Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation

Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation | 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 Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation Xiao Wang, Guangyu Lin, Zhidong Liao, Xiaoxin Chen, Zhouying Gu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6903614/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 Background Endometrial Cancer (EC) is a common type of gynecological malignancy, with a rising incidence rate each year. However, the prognostic value of Anoikis-related genes in EC remains unclear. This study aims to investigate the roles of Anoikis-related genes in EC diagnosis, prognosis, and drug treatment prediction. Methods Differentially expressed Anoikis-related genes were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were categorized based on consensus clustering analysis of these genes. Functional analysis of differentially expressed genes between subgroups was conducted using Gene Set Variation Analysis (GSVA) to explore the functional state. A prognostic risk model for EC was constructed and its independent prognostic value was evaluated using univariate Cox proportional hazards regression analysis and LASSO regression analysis. Additionally, the roles of these genes in the tumor microenvironment, their association with tumor immune cell infiltration, and their relationship with drug sensitivity were investigated. Results Key Anoikis-related genes, including BUB1, PLK1, UBE2C, and BIRC5, were identified, and an Anoikis-related prognostic risk model was successfully constructed, in which the indicators G3, High Grade, and risk score were especially significant (p < 0.001) and could independently serve as prognostic markers for UCEC patients. A nomogram score was developed to predict patient survival rates in the future. Based on the median risk score, patients were divided into two groups. In the test dataset, patients with highrisk scores generally had lower survival probabilities and died earlier than those with lowrisk scores (p < 0.001). The risk model also demonstrated the ability to predict the immune microenvironment in EC patients and was closely associated with treatment resistance in EC. Conclusion The prognostic risk model based on Anoikis-related genes can predict the overall survival rate of EC patients and provides insight into the tumor immune microenvironment. The expression level of Anoikis-related genes may influence the sensitivity of various drugs to EC treatment. This study offers a theoretical basis for the discovery of new molecular markers and therapeutic targets in EC. Endometrial Cancer Anoikis Prognostic Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background EC is a common type of gynecological malignancy with an increasing incidence rate, posing a severe threat to women's health [ 1 ]. Although surgery and hormone therapy have shown some efficacy in clinical treatment, many patients still face the risk of recurrence and metastasis in advanced stages due to a lack of effective prognostic assessment and personalized treatment strategies. Therefore, in-depth research on the molecular mechanisms of endometrial cancer and the search for new biomarkers and therapeutic targets are crucial for improving patient outcomes and guiding clinical treatment. Anoikis is a process of programmed cell death triggered by the loss of interaction between cells and the extracellular matrix [ 2 ]. As a specific form of programmed cell death, it plays an essential role in maintaining tissue homeostasis and inhibiting tumor metastasis [ 3 ]. Tumor cells are protected by a “barrier” that prevents the activation of Anoikis-initiating molecules, enabling cells to evade death and resist Anoikis, promoting cell survival and invasion [ 4 ]. Anoikis resistance is a characteristic of many cancer cells, which enhances cell survival and invasion [ 5 ]. Multiple studies have shown that changes in the expression of Anoikis-related genes are closely related to tumor progression and prognosis. For instance, toll-like receptor 4 ligand, manganese superoxide dismutase, collagen XIII, nuclear factor kappa B (IκB), kinase-ε (IKKε), and Deleted in Breast Cancer 1 (DBC1) are related to Anoikis resistance in triple-negative breast cancer [ 6 ]. Similarly, the expression of elongation factor-2 kinase contributes to Anoikis resistance and invasion in human glioma cells [ 7 ], while the PLAG1-GDH1 axis promotes Anoikis resistance and tumor metastasis in LKB1-deficient lung cancer through CamKK2-AMPK signaling [ 8 ]. Anoikis resistance often occurs in glioma, promoting diffuse infiltration [ 9 ], while in EC, the specific mechanisms underlying Anoikis resistance remain unclear. Therefore, further in-depth research is required to explore the role and prognostic significance of Anoikis in the progression of endometrial cancer to better guide its clinical application. In recent years, with the development of high-throughput sequencing technology, public databases such as TCGA and GEO have provided vast amounts of gene expression data and clinical information for endometrial cancer research. This study aims to identify differentially expressed genes related to Anoikis in endometrial cancer by integrating data from the TCGA and GEO databases and to construct a prognostic risk model based on these genes. Additionally, this study will explore the roles of these genes in the tumor microenvironment, their association with immune cell infiltration, and their relationship with drug sensitivity, thus providing a theoretical basis for the precision treatment of endometrial cancer. 2. Materials and Methods 2.1 Data Collection Gene expression profiles and clinical data from 589 samples were downloaded from The Cancer Genome Atlas (TCGA)-UCEC database ( https://portal.gdc.cancer.gov/ ), comprising 554 UCEC samples and 35 normal controls. Additionally, microarray data from an independent endometrial cancer cohort consisting of 103 cancer samples (GSE17025) were downloaded from the GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) to serve as a validation cohort for the prognostic model. 2.2 Identification of Anoikis-Related Genes in Endometrial Cancer Anoikis-related gene sets were obtained from the GeneCards Human Gene Database ( https://www.genecards.org/ ) and the Harmonizome Database ( https://maayanlab.cloud/Harmonizome/ ), with a selection criterion of a GeneCards score greater than 0.4. After removing duplicate genes, a total of 575 Anoikis-related genes were identified. By analyzing the expression profiles of these genes across both datasets, 183 intersecting genes were obtained for further analysis. 2.3 Consensus Clustering Analysis of Anoikis-Related Genes The R package "Consensus Cluster Plus" was used to identify unsupervised subgroups and clusters in the TCGA-UCEC dataset based on Anoikis-related genes. The clustering was validated using Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), with the R packages “broom,” “Rtsne,” and “umap,” respectively. Kaplan-Meier survival curves for different subgroups and clusters were analyzed and plotted using the R packages “survival” and “survminer.” Gene expression across different subgroups and clusters was visualized using the R package “pheatmap.” 2.4 GSVA of Anoikis-Related Genes The R package "GSVA" was used to analyze KEGG pathways to explore differences in biological processes between different subgroups. The ssGSEA algorithm was employed to study immune cell infiltration across subgroups. The infiltration of immune cells in different subgroups was visualized using the R package “ggplot2.” 2.5 Construction of the Anoikis-Related Prognostic Risk Model Gene expression profiles and survival data were merged for further analysis. A training dataset (n = 183) and a testing dataset (n = 182) were randomly selected from a total of 365 patients in a 1:1 ratio. Univariate Cox proportional hazards regression analysis was performed to identify candidate prognostic genes, and the results were visualized using the R packages “survival” and “forestplot.” Genes with p < 0.05 were included and further analyzed using LASSO regression to avoid overfitting with the R package “glmnet.” The risk score was constructed as a predictive factor, calculated as the sum of the coefficients and expression levels of relevant genes: $$\:\text{R}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}=\:\sum\:_{\text{i}=1}^{\text{n}}\left(\text{C}\text{o}\text{e}\text{f}\text{i}\times\:{\text{X}}_{\text{i}}\right)$$ where Coefi is the coefficient associated with each prognostic gene and \(\:{\:\text{X}}_{\text{i}}\) is the expression level of each prognostic gene. Finally, multivariate Cox proportional hazards regression analysis was conducted to identify key clinical phenotypes. 2.6 Survival Analysis Based on the median risk score, patients in both the training and testing datasets were divided into high-risk and low-risk groups. The R package “Complex Heatmap” was used to visualize prognostic gene expression as heat maps for both datasets. Kaplan-Meier survival curves were plotted with the R package “survminer” to demonstrate the predictive effectiveness of the model. Different endpoints (1-year, 3-year, and 5-year) were set, and the model's performance was evaluated using time-dependent Receiver Operating Characteristic (ROC) curves with the R package “time ROC.” A nomogram was constructed with the R package “rms” to predict overall survival based on risk scores and clinical pathological features (e.g., age, gender, and stage). The R packages “highcharter,” “ggplot2,” and “ggalluvial” were used to create Sankey diagrams displaying the clustering distribution and survival outcomes across risk groups. 2.7 Tumor Immune Analysis The CIBERSORT algorithm was used to analyze the correlation between prognostic genes, risk scores, and tumor-infiltrating immune cells. 2.8 Drug Sensitivity Analysis The Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/ ) and CellMiner (version 2.10, https://discover.nci.nih.gov/cellminer/home.do ) databases were used to investigate the potential impact of Anoikis expression profiles on drug sensitivity in gastric cancer. First, the R package “oncoPredict” and data from GDSC were used to compare the half-maximal inhibitory concentration (IC50) of several chemotherapy and targeted drugs between different risk groups. Next, the CellMiner database was queried to retrieve RNA-seq composite expression data and DTP NCI-60 z-scores, selecting only FDA-approved drugs (n = 314) for further analysis. Finally, Pearson correlation analysis was performed to assess the relationship between the expression of key Anoikis-related core genes and drug sensitivity, with a correlation coefficient above 3 and a p-value less than 0.05 used as selection criteria. 2.9 Statistical Analysis All statistical analyses were performed using R software (version 4.1.2), with a p-value < 0.05 considered statistically significant. 3. Result 3.1 Identification of Differentially Expressed Overlapping Anoikis-Related Genes A total of 575 Anoikis-related genes were identified from the GeneCards and Harmonizome databases, and 183 differentially expressed overlapping Anoikis-related genes were selected for further analysis and validated in the TCGA dataset. Limma differential analysis of UCEC samples in the TCGA dataset revealed 102 downregulated and 89 upregulated genes (all p < 0.05, Fig. 1 A), which were visualized in a heatmap showing these Anoikis-related differentially expressed genes (DEGs) (Fig. 1 B). To improve the accuracy of core gene selection, univariate Cox regression analysis was performed on the 183 Anoikis-related DEGs, resulting in 41 prognostic genes (all p < 0.05, Fig. 1 C). A prognostic network diagram of the Cox regression analysis results was plotted using the R package “igraph” (Fig. 1 D), and a protein-protein interaction (PPI) network demonstrated complex interactions among these prognostic genes (Fig. 1 E). Node analysis using the “MCC” algorithm in the “cytoHubba” plugin identified the top 10 key Anoikis-related DEGs as BUB1, PLK1, UBE2C, BIRC5, E2F1, MYC, CDC25C, EZH2, CDKN2A, and RPS6KA1 (Fig. 1 F). 3.2 Identification and Characterization of Anoikis-Related Subgroups To identify Anoikis-related subgroups, we applied a consensus clustering algorithm for unsupervised clustering based on the expression of overlapping Anoikis-related genes in the TCGA-UCEC dataset. The results indicated that k = 2 was the optimal parameter for dividing the UCEC dataset into subgroups A and B (Fig.s 2A, B, and C). PCA revealed a clear distinction between subgroup A and subgroup B (Fig. 2 D). The results were further validated using t-SNE and UMAP analyses (Fig.s 2E and F). Additionally, subgroup B exhibited a lower survival probability than subgroup A (Fig. 2 G). A heatmap of overlapping Anoikis-related gene expression showed differences between the two subgroups (Fig. 2 H). These findings suggest that the TCGA-UCEC dataset can be classified based on Anoikis-related genes. To explore the different biological processes between the two subgroups, we conducted Gene Set Variation Analysis (GSVA) to identify distinct KEGG pathways in each subgroup. The results showed that subgroup A was highly enriched in linoleic acid metabolism, tyrosine metabolism, steroid hormone biosynthesis, primary bile acid biosynthesis, and complement and coagulation cascades. In contrast, subgroup B was highly enriched in pathways such as mitochondrial one-carbon metabolism, spliceosome, and nucleotide excision repair (Fig. 3 A). Additionally, we used ssGSEA to analyze differences in immune infiltration levels between the two subgroups (Fig. 3 B). The results indicated distinct characteristics in KEGG pathways and immune infiltration levels between the two subgroups. 3.3 Construction and Validation of the Anoikis-Related Prognostic Risk Model To construct an Anoikis-related prognostic risk model, 183 Anoikis-related DEGs were analyzed using univariate Cox regression, resulting in 41 prognostic genes. Subsequently, LASSO Cox regression analysis was performed to identify key Anoikis-related genes in the TCGA training cohort, yielding eight selected genes (Fig. 4 A and B). The risk model equation was defined as: Risk Score=(− 0.46877×IGF1)+(0.217639×CDKN2A)+(− 0.69636×RPS6KA1)+(0.361010×UBE2C)+(0.194701×PTK6)+(0.235208×MNX1)+(0.241016×CDH3)+(1.257817×SPTA1). Where CDKN2A, RPS6KA1, and UBE2C were identified as key genes using the MCC algorithm. Patients were divided into two groups based on the median risk score. In the test dataset, patients with highrisk scores generally had lower survival probabilities and tended to die earlier than those with lowrisk scores (p < 0.001, Fig. 4 C). Additionally, the gene expression profiles of prognostic genes differed significantly between the two risk groups (Fig. 4 E). Furthermore, the overall survival, survival time, and gene expression profiles of prognostic genes in the test dataset were consistent with those in the training dataset (Fig. 4 D and F). We also predicted the overall survival rates in the training and test datasets. The time-dependent Area Under the Curve (AUC) values of the ROC curves for 1-year, 3-year, and 5-year survival in the test dataset were 0.647, 0.696, and 0.629, respectively (Fig. 5 A), and in the training dataset, they were 0.747, 0.737, and 0.823, respectively (Fig. 5 B). 3.4 Independent Prognostic Value of the Anoikis-Related Prognostic Risk Score Model To comprehensively assess the model's performance and accuracy, we conducted a multivariate Cox regression analysis to evaluate the independent prognostic value of the risk score model (Fig. 6 A). The results indicated statistically significant differences (p < 0.05) for the following: risk score (p < 0.001, HR = 1.04 [1.02–1.06]), clinical stage G2 (p < 0.05, HR = 6.43 [1.44–28.66]), clinical stage G3 (p < 0.001, HR = 11.29 [2.76–46.21]), High Grade (p < 0.001, HR = 29.11 [5.82–145.60]), and age (p < 0.05, HR = 1.02 [1.00-1.05]). Among these, G3, High Grade, and the risk score were especially significant (p < 0.001) and could independently serve as prognostic indicators for UCEC patients. Based on these findings, a nomogram was created to estimate future survival rates of patients based on their scores (Fig. 6 B). Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) aligned closely with the 45-degree line, indicating a strong agreement between predicted probabilities and actual observations (Fig. 6 C). The cumulative risk curve demonstrated a marked increase in risk score over time for the high-risk group, with significantly higher scores compared to the low-risk group (Fig. 6 D). These findings indicate that the Anoikis-related gene-based risk score has significant prognostic value in UCEC, and the successfully constructed prognostic risk model performs well in predicting overall survival in endometrial cancer. 3.5 Immune Status of UCEC Patients Based on the results above, we observed the distribution and relationships among two subgroups, two risk groups, and two clinical outcomes (Fig. 7 A). To investigate the potential mechanisms by which key Anoikis-related genes regulate UCEC progression, we conducted immune cell correlation analysis on core genes using TCGA sequencing data and the respective risk groups (Fig. 7 B). The analysis revealed significant differences (p < 0.001) in the risk group for CDKN2A, where CD8 + T cells, activated CD4 memory T cells, activated NK cells, and activated dendritic cells (DCs) were notably varied. In the case of UBE2C, regulatory T cells, follicular helper T cells, resting CD4 memory T cells, and M1 macrophages showed significant differences (p < 0.001) in the risk group. To further explore the variations in immune cell content between risk groups, we used CIBERSORT for immune cell differential analysis and visualized the differences in immune cells and risk groups with bar plots, heatmaps, and violin plots (Fig.s 7C, D, and E). The results showed differences in the composition of various immune cells between the risk groups (p < 0.05). We also conducted Gene Set Enrichment Analysis (GSEA) on TCGA sequencing data and the corresponding risk groups. The results indicated that pathways such as neural signaling transmission, myocardial contraction, and cytokine receptors were enriched in the high-risk group (Fig. 7 F), while pathways related to allograft rejection, fatty acid metabolism, and melanoma were enriched in the low-risk group (Fig. 7 G). These findings suggest that multiple pathways regulated by Anoikis-related genes are involved in UCEC progression, which may offer potential therapeutic targets for UCEC. Additionally, we used CIBERSORT to explore the model's ability to predict the immune microenvironment in UCEC patients. In Fig. 7 H, memory B cells, activated dendritic cells, M1 macrophages, and follicular helper T cells were positively correlated with risk scores, whereas resting dendritic cells, CD8 + T cells, and regulatory T cells were negatively correlated with risk scores. 3.6 Drug Sensitivity Analysis Considering the significant pathway dysregulation observed between different risk groups, it is worthwhile to investigate whether the risk score correlates with treatment resistance in EC patients. Therefore, we conducted a differential analysis of drug sensitivity between high-risk and low-risk groups using the GDSC database for both standard chemotherapy and targeted therapies (Fig. 8 A, all p < 0.05). The results showed that the IC50 values for drugs such as AstraZeneca, Dacomitinib, Damirone, Palbociclib, and Uprosertib were significantly higher in the high-risk group than in the low-risk group, suggesting that high-risk patients may have lower sensitivity to these drugs. Conversely, for drugs like Elotuzumab, Sapitinib, YM155, Bcl-XL inhibitor, and WIKI4, the IC50 values were lower in the high-risk group, indicating that high-risk patients may be more sensitive to these therapies. Additionally, we analyzed the impact of core gene expression levels on drug sensitivity using the Cellminer database. The results showed that representative drugs with a high correlation to CDKN2A included Elotuzumab (cor = 0.38) and Bisacodyl (cor = 0.3), Daporinad (cor = 0.37) and MI-219 (cor = -0.36), Mitoxantrone Hydrochloride (cor = -0.36), O6-Benzylguanine (cor = -0.32), Teglarinad (cor = 0.4), and Piroxantrone (cor = -0.36) (Fig. 8 B). For RPS6KA1, representative drugs with a high correlation included 8-Chloroadenosine (cor = 0.37) and Allopurinol (cor = 0.36), Artemether (cor = 0.35) and AT-9283 (cor = -0.3), Cyclophosphamide (cor = 0.33), ICG-001 (cor = -0.35), Lenvatinib (cor = -0.3), Methylprednisolone (cor = 0.33), Perifosine (cor = 0.32), PKI-587 (cor = -0.35), and RSK2 Inhibitor (cor = -0.33) (Fig. 8 C). For UBE2C, drugs with high correlation included Indolinone (cor = -0.33) and BMS-754807 (cor = 0.38), CH-5132799 (cor = 0.3) and Dinoprost (cor = -0.44), Cypionate (cor = -0.32), ENMD-2076 (cor = 0.41), GSK-2126458 (cor = 0.33), H-89 (cor = 0.32), Imatinib (cor = -0.48), Eloxatin (cor = 0.45), Rociletinib (cor = -0.41), Masitinib (cor = -0.31), Medroxyprogesterone (cor = -0.43), Nilotinib (cor = -0.33), Salinomycin (cor = -0.3), and Zidovudine (cor = -0.39) (Fig. 8 D). These findings suggest that the expression levels of Anoikis-related genes may influence the therapeutic sensitivity of various drugs in UCEC, providing potential new therapeutic targets. 4. Discussion Anoikis is a critical form of programmed cell death that plays an essential role in maintaining tissue homeostasis and inhibiting tumor metastasis. This specialized form of cell death is triggered by a reduction in survival signals, which occurs when cells lose their adhesion to appropriate surfaces or membranes via integrins [ 10 ]. The loss of interaction among integrins, cell adhesion molecules, and the extracellular matrix (ECM) leads to a loss of survival signals for non-adherent cells, ultimately resulting in apoptosis [ 11 ]. Anoikis is considered a key mechanism that prevents the growth and proliferation of non-adherent cells in inappropriate environments. To survive in the bloodstream or lymphatic circulation, certain cancer cells develop resistance mechanisms against Anoikis. In endometrial cancer, Anoikis resistance is one of the primary factors that promotes the survival and invasion of tumor cells. Changes in the expression of Anoikis-related genes are closely associated with tumor progression and prognosis, and dysregulation of these genes can lead to abnormal proliferation of endometrial cancer cells and tumor development [ 12 – 13 ]. Additionally, Anoikis resistance is closely associated with immune regulation. Studies have shown that in endometrial cancer, Anoikis-related genes may also influence antigen presentation and immune evasion of tumor cells, thereby affecting the efficacy of immunotherapy. Tumor-specific immune responses vary across different molecular subtypes, suggesting that tumor immune response could serve as a new biomarker to predict which patients may respond to immunotherapy [ 14 ]. Moreover, the apoptosis of tumor-infiltrating lymphocytes is considered a relevant mechanism of resistance to immunotherapy, which can be blocked by interfering with the Fas/Fas ligand pathway [ 15 ]. In the tumor microenvironment, immune evasion mechanisms include dysfunction in antigen presentation processes and upregulation of immunosuppressive signals [ 16 ]. Therefore, understanding these mechanisms is crucial for improving the effectiveness of immunotherapy in endometrial cancer. Numerous studies have investigated potential biomarkers for endometrial cancer [ 17 – 18 ]. In this study, 183 differentially expressed Anoikis-related genes were identified from the GeneCards and Harmonizome databases and validated in TCGA’s UCEC samples. Limma differential analysis revealed 102 downregulated and 89 upregulated genes. Univariate Cox regression analysis further identified 41 prognostic genes, and the top 10 key genes were selected using the MCC algorithm in the PPI network and “cytoHubba.” These findings suggest that these genes may play a crucial role in the progression of endometrial cancer, providing potential biomarker candidates for prognosis assessment and treatment in endometrial cancer. Using a consensus clustering algorithm, we divided the TCGA-UCEC dataset into subgroups A and B based on the expression of Anoikis-related genes, with subgroup B demonstrating a lower survival probability. The distinctness of subgroup classification was validated by PCA, t-SNE, and UMAP analyses. GSVA and ssGSEA analyses revealed significant differences between the two subgroups in KEGG pathways and immune infiltration levels, providing new insights into the biological processes underlying endometrial cancer. These findings align with our understanding of the molecular characteristics of various endometrial cancer subtypes, particularly regarding the causal relationships between metabolites and proteins. Through multi-omics analysis, we can explore the pathological mechanisms of endometrial cancer more deeply and identify potential biomarkers for future targeted therapies [ 19 ]. This classification approach aids in identifying patient groups with different prognoses, potentially guiding clinical treatment decisions. Scholars have proposed an integrative framework that combines clinical, genetic, biochemical, imaging, pathological, and molecular data to assess adenomas originating from all pituitary cell lineages comprehensively. This evidence-based risk factor scoring system generates a cumulative score to assess disease severity, prognosis, and treatment efficacy, thus guiding the management of pituitary adenomas at the bedside [ 20 ]. Researchers have also defined a molecular subtype of prostate cancer using the combined status of CRISP3, ERG, and PTEN, which is associated with the worst and most fatal clinical outcomes [ 21 ]. Evaluating the combined value of these biomarkers may help stratify patients into different prognostic groups and identify those with the poorest clinical outcomes. In this study, 41 prognostic genes were selected from 183 Anoikis-related DEGs using univariate Cox regression analysis, and further refined to 8 key genes via LASSO Cox regression to construct a prognostic risk model. This model successfully differentiated high-risk and low-risk patients, with significantly lower survival probabilities observed in the high-risk group. This finding was validated in both the training and test datasets, indicating the model's reliable predictive capacity. The model’s ability to classify patients into high-risk and low-risk groups with differential survival probabilities supports its potential for personalized treatment. The effectiveness of this approach has been corroborated in similar studies, such as the development of a 9-gene prognostic model for pancreatic adenocarcinoma [ 22 ], as well as analyses on immune cell infiltration and immune-related gene expression in colorectal cancer [ 23 ]. Additionally, research in breast cancer has highlighted the importance of long non-coding RNA (lncRNA) in constructing prognostic models [ 24 ]. This study predicted the overall survival rates of endometrial cancer patients in both the training and test sets, calculating the time-dependent AUC for 1-year, 3-year, and 5-year survival points. By comparing different prognostic models, we can better understand the survival prognosis of endometrial cancer patients and support clinical decision-making. Similar to prognostic studies in other cancers, such as breast cancer, the use of prognostic markers related to DNA repair genes has been shown to improve the accuracy of survival predictions [ 25 ]. Furthermore, the development and external validation of survival prediction models for aggressive breast cancer have also demonstrated the effectiveness of various models in survival rate prediction [ 26 ]. These studies indicate that machine learning and statistical models can significantly enhance the ability to predict survival in cancer patients [ 27 ]. This study comprehensively evaluated the independent prognostic value of the risk score model through multivariate Cox analysis, revealing that the risk score, high grade, and G3 stage are significant prognostic indicators for UCEC patients. The accuracy of the model was further validated by the nomogram and calibration curves, while the cumulative risk curve showed a significant increase in risk scores over time for the high-risk group. These findings confirm that the Anoikis-related gene-based risk score has substantial value in predicting overall survival in endometrial cancer patients. The reliability of these research methods has also been demonstrated in previous studies [ 28 – 29 ]. The study revealed the distribution relationships between subgroups and risk groups, finding that key Anoikis-related genes such as CDKN2A and UBE2C are associated with immune cell activation states. CIBERSORT analysis showed significant differences in immune cell content between the risk groups. This finding is consistent with previous research, suggesting that the composition of immune cells in the tumor microenvironment may influence tumor prognosis and therapeutic response [ 30 – 31 ]. GSEA analysis indicated that neural signaling pathways were enriched in the high-risk group, while pathways such as fatty acid metabolism were enriched in the low-risk group, suggesting that Anoikis-related gene regulation may offer new targets for endometrial cancer treatment. CIBERSORT also identified correlations between certain immune cells and risk scores, further highlighting the role of the immune microenvironment in disease progression. This finding aligns with research by Cassetta et al., who proposed SIGLEC1 and CCL8 as novel biomarkers for patient stratification and prognosis in breast cancer through tumor-associated macrophages [ 32 ]. Additionally, Pribluda and colleagues demonstrated that chronic stress can trigger a senescence-inflammatory response, promoting tumorigenesis even in the absence of external inflammatory triggers [ 33 ]. These studies collectively emphasize the critical role of immune-mediated inflammation in tumor development, suggesting that Anoikis-related genes may regulate tumor progression by influencing immune cells in the tumor microenvironment, thus providing potential targets for immunotherapy. Through analysis using the GDSC database, we found that high-risk endometrial cancer patients exhibited lower sensitivity to certain drugs, such as AstraZeneca, while showing higher sensitivity to others, like Elotuzumab. Analysis with the Cellminer database further revealed correlations between specific drugs and the expression levels of key genes such as CDKN2A, RPS6KA1, and UBE2C. These genes play significant roles in the onset and progression of cancer. Upregulation of CDKN2A is associated with cellular senescence and the inhibition of cell proliferation [ 34 ]; RPS6KA1 and UBE2C are closely linked to cell proliferation and apoptosis, with the former playing an essential role in cell growth and the latter being critical in cell cycle regulation [ 35 ]. The CellMiner tool enables researchers to rapidly retrieve gene expression data and compare it with drug activity, uncovering potential drug mechanisms and resistance pathways [ 36 – 37 ]. These findings suggest that the expression of Anoikis-related genes may predict drug sensitivity, offering potential biomarkers for personalized therapy. This provides a foundation for developing targeted drug treatments based on specific gene expression profiles, advancing precision medicine. Despite providing valuable insights, this study has several limitations. For instance, the research relies on data from public databases, which may introduce selection bias. Additionally, our risk score model needs validation in larger patient cohorts and further exploration of its applicability across diverse populations and clinical settings. Future studies should also consider incorporating a broader range of clinical variables and combining experimental research to further investigate the mechanisms of Anoikis-related genes in endometrial cancer. 5. Conclusion This study provides an in-depth exploration of the role of Apoptosis related genes in endometrial cancer, revealing their close association with tumor progression, prognosis, and immune regulation, which has important clinical significance. A risk scoring model has been established to accurately predict the overall survival of patients, which can help clinical doctors better evaluate patient prognosis and develop more accurate personalized treatment plans. What’s more, the discovery of potential biomarkers associated with immune cell infiltration and drug sensitivity provides important evidence for the development of new immunotherapy strategies and personalized treatments. Last but not least, these findings not only enhance our understanding of the biology of endometrial cancer, but also lay the foundation for the development of new targeted therapies targeting these apoptosis related genes. Future research should validate these results in larger independent samples and further explore their potential mechanisms through experimental studies. This will help to better translate these research findings into clinical applications, benefiting more endometrial cancer patients. Abbreviations EC Endometrial Cancer TCGA The Cancer Genome Atlas, GEO Gene Expression Omnibus IκB Nuclear factor Kappa B IKKε Nuclear factor Kinase-ε DBC1 Deleted in Breast Cancer 1 PCA Principal Component Analysis t-SNE t-distributed Stochastic Neighbor Embedding UMAP Uniform Manifold Approximation and Projection ROC Receiver Operating Characteristic GDSC The Genomics of Drug Sensitivity in Cancer DEGs Differentially expressed genes GSVA Gene Set Variation Analysis AUC Area Under the Curve OS Overall Survival DCs Dendritic Cells GSEA Gene Set Enrichment Analysis Declarations Acknowledgements Not applicable. Author contributions CGM was responsible for the overall project progress, paper revision. WX and LGY conceptualized and designed the study, supervised data collection and reviewed and revised the manuscript. LZD and CXX collected data, carried out the initial analyses, drafted the initial manuscript, and revised the manuscript. GZY processed the data, and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Funding This research received no specifc grant from any funding agency in the public, commercial or not-for-proft sectors. Availability of data and materials All data included in this study are available upon request by contact with the corresponding author. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Suryo Rahmanto Y, Shen W, Shi X, et al. Inactivation of Arid1a in the endometrium is associated with endometrioid tumorigenesis through transcriptional reprogramming. Nat Commun. 2020;11(1):2717. https://doi.org/10.1038/s41467-020-16416-0 . Frisch SM, Francis H. Disruption of epithelial cell-matrix interactions induces apoptosis. J Cell Biol. 1994;124(4):619–. https://doi.org/10.1083/jcb.124.4.619 . 26. Simpson CD, Anyiwe K, Schimmer AD. Anoikis resistance and tumor metastasis. Cancer Lett. 2008;272:177–85. https://doi.org/10.1016/j.canlet.2008.05.029 . Raeisi M, Zehtabi M, Velaei K, et al. Anoikis in cancer: The role of lipid signaling. Cell Biol Int. 2022;46(11):1717–28. https://doi.org/10.1002/cbin.11896 . Paoli P, Giannoni E, Chiarugi P. Anoikis molecular pathways and its role in cancer progression. Biochim Biophys Acta.1833;2013:3481–98. Tajbakhsh A, Rivandi M, Abedini S, et al. Regulators and mechanisms of Anoikis in triple-negative breast cancer (TNBC): A review. Crit Rev Oncol Hematol. 2019;140:17–27. https://doi.org/10.1016/j.critrevonc.2019.05.009 . Zhang L, Zhang Y, Liu XY, et al. Expression of elongation factor-2 kinase contributes to Anoikis resistance and invasion of human glioma cells. Acta Pharmacol Sin. 2011;32(3):361–7. https://doi.org/10.1038/aps.2010.213 . Jin L, Chun J, Pan C, et al. The PLAG1-GDH1 Axis Promotes Anoikis Resistance and Tumor Metastasis through CamKK2-AMPK Signaling in LKB1-Deficient Lung Cancer. Mol Cell. 2018;69(1):87–99. https://doi.org/10.1016/j.molcel.2017.11.025 . e7. Zhu Z, Fang C, Xu H, et al. Anoikis resistance in diffuse glioma: The potential therapeutic targets in the future. Front Oncol. 2022;12:976557. https://doi.org/10.3389/fonc.2022.976557 . Frisch SM, Ruoslahti E. Integrins and Anoikis. Curr Opin Cell Biol. 1997;9:701–6. https://doi.org/10.1016/S0955-0674(97)80124-X . Vachon PH. Integrin signaling, cell survival, and Anoikis: distinctions, differences, and differentiation. J Signal Transduct. 2011;2011:738137. https://doi.org/10.1155/2011/738137 . Halim H, Luanpitpong S, Chanvorachote P. Acquisition of Anoikis resistance up-regulates caveolin-1 expression in human non-small cell lung cancer cells. Anticancer Res. 2012;32:1649–58. Tang W, Feng X, Zhang S, et al. Caveolin-1 confers resistance of hepatoma cells to Anoikis by activating IGF-1 pathway. Cell Physiol Biochem. 2015;36:1223–36. https://doi.org/10.1159/000430292 . Mullen MM, Mutch DG. Endometrial Tumor Immune Response: Predictive Biomarker of Response to Immunotherapy. Clin Cancer Res. 2019;25(8):2366–8. https://doi.org/10.1158/1078-0432.CCR-18-4122 . Zhu J, Powis de Tenbossche CG, Cané S, et al. Resistance to cancer immunotherapy mediated by apoptosis of tumor-infiltrating lymphocytes. Nat Commun. 2017;8(1):1404. https://doi.org/10.1038/s41467-017-00784-1 . Yi M, Dong B, Chu Q, Wu K. Immune pressures drive the promoter hypermethylation of neoantigen genes. Exp Hematol Oncol. 2019;8:32. https://doi.org/10.1186/s40164-019-0156-7 . Zhou Q, Eldakhakhny S, Conforti F, et al. Pir2/Rnf144b is a potential endometrial cancer biomarker that promotes cell proliferation. Cell Death Dis. 2018;9(5):504. https://doi.org/10.1038/s41419-018-0521-1 . Townsend MH, Ence ZE, Felsted AM, et al. Potential new biomarkers for endometrial cancer. Cancer Cell Int. 2019;19:19. https://doi.org/10.1186/s12935-019-0731-3 . Shen Y, Tian Y, Ding J, et al. Unravelling the molecular landscape of endometrial cancer subtypes: insights from multiomics analysis. Int J Surg. 2024;110(9):5385–95. https://doi.org/10.1097/JS9.0000000000001685 . Ho KKY, Fleseriu M, Wass J, et al. A proposed clinical classification for pituitary neoplasms to guide therapy and prognosis. Lancet Diabetes Endocrinol. 2024;12(3):209–14. https://doi.org/10.1016/S2213-8587(23)00382-0 . Al Bashir S, Alshalalfa M, Hegazy SA, et al. Cysteine- rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients' prognosis. J Hematol Oncol. 2014;7:21. https://doi.org/10.1186/1756-8722-7-21 . Xu D, Wang Y, Liu X, et al. Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma. Pharmacol Res. 2021;164:105370. https://doi.org/10.1016/j.phrs.2020.105370 . Ge P, Wang W, Li L, et al. Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer. Biomed Pharmacother. 2019;118:109228. https://doi.org/10.1016/j.biopha.2019.109228 . Liu Z, Li M, Hua Q, Li Y, Wang G. Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Coxproportional hazards model based on L1-penalized estimation. Int J Mol Med. 2019;44(4):1333–43. https://doi.org/10.3892/ijmm.2019.4303 . Zhang D, Yang S, Li Y, et al. Prediction of Overall Survival Among Female Patients With Breast Cancer Using a Prognostic Signature Based on 8 DNA Repair-Related Genes. JAMA Netw Open. 2020;3(10):e2014622. https://doi.org/10.1001/jamanetworkopen.2020.14622 . Karapanagiotis S, Pharoah PDP, Jackson CH, Newcombe PJ. Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath. Clin Cancer Res. 2018;24(9):2110–5. https://doi.org/10.1158/1078-0432.CCR-17-3542 . Rahman SA, Walker RC, Maynard N, et al. The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests. Ann Surg. 2023;277(2):267–74. https://doi.org/10.1097/SLA.0000000000004794 . Bria E, De Manzoni G, Beghelli S, et al. A clinical-biological risk stratification model for resected gastric cancer: prognostic impact of Her2, Fhit, and APC expression status. Ann Oncol. 2013;24(3):693–701. https://doi.org/10.1093/annonc/mds506 . El Sharouni MA, Ahmed T, Varey AHR, et al. Development and Validation of Nomograms to Predict Local, Regional, and Distant Recurrence in Patients With Thin (T1) Melanomas. J Clin Oncol. 2021;39(11):1243–52. https://doi.org/10.1200/JCO.20.02446 . Tekpli X, Lien T, Røssevold AH, et al. An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment. Nat Commun. 2019;10(1):5499. https://doi.org/10.1038/s41467-019-13329-5 . Safonov A, Jiang T, Bianchini G, et al. Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer. Cancer Res. 2017;77(12):3317–24. https://doi.org/10.1158/0008-5472.CAN-16-3478 . Bronte V. Deciphering Macrophage and Monocyte Code to Stratify Human Breast Cancer Patients. Cancer Cell. 2019;35(4):538–9. https://doi.org/10.1016/j.ccell.2019.03.010 . Bondar T, Medzhitov R. The origins of tumor-promoting inflammation. Cancer Cell. 2013;24(2):143–4. https://doi.org/10.1016/j.ccr.2013.07.016 . Stein C, Riedl S, Rüthnick D, et al. The arginine methyltransferase PRMT6 regulates cell proliferation and senescence through transcriptional repression of tumor suppressor genes. Nucleic Acids Res. 2012;40(19):9522–33. https://doi.org/10.1093/nar/gks767 . Gagrica S, Brookes S, Anderton E, et al. Contrasting behavior of the p18INK4c and p16INK4a tumor suppressors in both replicative and oncogene-induced senescence. Cancer Res. 2012;72(1):165–75. https://doi.org/10.1158/0008-5472.CAN-11-2552 . Reinhold WC, Sunshine M, Liu H, et al. CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res. 2012;72(14):3499–511. https://doi.org/10.1158/0008-5472.CAN-12-1370 . Reinhold WC, Varma S, Sunshine M, et al. RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB. Cancer Res. 2019;79(13):3514–24. https://doi.org/10.1158/0008-5472.CAN-18-2047 . 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. 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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-6903614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474874023,"identity":"1627c4a7-c7fb-4d4b-9e16-d87e9a9d1c30","order_by":0,"name":"Xiao Wang","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wang","suffix":""},{"id":474874024,"identity":"6b3e309d-985d-48dd-85cd-73cb5eaa573a","order_by":1,"name":"Guangyu Lin","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangyu","middleName":"","lastName":"Lin","suffix":""},{"id":474874025,"identity":"d359aa8f-dc22-4140-9b92-59940df03d35","order_by":2,"name":"Zhidong Liao","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhidong","middleName":"","lastName":"Liao","suffix":""},{"id":474874026,"identity":"ab4f7a96-8653-4060-8ead-23da668ef50a","order_by":3,"name":"Xiaoxin Chen","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxin","middleName":"","lastName":"Chen","suffix":""},{"id":474874027,"identity":"5fc85134-bf3a-450e-93fd-ed6a8988b545","order_by":4,"name":"Zhouying Gu","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhouying","middleName":"","lastName":"Gu","suffix":""},{"id":474874028,"identity":"d9bf7c54-cbb2-4ebb-a033-81f97d81235d","order_by":5,"name":"Guangmou Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACxobzHx9+/Gdjx8/eQKQW5sYDxsYSbGnJkj0HiNTC3nzATIKH7TDjhhsJRGrhbTuQbCDBk8ZscPPxxhsMNTbRBLUA3XPwQYGEDZ/k7bRiC4ZjabkNhLQYzjjYbCBhkMbMdzvHTIKx4TBhLfb3H7NJ8CQcZmy4eYZILYwNx4BaDhxmnHCDh2gtZ5iNJRtAgQz0SwIxfgFqYXz4sQEUlYc33vhQY0NYCzIwkEggRTlEC6k6RsEoGAWjYGQAAKo4RBHRzdGAAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guangmou","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-16 08:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6903614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6903614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85369694,"identity":"7bfe1776-1fcd-49a5-8f48-e63e4eed336e","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":948107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic Gene Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A and B) Volcano plot and heatmap of\u0026nbsp;Anoikis-related DEGs from the TCGA training cohort.\u003c/li\u003e\n \u003cli\u003e(C) Univariate Cox analysis of Anoikis-related DEGs for prognostic gene selection.\u003c/li\u003e\n \u003cli\u003e(D) Prognostic network diagram of prognostic genes.\u003c/li\u003e\n \u003cli\u003e(E) Construction of the PPI network for prognostic genes using STRING.\u003c/li\u003e\n \u003cli\u003e(F) Core Anoikis-related DEGs identified using the “MCC” algorithm in the “cytoHubba” plugin.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/cd1b87dc518175ae77e3064c.png"},{"id":85369692,"identity":"1b8cb1e9-bbcb-49f3-9535-233ea7891142","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":861004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus Clustering Analysis of Anoikis-Related Subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) Heatmap of the consensus matrix when k = 2.\u003c/li\u003e\n \u003cli\u003e(B and C) Cumulative Distribution Function (CDF) plots of 41 prognostic genes in the TCGA-UCEC dataset.\u003c/li\u003e\n \u003cli\u003e(D, E, F) Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold\u0026nbsp;Approximation and Projection (UMAP) analyses between subgroups.\u003c/li\u003e\n \u003cli\u003e(G) Kaplan-Meier survival curve for the two clusters in the TCGA-UCEC dataset.\u003c/li\u003e\n \u003cli\u003e(H) Heatmap showing differences in gene expression between the two subgroups.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/e3388f688768ac4b3a8fe18c.png"},{"id":85369695,"identity":"af53063c-4ca0-43ca-9d19-88ab745600d6","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1021590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferent Biological Processes Between the Two Subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) GSVA of the two subgroups.\u003c/li\u003e\n \u003cli\u003e(B) Immune infiltration level analysis of the two subgroups (ssGSEA).\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/8036e72313d0944c04bca893.png"},{"id":85369698,"identity":"73b80901-ed0b-4d15-8fe4-d85c29dee857","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1018751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Validation of the Prognostic Risk Model\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A and B) Lasso Cox analysis identifying key Anoikis-related genes for constructing the risk score model in the TCGA\u0026nbsp;training cohort.\u003c/li\u003e\n \u003cli\u003e(C) Survival curve for different risk groups in the test dataset.\u003c/li\u003e\n \u003cli\u003e(D) Survival curve for different risk groups in\u0026nbsp;the training dataset.\u003c/li\u003e\n \u003cli\u003e(E) Gene expression profiles of prognostic genes in the test dataset.\u003c/li\u003e\n \u003cli\u003e(F) Gene expression profiles of prognostic genes in the training dataset.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/623e029de10b6a9a1cff858c.png"},{"id":85370851,"identity":"b56e6f19-f73e-4978-84b5-dc15365ce1f5","added_by":"auto","created_at":"2025-06-25 07:29:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) ROC curves for 1-year, 3-year, and 5-year survival in the test dataset.\u003c/li\u003e\n \u003cli\u003e(B) ROC curves for 1-year, 3-year, and 5-year survival in the training dataset.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/9d0a3132541d0eefb112ef91.png"},{"id":85369708,"identity":"13a85715-d010-43da-8d0a-f5b5dbbc9056","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":359734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndependent Prognostic Analysis of the Risk Assessment Model\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) Multivariate Cox analysis of the risk assessment model.\u003c/li\u003e\n \u003cli\u003e(B) Prognostic nomogram based on the risk score and clinical stages for predicting OS in endometrial cancer.\u003c/li\u003e\n \u003cli\u003e(C) Calibration curve to evaluate the accuracy of the prognostic nomogram.\u003c/li\u003e\n \u003cli\u003e(D) Cumulative risk curve of the risk assessment model.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/d57ce4207d35b877512e164e.png"},{"id":85371245,"identity":"cb667bf4-925a-43e1-9ded-4c7a43ea2f18","added_by":"auto","created_at":"2025-06-25 07:37:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1041274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Status of UCEC Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) Relationship between risk score groups and survival status for the two patient subgroups.\u003c/li\u003e\n \u003cli\u003e(B) Correlation analysis between immune cells and\u0026nbsp;core genes.\u003c/li\u003e\n \u003cli\u003e(C, D, E) Bar plot, heatmap, and violin plot showing differences in immune cells between risk groups.\u003c/li\u003e\n \u003cli\u003e(F, G) GSEA analysis of TCGA sequencing data and corresponding risk groups.\u003c/li\u003e\n \u003cli\u003e(H) CIBERSORT analysis exploring the model's ability to predict the immune microenvironment in UCEC patients.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/41ef61278b39778645241bb9.png"},{"id":85369713,"identity":"3b1c296d-adff-4ff1-a763-35960781ea63","added_by":"auto","created_at":"2025-06-25 07:21:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":985537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug Sensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e(A) Drug sensitivity analysis using the GDSC\u0026nbsp;database.\u003c/li\u003e\n \u003cli\u003e(B, C, D) Drug sensitivity analysis of core genes using the Cellminer database.\u003c/li\u003e\n\u003c/ul\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/420c7d203287668b92ae681e.png"},{"id":89486808,"identity":"b19eb9c2-6e2e-48fc-8aed-5a5791c55919","added_by":"auto","created_at":"2025-08-20 13:02:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6911157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903614/v1/108d4f4a-beb5-4bac-b2a2-c49f699932e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation","fulltext":[{"header":"1. Background","content":"\u003cp\u003eEC is a common type of gynecological malignancy with an increasing incidence rate, posing a severe threat to women's health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although surgery and hormone therapy have shown some efficacy in clinical treatment, many patients still face the risk of recurrence and metastasis in advanced stages due to a lack of effective prognostic assessment and personalized treatment strategies. Therefore, in-depth research on the molecular mechanisms of endometrial cancer and the search for new biomarkers and therapeutic targets are crucial for improving patient outcomes and guiding clinical treatment.\u003c/p\u003e \u003cp\u003eAnoikis is a process of programmed cell death triggered by the loss of interaction between cells and the extracellular matrix [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a specific form of programmed cell death, it plays an essential role in maintaining tissue homeostasis and inhibiting tumor metastasis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Tumor cells are protected by a \u0026ldquo;barrier\u0026rdquo; that prevents the activation of Anoikis-initiating molecules, enabling cells to evade death and resist Anoikis, promoting cell survival and invasion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Anoikis resistance is a characteristic of many cancer cells, which enhances cell survival and invasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multiple studies have shown that changes in the expression of Anoikis-related genes are closely related to tumor progression and prognosis. For instance, toll-like receptor 4 ligand, manganese superoxide dismutase, collagen XIII, nuclear factor kappa B (IκB), kinase-ε (IKKε), and Deleted in Breast Cancer 1 (DBC1) are related to Anoikis resistance in triple-negative breast cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, the expression of elongation factor-2 kinase contributes to Anoikis resistance and invasion in human glioma cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while the PLAG1-GDH1 axis promotes Anoikis resistance and tumor metastasis in LKB1-deficient lung cancer through CamKK2-AMPK signaling [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Anoikis resistance often occurs in glioma, promoting diffuse infiltration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while in EC, the specific mechanisms underlying Anoikis resistance remain unclear. Therefore, further in-depth research is required to explore the role and prognostic significance of Anoikis in the progression of endometrial cancer to better guide its clinical application.\u003c/p\u003e \u003cp\u003eIn recent years, with the development of high-throughput sequencing technology, public databases such as TCGA and GEO have provided vast amounts of gene expression data and clinical information for endometrial cancer research. This study aims to identify differentially expressed genes related to Anoikis in endometrial cancer by integrating data from the TCGA and GEO databases and to construct a prognostic risk model based on these genes. Additionally, this study will explore the roles of these genes in the tumor microenvironment, their association with immune cell infiltration, and their relationship with drug sensitivity, thus providing a theoretical basis for the precision treatment of endometrial cancer.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection\u003c/h2\u003e \u003cp\u003eGene expression profiles and clinical data from 589 samples were downloaded from The Cancer Genome Atlas (TCGA)-UCEC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comprising 554 UCEC samples and 35 normal controls.\u003c/p\u003e \u003cp\u003eAdditionally, microarray data from an independent endometrial cancer cohort consisting of 103 cancer samples (GSE17025) were downloaded from the GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to serve as a validation cohort for the prognostic model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of Anoikis-Related Genes in Endometrial Cancer\u003c/h2\u003e \u003cp\u003eAnoikis-related gene sets were obtained from the GeneCards Human Gene Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Harmonizome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Harmonizome/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Harmonizome/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a selection criterion of a GeneCards score greater than 0.4. After removing duplicate genes, a total of 575 Anoikis-related genes were identified. By analyzing the expression profiles of these genes across both datasets, 183 intersecting genes were obtained for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Consensus Clustering Analysis of Anoikis-Related Genes\u003c/h2\u003e \u003cp\u003eThe R package \"Consensus Cluster Plus\" was used to identify unsupervised subgroups and clusters in the TCGA-UCEC dataset based on Anoikis-related genes. The clustering was validated using Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), with the R packages \u0026ldquo;broom,\u0026rdquo; \u0026ldquo;Rtsne,\u0026rdquo; and \u0026ldquo;umap,\u0026rdquo; respectively. Kaplan-Meier survival curves for different subgroups and clusters were analyzed and plotted using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer.\u0026rdquo; Gene expression across different subgroups and clusters was visualized using the R package \u0026ldquo;pheatmap.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 GSVA of Anoikis-Related Genes\u003c/h2\u003e \u003cp\u003eThe R package \"GSVA\" was used to analyze KEGG pathways to explore differences in biological processes between different subgroups. The ssGSEA algorithm was employed to study immune cell infiltration across subgroups. The infiltration of immune cells in different subgroups was visualized using the R package \u0026ldquo;ggplot2.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction of the Anoikis-Related Prognostic Risk Model\u003c/h2\u003e \u003cp\u003eGene expression profiles and survival data were merged for further analysis. A training dataset (n\u0026thinsp;=\u0026thinsp;183) and a testing dataset (n\u0026thinsp;=\u0026thinsp;182) were randomly selected from a total of 365 patients in a 1:1 ratio. Univariate Cox proportional hazards regression analysis was performed to identify candidate prognostic genes, and the results were visualized using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;forestplot.\u0026rdquo; Genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included and further analyzed using LASSO regression to avoid overfitting with the R package \u0026ldquo;glmnet.\u0026rdquo; The risk score was constructed as a predictive factor, calculated as the sum of the coefficients and expression levels of relevant genes:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{i}\\text{s}\\text{k}\\:\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}=\\:\\sum\\:_{\\text{i}=1}^{\\text{n}}\\left(\\text{C}\\text{o}\\text{e}\\text{f}\\text{i}\\times\\:{\\text{X}}_{\\text{i}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Coefi is the coefficient associated with each prognostic gene and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{X}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the expression level of each prognostic gene. Finally, multivariate Cox proportional hazards regression analysis was conducted to identify key clinical phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Survival Analysis\u003c/h2\u003e \u003cp\u003eBased on the median risk score, patients in both the training and testing datasets were divided into high-risk and low-risk groups. The R package \u0026ldquo;Complex Heatmap\u0026rdquo; was used to visualize prognostic gene expression as heat maps for both datasets. Kaplan-Meier survival curves were plotted with the R package \u0026ldquo;survminer\u0026rdquo; to demonstrate the predictive effectiveness of the model. Different endpoints (1-year, 3-year, and 5-year) were set, and the model's performance was evaluated using time-dependent Receiver Operating Characteristic (ROC) curves with the R package \u0026ldquo;time ROC.\u0026rdquo; A nomogram was constructed with the R package \u0026ldquo;rms\u0026rdquo; to predict overall survival based on risk scores and clinical pathological features (e.g., age, gender, and stage). The R packages \u0026ldquo;highcharter,\u0026rdquo; \u0026ldquo;ggplot2,\u0026rdquo; and \u0026ldquo;ggalluvial\u0026rdquo; were used to create Sankey diagrams displaying the clustering distribution and survival outcomes across risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Tumor Immune Analysis\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm was used to analyze the correlation between prognostic genes, risk scores, and tumor-infiltrating immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eThe Genomics of Drug Sensitivity in Cancer (GDSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CellMiner (version 2.10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/home.do\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer/home.do\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were used to investigate the potential impact of Anoikis expression profiles on drug sensitivity in gastric cancer. First, the R package \u0026ldquo;oncoPredict\u0026rdquo; and data from GDSC were used to compare the half-maximal inhibitory concentration (IC50) of several chemotherapy and targeted drugs between different risk groups. Next, the CellMiner database was queried to retrieve RNA-seq composite expression data and DTP NCI-60 z-scores, selecting only FDA-approved drugs (n\u0026thinsp;=\u0026thinsp;314) for further analysis. Finally, Pearson correlation analysis was performed to assess the relationship between the expression of key Anoikis-related core genes and drug sensitivity, with a correlation coefficient above 3 and a p-value less than 0.05 used as selection criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.1.2), with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Differentially Expressed Overlapping Anoikis-Related Genes\u003c/h2\u003e \u003cp\u003eA total of 575 Anoikis-related genes were identified from the GeneCards and Harmonizome databases, and 183 differentially expressed overlapping Anoikis-related genes were selected for further analysis and validated in the TCGA dataset. Limma differential analysis of UCEC samples in the TCGA dataset revealed 102 downregulated and 89 upregulated genes (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), which were visualized in a heatmap showing these Anoikis-related differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo improve the accuracy of core gene selection, univariate Cox regression analysis was performed on the 183 Anoikis-related DEGs, resulting in 41 prognostic genes (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A prognostic network diagram of the Cox regression analysis results was plotted using the R package \u0026ldquo;igraph\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and a protein-protein interaction (PPI) network demonstrated complex interactions among these prognostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Node analysis using the \u0026ldquo;MCC\u0026rdquo; algorithm in the \u0026ldquo;cytoHubba\u0026rdquo; plugin identified the top 10 key Anoikis-related DEGs as BUB1, PLK1, UBE2C, BIRC5, E2F1, MYC, CDC25C, EZH2, CDKN2A, and RPS6KA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification and Characterization of Anoikis-Related Subgroups\u003c/h2\u003e \u003cp\u003eTo identify Anoikis-related subgroups, we applied a consensus clustering algorithm for unsupervised clustering based on the expression of overlapping Anoikis-related genes in the TCGA-UCEC dataset. The results indicated that k\u0026thinsp;=\u0026thinsp;2 was the optimal parameter for dividing the UCEC dataset into subgroups A and B (Fig.s 2A, B, and C). PCA revealed a clear distinction between subgroup A and subgroup B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The results were further validated using t-SNE and UMAP analyses (Fig.s 2E and F). Additionally, subgroup B exhibited a lower survival probability than subgroup A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). A heatmap of overlapping Anoikis-related gene expression showed differences between the two subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). These findings suggest that the TCGA-UCEC dataset can be classified based on Anoikis-related genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the different biological processes between the two subgroups, we conducted Gene Set Variation Analysis (GSVA) to identify distinct KEGG pathways in each subgroup. The results showed that subgroup A was highly enriched in linoleic acid metabolism, tyrosine metabolism, steroid hormone biosynthesis, primary bile acid biosynthesis, and complement and coagulation cascades. In contrast, subgroup B was highly enriched in pathways such as mitochondrial one-carbon metabolism, spliceosome, and nucleotide excision repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, we used ssGSEA to analyze differences in immune infiltration levels between the two subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results indicated distinct characteristics in KEGG pathways and immune infiltration levels between the two subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and Validation of the Anoikis-Related Prognostic Risk Model\u003c/h2\u003e \u003cp\u003eTo construct an Anoikis-related prognostic risk model, 183 Anoikis-related DEGs were analyzed using univariate Cox regression, resulting in 41 prognostic genes. Subsequently, LASSO Cox regression analysis was performed to identify key Anoikis-related genes in the TCGA training cohort, yielding eight selected genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). The risk model equation was defined as:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRisk Score=(\u0026minus;\u0026thinsp;0.46877\u0026times;IGF1)+(0.217639\u0026times;CDKN2A)+(\u0026minus;\u0026thinsp;0.69636\u0026times;RPS6KA1)+(0.361010\u0026times;UBE2C)+(0.194701\u0026times;PTK6)+(0.235208\u0026times;MNX1)+(0.241016\u0026times;CDH3)+(1.257817\u0026times;SPTA1). Where CDKN2A, RPS6KA1, and UBE2C were identified as key genes using the MCC algorithm.\u003c/p\u003e \u003cp\u003ePatients were divided into two groups based on the median risk score. In the test dataset, patients with highrisk scores generally had lower survival probabilities and tended to die earlier than those with lowrisk scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Additionally, the gene expression profiles of prognostic genes differed significantly between the two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Furthermore, the overall survival, survival time, and gene expression profiles of prognostic genes in the test dataset were consistent with those in the training dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and F).\u003c/p\u003e \u003cp\u003eWe also predicted the overall survival rates in the training and test datasets. The time-dependent Area Under the Curve (AUC) values of the ROC curves for 1-year, 3-year, and 5-year survival in the test dataset were 0.647, 0.696, and 0.629, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and in the training dataset, they were 0.747, 0.737, and 0.823, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Independent Prognostic Value of the Anoikis-Related Prognostic Risk Score Model\u003c/h2\u003e \u003cp\u003eTo comprehensively assess the model's performance and accuracy, we conducted a multivariate Cox regression analysis to evaluate the independent prognostic value of the risk score model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The results indicated statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the following: risk score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.04 [1.02\u0026ndash;1.06]), clinical stage G2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;=\u0026thinsp;6.43 [1.44\u0026ndash;28.66]), clinical stage G3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;11.29 [2.76\u0026ndash;46.21]), High Grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;29.11 [5.82\u0026ndash;145.60]), and age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;=\u0026thinsp;1.02 [1.00-1.05]). Among these, G3, High Grade, and the risk score were especially significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and could independently serve as prognostic indicators for UCEC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on these findings, a nomogram was created to estimate future survival rates of patients based on their scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) aligned closely with the 45-degree line, indicating a strong agreement between predicted probabilities and actual observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The cumulative risk curve demonstrated a marked increase in risk score over time for the high-risk group, with significantly higher scores compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThese findings indicate that the Anoikis-related gene-based risk score has significant prognostic value in UCEC, and the successfully constructed prognostic risk model performs well in predicting overall survival in endometrial cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Immune Status of UCEC Patients\u003c/h2\u003e \u003cp\u003eBased on the results above, we observed the distribution and relationships among two subgroups, two risk groups, and two clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). To investigate the potential mechanisms by which key Anoikis-related genes regulate UCEC progression, we conducted immune cell correlation analysis on core genes using TCGA sequencing data and the respective risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The analysis revealed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the risk group for CDKN2A, where CD8\u0026thinsp;+\u0026thinsp;T cells, activated CD4 memory T cells, activated NK cells, and activated dendritic cells (DCs) were notably varied. In the case of UBE2C, regulatory T cells, follicular helper T cells, resting CD4 memory T cells, and M1 macrophages showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the risk group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the variations in immune cell content between risk groups, we used CIBERSORT for immune cell differential analysis and visualized the differences in immune cells and risk groups with bar plots, heatmaps, and violin plots (Fig.s 7C, D, and E). The results showed differences in the composition of various immune cells between the risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWe also conducted Gene Set Enrichment Analysis (GSEA) on TCGA sequencing data and the corresponding risk groups. The results indicated that pathways such as neural signaling transmission, myocardial contraction, and cytokine receptors were enriched in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), while pathways related to allograft rejection, fatty acid metabolism, and melanoma were enriched in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). These findings suggest that multiple pathways regulated by Anoikis-related genes are involved in UCEC progression, which may offer potential therapeutic targets for UCEC.\u003c/p\u003e \u003cp\u003eAdditionally, we used CIBERSORT to explore the model's ability to predict the immune microenvironment in UCEC patients. In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH, memory B cells, activated dendritic cells, M1 macrophages, and follicular helper T cells were positively correlated with risk scores, whereas resting dendritic cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and regulatory T cells were negatively correlated with risk scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eConsidering the significant pathway dysregulation observed between different risk groups, it is worthwhile to investigate whether the risk score correlates with treatment resistance in EC patients. Therefore, we conducted a differential analysis of drug sensitivity between high-risk and low-risk groups using the GDSC database for both standard chemotherapy and targeted therapies (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results showed that the IC50 values for drugs such as AstraZeneca, Dacomitinib, Damirone, Palbociclib, and Uprosertib were significantly higher in the high-risk group than in the low-risk group, suggesting that high-risk patients may have lower sensitivity to these drugs. Conversely, for drugs like Elotuzumab, Sapitinib, YM155, Bcl-XL inhibitor, and WIKI4, the IC50 values were lower in the high-risk group, indicating that high-risk patients may be more sensitive to these therapies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we analyzed the impact of core gene expression levels on drug sensitivity using the Cellminer database. The results showed that representative drugs with a high correlation to CDKN2A included Elotuzumab (cor\u0026thinsp;=\u0026thinsp;0.38) and Bisacodyl (cor\u0026thinsp;=\u0026thinsp;0.3), Daporinad (cor\u0026thinsp;=\u0026thinsp;0.37) and MI-219 (cor = -0.36), Mitoxantrone Hydrochloride (cor = -0.36), O6-Benzylguanine (cor = -0.32), Teglarinad (cor\u0026thinsp;=\u0026thinsp;0.4), and Piroxantrone (cor = -0.36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). For RPS6KA1, representative drugs with a high correlation included 8-Chloroadenosine (cor\u0026thinsp;=\u0026thinsp;0.37) and Allopurinol (cor\u0026thinsp;=\u0026thinsp;0.36), Artemether (cor\u0026thinsp;=\u0026thinsp;0.35) and AT-9283 (cor = -0.3), Cyclophosphamide (cor\u0026thinsp;=\u0026thinsp;0.33), ICG-001 (cor = -0.35), Lenvatinib (cor = -0.3), Methylprednisolone (cor\u0026thinsp;=\u0026thinsp;0.33), Perifosine (cor\u0026thinsp;=\u0026thinsp;0.32), PKI-587 (cor = -0.35), and RSK2 Inhibitor (cor = -0.33) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). For UBE2C, drugs with high correlation included Indolinone (cor = -0.33) and BMS-754807 (cor\u0026thinsp;=\u0026thinsp;0.38), CH-5132799 (cor\u0026thinsp;=\u0026thinsp;0.3) and Dinoprost (cor = -0.44), Cypionate (cor = -0.32), ENMD-2076 (cor\u0026thinsp;=\u0026thinsp;0.41), GSK-2126458 (cor\u0026thinsp;=\u0026thinsp;0.33), H-89 (cor\u0026thinsp;=\u0026thinsp;0.32), Imatinib (cor = -0.48), Eloxatin (cor\u0026thinsp;=\u0026thinsp;0.45), Rociletinib (cor = -0.41), Masitinib (cor = -0.31), Medroxyprogesterone (cor = -0.43), Nilotinib (cor = -0.33), Salinomycin (cor = -0.3), and Zidovudine (cor = -0.39) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These findings suggest that the expression levels of Anoikis-related genes may influence the therapeutic sensitivity of various drugs in UCEC, providing potential new therapeutic targets.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAnoikis is a critical form of programmed cell death that plays an essential role in maintaining tissue homeostasis and inhibiting tumor metastasis. This specialized form of cell death is triggered by a reduction in survival signals, which occurs when cells lose their adhesion to appropriate surfaces or membranes via integrins [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The loss of interaction among integrins, cell adhesion molecules, and the extracellular matrix (ECM) leads to a loss of survival signals for non-adherent cells, ultimately resulting in apoptosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Anoikis is considered a key mechanism that prevents the growth and proliferation of non-adherent cells in inappropriate environments. To survive in the bloodstream or lymphatic circulation, certain cancer cells develop resistance mechanisms against Anoikis. In endometrial cancer, Anoikis resistance is one of the primary factors that promotes the survival and invasion of tumor cells. Changes in the expression of Anoikis-related genes are closely associated with tumor progression and prognosis, and dysregulation of these genes can lead to abnormal proliferation of endometrial cancer cells and tumor development [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, Anoikis resistance is closely associated with immune regulation. Studies have shown that in endometrial cancer, Anoikis-related genes may also influence antigen presentation and immune evasion of tumor cells, thereby affecting the efficacy of immunotherapy. Tumor-specific immune responses vary across different molecular subtypes, suggesting that tumor immune response could serve as a new biomarker to predict which patients may respond to immunotherapy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the apoptosis of tumor-infiltrating lymphocytes is considered a relevant mechanism of resistance to immunotherapy, which can be blocked by interfering with the Fas/Fas ligand pathway [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the tumor microenvironment, immune evasion mechanisms include dysfunction in antigen presentation processes and upregulation of immunosuppressive signals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, understanding these mechanisms is crucial for improving the effectiveness of immunotherapy in endometrial cancer.\u003c/p\u003e \u003cp\u003eNumerous studies have investigated potential biomarkers for endometrial cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, 183 differentially expressed Anoikis-related genes were identified from the GeneCards and Harmonizome databases and validated in TCGA\u0026rsquo;s UCEC samples. Limma differential analysis revealed 102 downregulated and 89 upregulated genes. Univariate Cox regression analysis further identified 41 prognostic genes, and the top 10 key genes were selected using the MCC algorithm in the PPI network and \u0026ldquo;cytoHubba.\u0026rdquo; These findings suggest that these genes may play a crucial role in the progression of endometrial cancer, providing potential biomarker candidates for prognosis assessment and treatment in endometrial cancer.\u003c/p\u003e \u003cp\u003eUsing a consensus clustering algorithm, we divided the TCGA-UCEC dataset into subgroups A and B based on the expression of Anoikis-related genes, with subgroup B demonstrating a lower survival probability. The distinctness of subgroup classification was validated by PCA, t-SNE, and UMAP analyses. GSVA and ssGSEA analyses revealed significant differences between the two subgroups in KEGG pathways and immune infiltration levels, providing new insights into the biological processes underlying endometrial cancer. These findings align with our understanding of the molecular characteristics of various endometrial cancer subtypes, particularly regarding the causal relationships between metabolites and proteins. Through multi-omics analysis, we can explore the pathological mechanisms of endometrial cancer more deeply and identify potential biomarkers for future targeted therapies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This classification approach aids in identifying patient groups with different prognoses, potentially guiding clinical treatment decisions.\u003c/p\u003e \u003cp\u003eScholars have proposed an integrative framework that combines clinical, genetic, biochemical, imaging, pathological, and molecular data to assess adenomas originating from all pituitary cell lineages comprehensively. This evidence-based risk factor scoring system generates a cumulative score to assess disease severity, prognosis, and treatment efficacy, thus guiding the management of pituitary adenomas at the bedside [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Researchers have also defined a molecular subtype of prostate cancer using the combined status of CRISP3, ERG, and PTEN, which is associated with the worst and most fatal clinical outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Evaluating the combined value of these biomarkers may help stratify patients into different prognostic groups and identify those with the poorest clinical outcomes.\u003c/p\u003e \u003cp\u003eIn this study, 41 prognostic genes were selected from 183 Anoikis-related DEGs using univariate Cox regression analysis, and further refined to 8 key genes via LASSO Cox regression to construct a prognostic risk model. This model successfully differentiated high-risk and low-risk patients, with significantly lower survival probabilities observed in the high-risk group. This finding was validated in both the training and test datasets, indicating the model's reliable predictive capacity. The model\u0026rsquo;s ability to classify patients into high-risk and low-risk groups with differential survival probabilities supports its potential for personalized treatment. The effectiveness of this approach has been corroborated in similar studies, such as the development of a 9-gene prognostic model for pancreatic adenocarcinoma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], as well as analyses on immune cell infiltration and immune-related gene expression in colorectal cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, research in breast cancer has highlighted the importance of long non-coding RNA (lncRNA) in constructing prognostic models [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study predicted the overall survival rates of endometrial cancer patients in both the training and test sets, calculating the time-dependent AUC for 1-year, 3-year, and 5-year survival points. By comparing different prognostic models, we can better understand the survival prognosis of endometrial cancer patients and support clinical decision-making. Similar to prognostic studies in other cancers, such as breast cancer, the use of prognostic markers related to DNA repair genes has been shown to improve the accuracy of survival predictions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, the development and external validation of survival prediction models for aggressive breast cancer have also demonstrated the effectiveness of various models in survival rate prediction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These studies indicate that machine learning and statistical models can significantly enhance the ability to predict survival in cancer patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study comprehensively evaluated the independent prognostic value of the risk score model through multivariate Cox analysis, revealing that the risk score, high grade, and G3 stage are significant prognostic indicators for UCEC patients. The accuracy of the model was further validated by the nomogram and calibration curves, while the cumulative risk curve showed a significant increase in risk scores over time for the high-risk group. These findings confirm that the Anoikis-related gene-based risk score has substantial value in predicting overall survival in endometrial cancer patients. The reliability of these research methods has also been demonstrated in previous studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study revealed the distribution relationships between subgroups and risk groups, finding that key Anoikis-related genes such as CDKN2A and UBE2C are associated with immune cell activation states. CIBERSORT analysis showed significant differences in immune cell content between the risk groups. This finding is consistent with previous research, suggesting that the composition of immune cells in the tumor microenvironment may influence tumor prognosis and therapeutic response [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. GSEA analysis indicated that neural signaling pathways were enriched in the high-risk group, while pathways such as fatty acid metabolism were enriched in the low-risk group, suggesting that Anoikis-related gene regulation may offer new targets for endometrial cancer treatment. CIBERSORT also identified correlations between certain immune cells and risk scores, further highlighting the role of the immune microenvironment in disease progression. This finding aligns with research by Cassetta et al., who proposed SIGLEC1 and CCL8 as novel biomarkers for patient stratification and prognosis in breast cancer through tumor-associated macrophages [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, Pribluda and colleagues demonstrated that chronic stress can trigger a senescence-inflammatory response, promoting tumorigenesis even in the absence of external inflammatory triggers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These studies collectively emphasize the critical role of immune-mediated inflammation in tumor development, suggesting that Anoikis-related genes may regulate tumor progression by influencing immune cells in the tumor microenvironment, thus providing potential targets for immunotherapy.\u003c/p\u003e \u003cp\u003eThrough analysis using the GDSC database, we found that high-risk endometrial cancer patients exhibited lower sensitivity to certain drugs, such as AstraZeneca, while showing higher sensitivity to others, like Elotuzumab. Analysis with the Cellminer database further revealed correlations between specific drugs and the expression levels of key genes such as CDKN2A, RPS6KA1, and UBE2C. These genes play significant roles in the onset and progression of cancer. Upregulation of CDKN2A is associated with cellular senescence and the inhibition of cell proliferation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; RPS6KA1 and UBE2C are closely linked to cell proliferation and apoptosis, with the former playing an essential role in cell growth and the latter being critical in cell cycle regulation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The CellMiner tool enables researchers to rapidly retrieve gene expression data and compare it with drug activity, uncovering potential drug mechanisms and resistance pathways [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings suggest that the expression of Anoikis-related genes may predict drug sensitivity, offering potential biomarkers for personalized therapy. This provides a foundation for developing targeted drug treatments based on specific gene expression profiles, advancing precision medicine.\u003c/p\u003e \u003cp\u003eDespite providing valuable insights, this study has several limitations. For instance, the research relies on data from public databases, which may introduce selection bias. Additionally, our risk score model needs validation in larger patient cohorts and further exploration of its applicability across diverse populations and clinical settings. Future studies should also consider incorporating a broader range of clinical variables and combining experimental research to further investigate the mechanisms of Anoikis-related genes in endometrial cancer.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides an in-depth exploration of the role of Apoptosis related genes in endometrial cancer, revealing their close association with tumor progression, prognosis, and immune regulation, which has important clinical significance. A risk scoring model has been established to accurately predict the overall survival of patients, which can help clinical doctors better evaluate patient prognosis and develop more accurate personalized treatment plans. What\u0026rsquo;s more, the discovery of potential biomarkers associated with immune cell infiltration and drug sensitivity provides important evidence for the development of new immunotherapy strategies and personalized treatments. Last but not least, these findings not only enhance our understanding of the biology of endometrial cancer, but also lay the foundation for the development of new targeted therapies targeting these apoptosis related genes.\u003c/p\u003e \u003cp\u003eFuture research should validate these results in larger independent samples and further explore their potential mechanisms through experimental studies. This will help to better translate these research findings into clinical applications, benefiting more endometrial cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEC Endometrial Cancer\u003c/p\u003e \u003cp\u003eTCGA The Cancer Genome Atlas,\u003c/p\u003e \u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e \u003cp\u003eIκB Nuclear factor Kappa B\u003c/p\u003e \u003cp\u003eIKKε Nuclear factor Kinase-ε\u003c/p\u003e \u003cp\u003eDBC1 Deleted in Breast Cancer 1\u003c/p\u003e \u003cp\u003ePCA Principal Component Analysis\u003c/p\u003e \u003cp\u003et-SNE t-distributed Stochastic Neighbor Embedding\u003c/p\u003e \u003cp\u003eUMAP Uniform Manifold Approximation and Projection\u003c/p\u003e \u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e \u003cp\u003eGDSC The Genomics of Drug Sensitivity in Cancer\u003c/p\u003e \u003cp\u003eDEGs Differentially expressed genes\u003c/p\u003e \u003cp\u003eGSVA Gene Set Variation Analysis\u003c/p\u003e \u003cp\u003eAUC Area Under the Curve\u003c/p\u003e \u003cp\u003eOS Overall Survival\u003c/p\u003e \u003cp\u003eDCs Dendritic Cells\u003c/p\u003e \u003cp\u003eGSEA Gene Set Enrichment Analysis\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCGM\u0026nbsp;was responsible for the overall project progress, paper revision. WX and LGY conceptualized and designed the study, supervised data collection and reviewed and revised the manuscript. LZD and CXX collected data, carried out the initial analyses, drafted the initial manuscript, and revised the manuscript. GZY processed the data, and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specifc grant from any funding agency in the \u0026nbsp;public, commercial or not-for-proft sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data included in this study are available upon request by contact with the corresponding author.\u003c/p\u003e\n\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\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSuryo Rahmanto Y, Shen W, Shi X, et al. 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RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB. Cancer Res. 2019;79(13):3514\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.CAN-18-2047\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-18-2047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial Cancer, Anoikis, Prognostic Model","lastPublishedDoi":"10.21203/rs.3.rs-6903614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6903614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndometrial Cancer (EC) is a common type of gynecological malignancy, with a rising incidence rate each year. However, the prognostic value of Anoikis-related genes in EC remains unclear. This study aims to investigate the roles of Anoikis-related genes in EC diagnosis, prognosis, and drug treatment prediction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDifferentially expressed Anoikis-related genes were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were categorized based on consensus clustering analysis of these genes. Functional analysis of differentially expressed genes between subgroups was conducted using Gene Set Variation Analysis (GSVA) to explore the functional state. A prognostic risk model for EC was constructed and its independent prognostic value was evaluated using univariate Cox proportional hazards regression analysis and LASSO regression analysis. Additionally, the roles of these genes in the tumor microenvironment, their association with tumor immune cell infiltration, and their relationship with drug sensitivity were investigated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKey Anoikis-related genes, including BUB1, PLK1, UBE2C, and BIRC5, were identified, and an Anoikis-related prognostic risk model was successfully constructed, in which the indicators G3, High Grade, and risk score were especially significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and could independently serve as prognostic markers for UCEC patients. A nomogram score was developed to predict patient survival rates in the future. Based on the median risk score, patients were divided into two groups. In the test dataset, patients with highrisk scores generally had lower survival probabilities and died earlier than those with lowrisk scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk model also demonstrated the ability to predict the immune microenvironment in EC patients and was closely associated with treatment resistance in EC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prognostic risk model based on Anoikis-related genes can predict the overall survival rate of EC patients and provides insight into the tumor immune microenvironment. The expression level of Anoikis-related genes may influence the sensitivity of various drugs to EC treatment. This study offers a theoretical basis for the discovery of new molecular markers and therapeutic targets in EC.\u003c/p\u003e","manuscriptTitle":"Identification of Anoikis-Related Genes in Endometrial Cancer and Their Applications in Treatment Sensitivity and Prognostic Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 07:21:52","doi":"10.21203/rs.3.rs-6903614/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":"38573bf4-2840-4489-b599-8b85f698a540","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T12:53:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 07:21:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6903614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6903614","identity":"rs-6903614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}