherein a heightened IPS score signifies enhanced immunogenicity. The TIDE score predicts immunotherapy response, derived from the primary mechanism of tumor immune evasion. The Mvigor210 cohort comprises clinical and gene expression profile data from patients with metastatic urothelial carcinoma who underwent treatment with anti-PD-L1 medications, sourced from the IMvigor210 CoreBiologies R package. the GSE91061 dataset contains information on patients with melanoma who underwent treatments involving anti-PD-1 and anti-CTLA4 therapies. Raw files from the IMvigor210 and GSE91061 datasets underwent conversion to transcripts per million (TPM) and log2-transformed. Patients from both cohorts were categorized into two groups according to their response to immunotherapy: progressive disease/stable disease (PD/SD) and partial response/complete response (PR/CR). The risk scores were computed, and its effect on the effectiveness and prognosis of PD-L1 inhibitors and CTLA4 inhibitors was assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Drug sensitive analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluation of BCRGRS\u0026apos;s predictive capacity in EC treatment response, the sensitivity of each sample to chemotherapy was predicted using cancer drug sensitivity genomics (GDSC) (https://www.cancerrxgene.org/). we computed the half maximal inhibitory concentration (IC50) for conventional chemotherapy drugs and targeted therapy drugs in both two risk groups by the \u0026quot;pRRophetic\u0026quot; R package. Genes known to interact with drugs were obtained from the Drug Gene Interaction Database (DGIdb, https://dgidb.org/). The complex interaction network between key genes and drugs, referred to as the drug-gene interaction network, was visualized and analyzed utilizing Cytoscape (version 3.9.1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;R software (version 4.2.2) was utilized for all statistical analyses in our study. The Wilcoxon test was utilized to calculate and compare the differences among normal and tumor tissues, as well as among BCRGRS groups. The log-rank test along with Kaplan-Meier curves were employed to assess differential survival time between the two risk groups. A significance level set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Using the \u0026quot;limma\u0026quot; package, we found 4151 DEGs in TCGA-UCEC, comprising 2029 up-regulated and 2122 down-regulated. For TCGA-BRCA, there were 3953 DEGs, with 1847 genes up-regulated and 2106 genes down-regulated. Volcano plots depicted the expression patterns of these DEGs across normal and cancerous samples (Figure 1A, B). 1832 differentially expressed genes were detected from the overlap of UCEC and BRCA, including 924 up-regulated and 908 down-regulated (Figure 1C). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 WGCNA network construction and module identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To identify the key modules most relevant to clinical characteristics, we performed WGCNA on the TCGA cohort. No samples were excluded from TCGA-UCEC and TCGA-BRCA based on outlier values determined through sample clustering. To validate the scale-free network, calculations were conducted for the scale-free fitting index and mean connectivity. The soft threshold \u0026beta; was 4 for UCEC and 7 for BRCA. The gene clustering trees are illustrated in Figure 2A and 2C. 12 gene modules were obtained from the co-expression network constructed. Among these, the yellow module exhibited the strongest positive correlated with EC (r = 0.55, P \u0026lt; 0.001), whereas the turquoise module displayed the most pronounced negative correlated with EC (r = -0.7, P \u0026lt; 0.001) (Figure 2B). Similarly, for breast cancer, the expression matrix was also divided into 12 modules, with the yellow module (r = 0.48, P \u0026lt; 0.001) and the turquoise module (r = -0.69, P \u0026lt; 0.001) showed the most significant associated with BC (Figure 2D). The overlapping regions between key modules in endometrial cancer and breast cancer were depicted using a Venn diagram, from which 531 essential genes were identified, including 250 positively correlated genes and 279 negatively correlated genes (Figure 2E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Identification of shared genes and pathway enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There were 366 shared genes overlapped between those identified through WGCNA and DEGs. These genes, associated with both endometrial and breast cancers, were defined as breast cancer-related genes (BCRGs), represent potential crosstalk between the two diseases (Figure 3A). Analyses of GO and KEGG enrichments were conducted on these 366 BCRGs to investigate shared regulatory pathways (Figure 3B, 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Construction of BCRGs models related to EC prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To determine BCRGs linked to the prognosis of EC patients, we initially screened 142 differentially expressed BCRGs using univariate regression analysis (Figure 4A). Subsequently, 11 genes with predictor significance were selected from these BCRGs through LASSO regression analysis (Figure 4B, C), including ATAD2, CDKN2A, E2F1, GGH, MTHFD2, NPR1, NR3C1, PAMAR1, SRPX, STXBP1, and TTK. Combined with the regression coefficient (Figure 4D), the risk score of the BCRGs (BCRGRS) is formulated as follows: Risk Score=(-0.05867*ATAD2) + (0.08699*CDKN2A) + (0.12431*E2F1) + (0.22998*GGH) + (0.17093*MTHFD2) + (0.07313*NPR1) + (0.11242*NR3C1) + (-0.16649*PAMR1) + (0.15211*SRPX) + (0.10530*STXBP1）+(0.02786*TTK). Among them, ATAD2, CDKN2A, E2F1, GGH, MTHFD2, and TTK exhibited high expression levels in EC, while NPR1, NR3C1, PAMAR1, SRPX, and STXBP1 showed low expression levels (Figure 4E). To validate the universality of BCRGs, we utilized the ICGC cohort (Figure 4F). \u0026nbsp;Kaplan-Meier analysis indicated that, with the exception of PAMAR1, the remaining 10 BCRGs were correlated with an adverse prognosis in EC (Figure 5A-K).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Validation of the BCRGRS risk model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 550 EC samples were stratified into the low-risk group (LRG) and the high-risk group (HRG), according to the median score derived from the risk scoring formula. HRG patients exhibited higher mortality rates and shorter survival times compared to LRG patients, indicating that the overall survival (OS) among two risk groups had a significant disparity. The heatmap depicted the differential expression levels of the 11 BCRGs across distinct risk subgroups (Figure 6A). Kaplan-Meier analysis revealed a markedly lower survival rate among HRG patients compared to LRG patients (Figure 6C, log-rank p \u0026lt; 0.001). The AUC for predicting the 1-, 3-, and 5- years OS was 0.749, 0.742, and 0.756, respectively (Figure 6E). To assess the robustness of the final model across different populations, 505 EC patients from the ICGC served as the validation set. Each patient\u0026apos;s BCRGs were computed using the established formula, and subsequently categorized into LRG and HRG group. Compared with the HRG, the LRG exhibited better prognosis in the ICGC dataset (Figure 6B, D, F), affirming the high predictive effectiveness of our model for EC patient prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Clinical relevance of the BCRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe forest plot illustrated the association between the expression levels of 11 BCRGs and OS (Figure 7A, B). In univariate regression analysis, all but one (PMAR1) of the 10 BCRGs were found to significantly enhance the risk of worse outcomes, of which some markers appeared statistically insignificant in multivariate Cox analysis (P\u0026gt;0.05), suggesting potential interactions with other markers affecting the outcome. The predictive model\u0026apos;s efficacy was evaluated using the C-index of the regression model and the ROC curve. In comparison to various single-factor models, the combined model exhibited a superior C-index (Figure 7C). Similarly, the AUC of the combined model surpassed that of single genes in both TCGA and ICGC datasets, indicating superior prognostic prediction efficiency (Figure 7D, E). The above findings underscore that accurate prognostic predictions require the inclusion of these 11 BCRGs. Subsequently, we investigated the relationship between the two groups and clinical features (Figure8A, B). The analysis revealed associations between age, stage, grade, obesity, and BCRGRS (P\u0026lt; 0.05) (Figure8C). Given the significant association between BCRGRS and UCEC aggressiveness, the analyses univariate and multivariate regression were conducted to evaluate BCRGRS as an independent prognostic indicator. The results showed that age, stage, grade, and BCRGRS independently predicted the prognosis of EC patients. (Figure 8D, E). Additionally, elevated risk scores were associated with poorer survival outcomes across various ages, grades, and stages (Figure8F).\u003c/p\u003e\n\u003cp\u003eFrom the above results, age, stage, grade, and risk score were identified as predictive signatures of EC patient survival rates, and a nomogram was developed to forecast survival probability at 1-, 3-, and 5- years intervals (Figure 9A). The C-index curve based on the change of different variables over time showed that the prediction model performed best compared with other clinical characteristics (Figure 9B). The accuracy of the prediction model is verified by the 1- 3-, and 5- years of Calibration curves (Figure 9C-E). The Time-ROC analysis revealed high prognostic ability of clinical characteristics and risk scores (Figure 9F-H), with the prediction model exhibiting high ROC values (1-year=0.850, 3-year=0.856, 5-year=0.884), indicating its high sensitivity and specificity in prognostic evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 The profile of immune infiltration in the two BCRGs subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreasing evidence suggests the pivotal role of infiltrating immune cells in shaping the tumor microenvironment (TME). We explored the relationship between the two risk groups and TME using the \u0026quot;ESTIMATE\u0026quot; R package. The result shows that the LRG exhibited higher stroma score, immune score, and estimated score, coupled with lower tumor purity (Figure 10A, B), suggesting elevated immune and stromal cell presence in the LRG, while the HRG contained more tumor cells, which explains why the prognosis of the LRG was better. Subsequently, the immune function and pathway analysis of the two subgroups were evaluated using the \u0026quot;ssGSEA\u0026quot; algorithm. For immune infiltrating cells, the LRG exhibited a significantly higher count of anti-tumor immune cells, including macrophages, neutrophils, CD8+ T cells, and T helper cells, TILs compared to the HRG. The immune function, such as C-C chemokine receptor (CCR), immune checkpoints, human leukocyte antigen (HLA), T cell co-inhibition, T cell co-stimulation, and type II interferon (IFN) responses were notably elevated in the LRG (Figure 10C, D). The levels of 31 immune checkpoint genes also showed variation in the two groups (Figure 10E). Immunotherapy markers commonly utilized in clinic such as PDCD1 and CTLA-4, which are prevalent in clinical trials, exhibited notably higher expression levels in the LRG, indicating a potential immunotherapeutic response in these patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe immunogenic potential of the two groups was evaluated through Immune Phenotype Scoring (IPS) analysis. In the LRG group, CTLA4_negative_PD1_negative, CTLA4_positive_PD1_negative, and CTLA4_positive_PD1_positve scored higher (Figure 11A). HRG patients exhibited a greater immune escape potential in the TIDE score, suggesting potentially reduced efficacy of immune checkpoint inhibitor therapy (ICI) (Figure 11B, C). above findings imply that LRG patients may exhibit improved response to immunotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we aimed to explore if BCRGRS could serve as predictor of immunotherapy response in EC. However, there is a lack of published datasets of EC patients receiving immunotherapy. Therefore, we included the urothelial carcinoma dataset treated with anti-PD-L1 treatment (IMvigor210) and the malignant melanoma dataset treated with anti-PD-L1 and anti-CTLA4 treatment (GSE91061) as external validation cohorts. The median value was employed to determine the optimal cutoff value of BCRGRS, dividing patients into high and low BCRGRS subgroups, respectively. Patients with the high BCRGRS group exhibited a poorer prognosis in comparison to those with the low BCRGRS group by Kaplan-Meier curves (Figure 12A, B). ROC analysis was subsequently conducted, yielding an area under the ROC curve of 0.645 in IMvigor210 and 0.807 in GSE91061 (Figure 12C, D). Concerning treatment response, patients with the low BCRGRS subtype exhibited a higher objective response rate than those with high BCRGRS subtype. Compared with patients with stable disease or progression (SD/PD), those with complete or partial remission (CR/PR) had significantly lower BCRGRS values, with CR/PR being notably higher in the low BCRGRS subtype than in the high BCRGRS subtype (30.9% vs 14.8%, Figure 12E). while the risk scores showed no significant differences between the CR/PR group and the SD/PD group in GSE91061, patients with the high BCRGRS subtype still demonstrate poor survival outcomes (Figure 12F). The KM curve showed survival times associated with different treatment responses (Figure 12G, H). assess the predictive capabilities of BCRGRS values for immunotherapy benefits (Figure 12I, J). In addition, the association between BCRGRS and various immune types, including IC and TC, was examined. BCRGRS levels in the IC2 subtype were found to be lower compared to those in IC0 and IC1. The BCRGRS of TC1 group was lower than that of the other two groups. The immune inflammatory subtype exhibited lower BCRGRS levels than both the immune desert and immune exclusion subtypes, indicating that the low BCRGRS subtypes responded better to immunotherapy than the high BCRGRS subtypes. This explains why EC patients with low BCRGRS values experienced better survival outcomes compared to those with higher BCRGRS values. Therefore, patients in the LRG group gained greater benefits from immunotherapy compared to those in the HRG group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Mutation landscape associated with BCRGs risk scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We compared somatic mutations across two risk groups.TP53 (62%) mutations were more frequently in HRG patients (Figure 13A), whereas PTEN (89%), ARID1A (59%), and PIK3CA (54%) displayed higher mutation rates in LRG patients (Figure 13B). LRG exhibited higher TMB compared to the HRG (p = 0.025) (Figure 13C), and a negative linear correlation was observed among the BCRGs scores and TMB level. (R = -0.19, p \u0026lt;0.001, Figure 13D). MSI analysis showed that MSI-H patients had lower BCRGs scores compared to MSS patients, with a notably higher occurrence of MSI-H within the LRG (Figure 13E). Subsequent analysis based on four TCGA molecular classifications indicated statistically significant differences in BCRGs scores among groups (Figure 13F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Drug sensitive analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the chemotherapy response in two patient groups, calculating the IC50 for each sample based on BCRGRS. Our findings indicate that HRG was generally more responsive to most drugs, except cisplatin, paclitaxel, and olaparib (tamoxifen P = 0.046, vincristine P \u0026lt; 0.001, gemcitabine P \u0026lt; 0.01, Niraparib P \u0026lt; 0.001, Talazoparib P \u0026lt; 0.001), while the LRG group was more sensitive to docetaxel (p\u0026lt; 0.001) (Figure 14A). T To identify potential therapeutic options, we conducted drug predictions using the DGIdb database, resulting in 197 drugs targeting five genes (CDKN2A, E2F1, NPR1, NR3C1, TTK), and subsequently constructed a prognostic gene-drug network (Figure 14B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreast cancer (BC) and endometrial cancer (EC) are two common malignant tumors in women often arising from \u0026quot;unresistant\u0026quot; estrogen stimulation. Endocrine therapy, comprising both estrogen receptor inhibitors and aromatase inhibitors (AI), is widely administered to patients with hormone receptor-positive BC worldwide. Tamoxifen (TAM) reduces the risk of BC recurrence and contralateral BC, but there are some side effects, primarily its proliferative impact on the endometrium, leading to EC[15]. Studies indicate that, 5-year adjuvant TAM therapy increased the risk of EC by 2.4 times compared with patients without adjuvant therapy[16, 17]. Even after adjusting for confounding factors (including age, BMI, diabetes, hypertension, dyslipidemia, PCOS, and GnRH agonist therapy), the use of TAM after BC still increased the risk of EC by about 4 times. Additionally, the risk of EC rises with prolonged TAM therapy duration. Patients who used TAM for 10 years face a 1.5%-3.2% increased risk compared to 5-year users[18]. However, it has been reported that a persistently high EC incidence in HR-negative BC patients who did not use TAM[19, 20]. A genome-based analysis found that EC patients reveals no significant differences in EC occurrence between TAM-exposed and non-exposed patients[11]. Furthermore, EC patients with a prior BC history were characterized by higher grades and more type II cancers than estrogen-dependent type I cancers[19, 21, 22], implying factors beyond TAM contribute, possibly stemming from shared genetic backgrounds or mutation characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, we used TCGA and ICGC genome expression data to establish 11 gene prognostic features of BC-related EC patients, aiming to discover promising biomarkers and therapeutic targets for EC prognosis and treatment. Initially, differentially expressed BCRGs were identified in EC through differential analysis and WGCNA. Subsequently, the analyses of GO enrichment and KEGG pathway revealed their association with processes, including mitosis, nuclear division, chromosome segregation, and cell cycle regulation. DEGs were mainly enriched associated with the cell cycle, motor proteins, oocyte meiosis, and p53 signaling, suggesting their potential therapeutic implications in tumor division. 11 key genes were identified through survival analysis, LASSO, and Cox analysis, including ATAD2, CDKN2A, E2F1, GGH, MTHFD2, NPR1, NR3C1, PAMAR1, SRPX, STXBP1, and TTK. These genes showed significant associations with EC patient overall survival rates based on Kaplan-Meier analysis. ATAD2 is recognized as a gene responsive to estrogen and androgen in hormone-dependent cancer cells, functioning as a transcriptional co-regulator of ER and AR[23, 24]. In EC, its expression significantly increases compared to normal endometrium and precancerous lesions, and this elevation correlates with a poor prognosis[25]. TAD2 overexpression is closely associated with upregulation of genes linked to increased proliferation, indicating its role as either an upstream mediator or a co-factor in proliferation regulation. Similar regulatory effects are also observed in breast cancer[26]. CDKN2A functions as a negative regulator of the cell cycle by inhibiting cyclin-dependent kinases[27, 28]. Hypermethylation of CDKN2A is a common epigenetic aberration in various cancers, including BC and EC[29]. The E2F1 transcription factor family orchestrates the transition from G1 to S phase in the cell cycle by activating target genes through transcription[30, 31], playing a pivotal role cell proliferation regulation[32, 33]. Studies have demonstrated its direct correlation with unfavorable prognosis in breast cancer[34], ovarian cancer[35], and other cancer types. Studies have shown upregulation of E2F1 in EC tissues[36], and the mechanism may potentially linked to signaling pathways such as CDK4/RB/E2Fs axis[37]. Cell metabolism is upregulated in cancer cells to support tumor growth and metastasis, and GGH and MTHFD2 are both enzymes involved in folate metabolism. GGH\u0026apos;s dysregulation affects DNA methylation and gene expression, impacting crucial biological pathways such as cell cycle regulation, development, and proliferation[38]. Elevated GGH expression has been observed in invasive breast cancer[39], ERG-negative prostate cancer[39]\u0026nbsp;and colon cancer[40], compared with adjacent non-cancerous tissues, correlating with unfavorable prognoses and clinical outcomes. Additionally, Heightened expression of GGH can diminish the sensitivity of cancer cells to chemotherapeutic agents such as 5-fluorouracil, methotrexate, pemetrexed, and carboplatin[41, 42]. The one-carbon folate cycle is pivotal in cancer metabolism, facilitating the synthesis of nucleotides and amino acids that are essential for rapid cellular proliferation[43]. The one-carbon metabolic enzyme MTHFD2, crucial for nucleic acid synthesis, is encoded by the nucleus and operates within the mitochondria[44, 45]. While widely upregulated during embryogenesis, its expression in normal adult tissues is generally low or absent. MTHFD2 is markedly overexpressed in various tumors, and its expression level is associated with the progression of malignant tumors. Proposed mechanisms include P53 inactivation[46], oxidative stress[47, 48], anti-inflammatory immunity[49], and DNA damage repair[50, 51]. E Extensively studied in breast cancer, MTHFD2 is closely linked to its occurrence and poor prognosis[52-54]. its potential molecular mechanisms in EC remain unclear. Neuropilin-1 (NRP1), a transmembrane protein widely expressed in cancer cells, plays crucial roles in tumor proliferation, migration, and invasion[55, 56] by stimulating multiple growth factor receptors such as VEGF and EGF[55, 57]. NRP1 also modulates immune cell function within the tumor microenvironment, affecting the host\u0026apos;s response to cancer[58]. Its expression in EC correlates with invasion ability and tumor grade[59], highlighting its potential as both a diagnostic and therapeutic target[60]. NR3C1, a key regulator of glucocorticoid action, is involved in several cellular processes, including tumor proliferation and differentiation, particularly when bound to glucocorticoids[61]. Extensive research has demonstrated that the upregulation of NR3C1 promotes tumor proliferation, metastasis, and drug resistance, such as triple-negative breast cancer, ovarian cancer[62, 63]. PAMR1, which contains a peptidase domain that contributes to muscle regeneration, was initially identified as downregulated in the muscles from mice with Duchenne muscular dystrophy (DMD). Recognized as a muscle protease vital for regeneration, PAMR1 is predominantly expressed in various tissues, including normal skeletal muscle and brain[64, 65]. In human malignant tumors, PAMR1 expression correlates with favorable cervical cancer prognoses[66]. PAMR1 has been shown to inhibit the growth of breast cancer cells and is frequently absent in breast cancer samples (20.8%-58.3%)[67, 68]\u0026nbsp;. This absence has led to its classification as a potential tumor suppressor in breast cancer, often being suppressed due to promoter hypermethylation in breast cancer tissues[69]. However, the role of PAMAR1 in the occurrence and progression of EC remains unexplored. SRPX , a transmembrane protein composed of 464 amino acids and 3 sushi domains, was originally identified as a pathogenic gene in patients with X-linked retinoic syndrome[70]. It functions as a tumor suppressor gene with pro-apoptotic function, and has been observed to be downregulated in various human tumor cells and tissues[71]\u0026nbsp;. Mouse gene knockout studies have further demonstrated its pro-apoptotic function[72]. Srpx knockout mice exhibit tumor development in around 30% of cases, encompassing lymphoma, lung cancer, and liver cancer[73]. Synapse binding protein 1 (STXBP1) primarily regulates the fusion of intracellular granular membranes and various exocytosis processes[74], i including vesicle fusion, initiation, docking, and membrane fusion. This protein is vital in eliminating tumor cells and facilitating granular cell membrane fusion[75]. Although exocytosis being prevalent in both normal and tumor cells, the expression and function of STXBP1 in tumor progression have been scarcely investigated. In lung adenocarcinoma, upregulated STXBP1 expression correlates with poor prognosis[76]. Threonine and tyrosine kinase (TTK), alternatively referred to as unipolar spindle 1 (Mps1), acts as a spindle assembly checkpoint, guaranteeing the precise segregation of chromosomes into daughter cells[77]. Apart from the testes and placenta, TTK is rarely detected in normal tissues[78]. However, high levels of TTK have been observed in various human malignancies, including breast cancer[79], ovarian cancer[80], pancreatic cancer[81]\u0026nbsp;correlating with poor prognosis. Knockdown of TTK expression or treatment with TTK inhibitors can inhibit tumor growth by suppressing cell proliferation and invasion, leading to significant survival benefits[82-84]. In ovarian cancer, downregulation of TTK can sensitize cisplatin-resistant cells to cisplatin therapy[85], potentially positioning TTK as a therapeutic target in cancer treatment. Studies indicate that TTK expression is increased in EC, associated with poor prognosis, positively correlated with high TNM stage, and involved in immune infiltration. This suggests its promising role as a diagnostic biomarker for distinguishing EC tissue from normal tissue[86]. Additionally, TTK inhibitor (NTRC0066-0) has demonstrated significant inhibition of EC cells growth[87]. These findings underscore the close association of identified key genes with tumor occurrence and progression, primarily through cell cycle regulation, which is consistent with GO results. Nevertheless, additional research is necessary to clarify their specific mechanisms in modulating EC.\u003c/p\u003e\n\u003cp\u003eCombining biomarkers in the risk model can improve predictive capacity and expedite the formulation of personalized treatment strategies, surpassing the efficacy of single clinical biomarkers. BCRGRS, constructed from these 11 genes, emerged as a significant independent prognostic indicator for OS. Utilizing both TCGA and ICGC datasets, BCRGRS categorized patients into HRG and LRG by median value. LRG patients exhibited higher survival rates, whereas those in the HRG group had poorer survival outcomes. Specifically, the median OS of patients in the LRG group significantly exceeded that of patients in the HRG group. Univariate and multivariate Cox regression analyses confirmed BCRGRS as an independent prognostic factor. Although the survival curves in the ICGC cohort potentially intersect at a 30% risk of death, this does not imply superior survival rates for 30% of patients in the LRG group compared to those in the HRG group. At the intersection, 6 patients in the LRG group were still alive, whereas only 4 remained in the HRG group. Thus, a 30% risk of death does not equate to 30% of patients deceased. As the last patient in the LRG group succumbed, the LRG curve dropped to 0, unavoidably intersecting with the HRG curve. Extension of follow-up duration, with surviving patients in the LRG group, might preclude curve intersection. Additionally, our data revealed higher BCRGs scores in EC patients aged over 65 years, obese individuals, and those with the increase of EC FIGO stage and tumor grade, BCRGs scores were also higher. A nomogram combining age, stage, grade, and BCRGRS to validate its strong ability for predict EC prognosis, i suggesting the potential utility of BCRGRS as a clinical biomarker.\u003c/p\u003e\n\u003cp\u003eThe tumor microenvironment (TME) plays a significant role in the occurrence, progression, and metastasis of malignant tumors. Comprising tumor cells, stromal cells, endothelial cells, immune cells, and extracellular matrix components produced by tumor-associated cells, the TME modulates immune responses, facilitating immune evasion and fostering tumor cell tolerance, ultimately impacting tumor pathogenesis. Identifying dependable prognostic indicators and immunotherapy targets is imperative in elucidating the dynamics of EC. Compared with other immune systems, the endometrial immune system exhibits distinctive characteristics, mainly manifested in two aspects: it prevents infection and defends against various bacteria and viruses, while also facilitating allogeneic embryo transplantation[88]. Upon comparing the immune cell infiltration and activation pathways between the LRG and HRG groups, we observed a general decrease in the infiltration of immune cells and a decrease in the activity of immune-related pathways in the HRG group. Extensive studies have demonstrated that substantial T cell infiltration, particularly by cytotoxic CD8 T cells, is associated with a favorable prognosis. CD8 T cells can eliminate tumor cells through the release of\u0026nbsp;cytotoxic molecules such as granzyme and perforin[89, 90], and secrete IFN-\u0026gamma; to induce tumor ferroptosis[91], which holds prognostic value for EC[92, 93]. Furthermore, BCRGRS shows promise in guiding immunotherapy selection for EC patients. Studies indicates that the approval of immune checkpoint inhibitors, such as dostarlimab or pembrolizumab (PD-1 inhibitors), benefits approximately 20-30% of 20-30% of patients with advanced EC[94]. Our study revealed that BCRGRS and several immune checkpoint genes, including PD-L1 and CTLA-4, showed higher expression in the LRG group, suggesting potential benefits from anti-PD-1 and anti-CTLA-4 therapy for patients belonging to this group. TIDE predicts the response to immune checkpoint inhibitors (ICIs) by simulating two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high cytotoxic T lymphocyte (CTL) infiltration, and the prevention of T cell infiltration in tumors with low CTL levels[95]. A positive correlation exists between the TIDE score and the possibility of tumor immune evasion, indicating that patients with higher TIDE scores may not benefit from ICI therapy. In our study, the LRG group exhibited lower TIDE scores and higher T cell dysfunction scores, whereas the HRG group displayed higher TIDE scores and increased T cell rejection scores. Consequently, HRG patients are more prone to T cell dysfunction and rejection when receiving immunotherapy, possibly leading to immune evasion as a consequence of T cell rejection. These patients typically exhibit a poor response to ICIs. In contrast, patients may benefit more from ICIs treatment. Furthermore, analysis of four different Immune Phenotype Scores (IPS) in the TCIA database revealed higher sensitivity in the LRG group, indicating that these patients might derive greater benefits from PD-1 and CTLA-4 inhibitors. External validation datasets, including urothelial carcinoma patients treated with anti-PD-L1 and malignant melanoma patients treated with anti-PD-1/CTLA-4, further substantiated the effectiveness of BCRGRS in predicting immunotherapy responses. In both immunotherapy cohorts, HRG patients exhibited lower OS and a higher incidence of disease progression following immunotherapy. This evidence suggests that BCRGRS can be considered an effective biomarker to predict response to immunotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003en 2013, the TCGA Alliance initially categorized the prognosis of EC into four categories: POLE hypermutation, microsatellite instability hypermutation (MSIH), low copy number (CNL), and high copy number (CNH)[96]. Gene-based detection is increasingly pivotal in EC treatment, dividing the prognosis into various risk categories and guiding surgical and adjuvant therapies. In our analysis of gene mutations across various BCRGRS subgroups, missense variants emerged as the most prevalent, followed by nonsense variants and frameshift deletions. TP53 and PIK3CA mutations occurred more frequently in HRG, while PTEN, PIK3, and ARIDI mutations were more prevalent in LRG. TP53 mutations, the most common genetic events in cancer, are associated with malignant tumor invasion and poor prognosis[97, 98]. It affects the cancer cell cycle via the p53/TGF\u0026szlig; signaling pathway. PIK3CA may promote tumor proliferation through the PI3K-AKT signaling pathway[99]. Consequently, HRG patients, who typically exhibit high levels of TP53 and PIK3CA mutations, face a worse prognosis compared to LRG patients with lower frequencies of these mutations, which is consistent with our findings. Some studies have demonstrated that tumor mutational burden (TMB) can reflect tumor neoantigen potential and is closely linked to DNA repair defects, predicting immunotherapy efficacy across various tumors. Patients with mismatch repair defects (dMMR) and high microsatellite instability (MSI-H) typically exhibit higher TMB[99, 100]. Treatment with immune checkpoint inhibitors often leads to improved response and survival benefits in patients with high TMB[101, 102]. Our study observed that BCRGRS scores were negatively correlated with TMB, with MSI-H patients having the lowest scores, aligning with our immune correlation analysis results. This suggests that LRG patients with EC combined with TMB-H and MSI-H may have a greater chance of responding positively to immunotherapy. Based on molecular typing system assessment, although there is no significant difference in POLE hypermutation scores and the three molecular subtypes, the proportion of POLE hypermutation with the best prognosis is higher than that of HRG, while the opposite is true for CNH with the worst prognosis. could function as an adjunctive tool for molecular typing in immunotherapy and as predictive markers, enhancing the accurate evaluation of EC patients\u0026apos; immune microenvironment and treatment effects.\u003c/p\u003e\n\u003cp\u003eThe main limitations of our study were as follows. Firstly, due to data limitations, our study cohorts are sourced from different public datasets, inevitably leading to intra-tumor or intra-patient tumor heterogeneity. Secondly, owing to time constraints, although we identified the survival impact of related genes shared between EC and BC in EC patients, there has been no experimental validation or clinical trials conducted to date. The underlying mechanisms behind these phenomena remain unclear, necessitating further experimental evidence. Thirdly, the robustness of the prognostic model still needs verification and explanation through large-scale prospective studies and functional mechanism experiments. Currently, more work needs to be done to identify common biomarkers focusing on endometrial cancer and breast cancer.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy conducting differential gene analysis and performing comprehensive analysis of BC and EC datasets by WGCNA, we identified 11 gene features with prognostic significance for EC. Based on the risk scores of the 11 genes, along with age, stage and grade, we constructed the BCRGRS model and proved its efficacy as a prognostic indicator for EC. The BCRGRS model offers novel insights for predicting EC survival and guiding treatment decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunling Xu conceived and designed the present study. Hualing Zhang, Xiaochen Qin, Kaili Zhang and Tianjiao He acquire analysis and interpret the data. Xiaoyi Ma and Yun Su wrote the manuscript. Yanci Che check and revised the manuscript. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM.C. Liu, A.B. Gardner, J.E. Wolford, K.S. Tewari, Endometrial cancer in the morbidly obese: a review, Current Opinion in Obstetrics \u0026amp; Gynecology, 32 (2020) 42-50.\u003c/li\u003e\n\u003cli\u003eK. Njoku, C.J. Sutton, A.D. Whetton, E.J. Crosbie, Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurr ence in Endometrial Cancer, Metabolites, 10 314.\u003c/li\u003e\n\u003cli\u003eE. Crosbie, J. 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Sinicrope, Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors, Cancer Discov, 10 (2020) 1808-1825.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometrial carcinoma, breast carcinoma, risk model, prognosis, TCGA","lastPublishedDoi":"10.21203/rs.3.rs-4332299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4332299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eBreast cancer (BC) and endometrial cancer (EC) both originate from sex hormone-dependent organs, yet their interaction mechanisms remain unclear. This study aims to explore the common genetic and molecular characteristics between BC and EC, predicting their potential roles in EC treatment and prognosis evaluation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on BC and EC were retrieved from The Cancer Genome Atlas Program (TCGA) and the International Cancer Genome Consortium (ICGC) databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify shared genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. Single-factor Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were employed to identify potential breast cancer-related genes (BCRGs), and a prognostic risk scoring system was developed. Additionally, we examined the relationship between risk groups and clinicopathological features, immune infiltration, tumor mutation burden, and drug sensitivity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 367 breast cancer-related DEGs were identified in EC, and 113 potentially prognostic DEGs were screened. From these, 11 key BCRGs significantly associated with the overall survival rate of EC patients were identified. Patients in the low-risk group exhibited longer overall survival (OS) compared to those in the high-risk group. Additionally, significant differences in clinical characteristics, tumor immune cell infiltration, somatic mutations, and drug sensitivity were observed between risk groups, with the low-risk group showing a higher likelihood of benefiting from immunotherapy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe risk score established in this study demonstrates prognostic ability, potentially aiding in identifying patients who may benefit from immunotherapy and targeted therapy after breast cancer diagnosis.\u003c/p\u003e","manuscriptTitle":"Construction of breast cancer-related gene prognostic signature in endometrial cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 20:26:50","doi":"10.21203/rs.3.rs-4332299/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6f7ab45-7f64-4262-b935-43974523ad7f","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T07:16:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-10 20:26:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4332299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4332299","identity":"rs-4332299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}