Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer

Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer Xuecheng Pang, Sumin Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2255910/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 Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. However, cuproptosis-related lncRNAs (CRLs) have an unclear relationship with endometrial cancer (EC). In this study, a total of 906 CRLs were identified, and 7 specific cuproptosis-related lncRNAs (AL807761.3, AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, LINC01166) were selected to conduct a risk model. Patients were divided into high- and low-risk groups according to the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, and the predictive accuracy was high (AUC = 0.781), indicating the good reliability and specificity of our risk model. According to Gene Set Variation Analysis (GSVA) and GSEA, both metabolism and cytoskeleton have CRL participation. In addition, we found that the CRLs-related scores were associated with the ESTIMATE score. Stratified survival analysis also revealed that the risk signature have has a high prediction accuracy among people with different clinicopathological characteristics. Further in vitro experimental validation indicated that LINC01615 may promote the invasion of EC cells during progression. The efficient risk model based on seven CRLs has a high prognostic accuracy, and LINC01615 may act as a novel biomarker and therapeutic target for EC patients. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer genomics Biological sciences/Cancer/Cancer therapy Biological sciences/Cancer/Oncogenes Biological sciences/Cancer/Tumour immunology cuproptosis lncRNA risk model LINC01615 ESTIMATE Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Endometrial cancer (EC) is the most common cancer of the female reproductive system worldwide, ranking second among female genital cancers, with a mortality rate of 2.7 per 100,000 in China [ 1 , 2 ]. Although most endometrial cancers are diagnosed at early and treatable stages, late diagnosis of endometrial cancers at advanced stages remains challenging to treat. According to the International Federation of Gynecology and Obstetrics (FIGO) staging system, the five-year survival for EC is over 90% for phase I, 70% for phase II, 60% for phase III, and 8% for stage IV [ 3 ]. Advanced EC, which is characterized by highly aggressive and easily metastatic clinical behavior, shows poor outcomes. Therefore, it is imperative to find more effective biomarkers to facilitate novel therapeutic methods. Long noncoding RNAs (lncRNAs) are regulatory RNA transcripts longer than 200 nucleotides without coding capacity [ 4 ]. It played an important regulatory role in immune response processes, such as immune cell infiltration, antigen recognition, antigen exposure, and tumor clearance [ 5 ]. Increasing research suggested that abnormal expression of lncRNA impairs homeostasis in organisms and may promote or inhibit some cancer [ 6 ]. For example, it has been demonstrated that lncRNA CCAT2 was highly expressed in endometrial cancer tissues. Knockdown of CCAT2 inhibited cell growth and metastasis of endometrial cancer cells by sponging miR-216b [ 7 ]. LncRNA NEAT1 functions as an oncogenic sponge for the tumor suppressor miR-361, which suppresses proliferation, invasion, and sphere formation by directly targeting the oncogene STAT3. Furthermore, NEAT1 also suppressed the expression of multiple metastatic genes and tumor microenvironment-related genes [ 8 ]. Although there are more and more studies on the role of lncRNA in cancer, our understanding of the role of lncRNA in the occurrence is less. Cuproptosis is a new regulatory cell death pattern, distinguished from other regulatory cell death features like pyroptosis, ferroptosis and apoptosis [ 9 ]. The most important element for cuproptosis is copper, which is an indispensable cofactor for all organisms to maintain life activities, as it plays an important role in biological processes such as mitochondrial respiration, antioxidant/detoxification, and iron uptake [ 10 ]. Copper is also an essential micronutrient with both beneficial and detrimental functions. It is reported that copper could promote cell proliferation through the activation of RAS-RAF-MEK-ERK signaling cascade [ 11 ]. The roles of cuproptosis-related lncRNAs (CRLs) in the development and outcome of EC have not yet been determined. In this study, we screened cuproptosis-related lncRNAs in EC patients to construct and validate a novel prognostic signature. We further analyzed its underlying value in predicting the diagnosis, prognosis, and tumor immune infiltration of patients with EC. The signature outperformed in predicting the prognosis of EC patients and benefits patients in guiding individualized immune therapy. Materials And Methods Sample selection and data processing The mRNA-sequencing data were downloaded for 532 EC and 35 normal tissues with corresponding clinicopathological features from the TCGA database. The clinical data included age, menopause, FIGO stage, tumor grade, histological type, recurrence, peritoneal cytology, and lymph node metastasis, and overall survival. Patients without complete clinical data were excluded. We further differentiated the transcriptomic data of TCGA-EC from mRNA and lncRNA and collected 20,475 mRNAs and 14,142 lncRNAs in EC. We first reviewed the literature and summarized the cuproptosis-related genes, and obtained a total of 19 genes (see Table S1 ). Next, the expression of cuproptosis-related lncRNAs (CRLs) was obtained by the R “limma” package [ 12 ]. According to the correlation coefficient > 0.3 and P < 0.05, 73 CRLs and their expression were identified. Finally, the co-expression of cuproptosis-related genes and CRLs were analyzed using the “ggplot2” and “ggalluvial” R package [ 13 ] to observe the interaction. The Institutional Ethics Committee (Human Research) of our hospital approved this study. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. Construction And Verification Of Risk Model We use the LASSO regression analysis to narrow the number of lncRNAs by the R package“glmnet”. We also explored the correlation among these selected CRLs with the “corrplot” R package. Finally, the risk signature was constructed with hub genes and the coefficient for each gene was obtained through the penalizing process (Table 2 ). The formula to calculate risk score was listed as following: risk model= (exprgene1 × coefficientgene1) + (exprgene2 × coefficientgene2) + ⋯ + (expression of gene n× coefficient gene n), where n is the number of CRLs modules. According to the optimal cut-off value investigated by using the R packages “survival” and “survminer”, the patients were stratified into high-risk and low-risk groups according to the median of the risk score. Kaplan-Meier (K-M) survival curves and time-dependent receptor operating characteristic (ROC) curves were plotted to evaluate the predictive accuracy of the risk score model on OS. Expression of each CRL was also compared in different risk groups. Furthermore, survival curve was also plotted according to the median value of each CRL. Construction And Evaluation Of A Predictive Nomogram And External Validation We carried out univariate and multivariate Cox regression analyses were performed to confirm whether the prognostic signature was independent of other clinical characteristics in predicting OS of patients with EC. Hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable were calculated. A predictive nomogram was constructed with these independent prognostic factors using “rms” and “Hmisc” R packages. The consistency index (C-Index) and calibration were used to evaluate the prediction accuracy of the nomogram. The whole group was divided into three subgroups including high-, moderate, and low-score subgroups by the prognostic risk value calculated using the nomogram. K-M survival curve was performed to evaluate predictive performance of nomogram and ROC curve analysis was to validate the sensitivity and specificity of the nomogram compared to the single risk score model in predicting OS. A subgroup analysis was conducted by classifying the patients into different clinicopathological stratifications (tumor grade and FIGO stage). Bioinformatics Analysis We performed the GO function enrichment analysis and KEGG pathway enrichment analysis to functionally annotate the differentially expressed genes. Both of them were conducted by the “clusterProfiler” package. We also conducted GSEA to study the functions associated with different risk groups of EC, in which |NES| > 1 and nom p < 0.05 were considered significant. Gene Set Variation Analysis (Gsva) To explore the variation in biological processes between distinct stemness-mediated subtypes, we used the ssGSEA method plus the Gene Set Variation Analysis (GSVA) package in R to evaluate infiltration levels of different immune cells, the related expression pathways, and the activity of immune-related functions. The gene set “c2.cp.kegg.v7.4.symbols.gmt” derived from MsigDB database was used as the well-defined biological signature [ 14 ]. Besides, to assess the enrichment level of 23 immune cell subtypes, we conducted ssGSEA and normalized the abundance score to unity distribution from 0 to 1. The bio-similarity of tumor-infiltrating immune cells was estimated using the multidimensional scaling and Gaussian fitting model. Immune Cell Infiltration And The Tumor Microenvironment Analysis CIBERSORT is a deconvolution method for expression matrices of immune cell subsets. LM22 is a gene signature matrix that specifies the content of immune cell types. We used the CIBERSORT package in R to calculate the number of immune cells per sample, setting the permutation to 1000 and selecting P < 0.05 as the screening threshold. Moreover, immune scores of different subgroups were calculated with the package “estimate”, and plot histograms of differences in immune scores, stromal scores, ESTIMATE score, and tumor purity of each EC tumor sample. Tumor mutational burden (TMB) in each tumor sample was referred to the number of mutated bases in per million bases, which included missense mutation, nonsense mutation, frameshift mutation and so on. We computed the TMB values with the number of variants at the length of the human exons (38 million) for each sample by Perl scripts. In Vitro Validation Of Hub Genes Five EC cell lines, HEC-1A, HEC-1B, AN3CA, ishikawa, and RL-952 were obtained from the National Collection of Authenticated Cell Culture of Chinese Academy of Sciences. All cells were cultured with DMEM containing 10% fetal bovine serum (FBS; Gibco, Gaithersburg, MD, USA), 100 U/ml penicillin, and 100 µg/ml streptomycin in an incubator with 95% humidified air containing 5% CO 2 at 37°C. siRNA for LINC01615 was transferred to AN3CA according to the manufacturer’s instructions. Total protein of EC cell lines were extracted and used for western blot as described earlier. GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) was taken as an internal control. Transwell experiment is conducted as previously described [ 15 ]. Statistics Student’s two-sided t -tests, nonparametric tests, Chi-square tests, and one-way ANOVA tests were used to compare the differences between two groups. The log-rank test was applied in Kaplan-Meier analysis to compare the OS between different groups. Univariate and multivariate Cox regression analyses were implemented to identify hazard ratio (HR) and its 95% confidence interval (CI). All statistical analyses were performed with R software (The R Foundation; http://www.r-project.org ; version 3.6.3). P < 0.05 was considered statistically significant. Result Identification of cuproptosis-related lncRNAs and differential analysis A total of 532 EC samples and 35 normal samples from the TCGA database were finally enrolled. The detailed clinical characteristics of the 532 EC patients are summarized in Table 1 . In total, 19 curoptosis-related genes were identified. The landscape and connections of the interactions among the 19 cuproptosis related genes and its related lncRNAs were illustrated in the network (Fig. 1 A). The differentially expressed CRLs (DE-CRLs) in the patients with EC were visualized in a volcano map (Fig. 1 B) and the heatmap (Fig. 1 C). As the figure shown, there were 18 up-regulated and 55 down-regulated DE-CRLs. Table 1 Characteristics of patients from TCGA dataset Variables TCGA cohort Total number 532 Living status Alive 440 82.71 Death 92 17.29 Age ༞=60 178 33.46 ༜60 354 66.54 Tumor grade G1 98 18.42 G2 119 22.37 G3 315 59.21 FIGO stage I 333 62.59 II 52 9.77 III 119 22.37 IV 28 5.26 Histological type EEA 399 75.00 MIX 22 4.14 SEA 111 20.86 Menopausal status post-menopause 458 86.09 pre-menopause 74 13.91 Recurrence Yes 106 19.92 No 426 80.08 Peritoneal cytology Negative 458 86.09 Positive 74 13.91 Cancer status With tumer 103 19.36 Tumor free 429 80.64 LNM Negative 451 84.77 Positive 81 15.23 FIGO, International Federation of Gynecology and Obstetrics; G, grade; EEA, endometrioid endometrial adenocarcinoma; LNM, lymph node metastasis. Table 2 Hub genes and correlated coeffificient value Cuproptosis-related gene Coeffificient AC008073.2 0.03360684 AC009229.1 0.11364294 AF131215.7 0.00094354 AL807761.3 -0.0015656 CDKN2A.DT 0.15255722 LINC01166 0.07124071 LINC01615 -0.0138985 risk score low: < 1.241 high: ≥ 1.241 Construction And Validation Of A Prognostic Model By The Crls Using LASSO Cox regression analysis, we finally determined that 7 prognostic CRLs (AL807761.3, AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, LINC01166) were significantly associated with prognosis in EC (Fig. 2 A-B). The signature, which consisted of the 7 key prognostic CRLs, was constructed using the respective regression coefficients. The risk scores of EC patients were calculated according to the following formula: Risk score = (AC008073.2×0.033606845)+ (AC009229.1×0.113642941) + (AF131215.7×0.000943541) + (AL807761.3×-0.001565595) + (CDKN2A.DT×0.152557217)+ (LINC01166×0.071240712)+ (LIN01615×-0.013898506). Then a prognostic model was built according to the coefficient for each which was obtained through the penalizing process. The relationships among the nine genes are shown in Fig. 2 C. AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, and LINC01166 were positively correlated with each other, but AL807761.3 was negatively correlated with the other six genes. The expression of the 7 CRLs was compared between normal and tumor tissues (Fig. 2 D and Figure S1A-B ). The results showed that AC008073.2, AC009229.1, AF131215.7, AL807761.3, and LINC01166 were significantly up-regulated in normal groups, while CDKN2A.DT and LINC01615 were down-regulated in the normal groups. Survival curves revealed that patients in high expression of AC008073.2, AC009229.1, AF131215.7, and CDKN2A.DT have a better OS compared with low expression group. On the other hand, the survival of high expression of LINC01615 was poor. The survival curve had no significant difference in AL807761.3 and LINC01166 groups (Fig. 2 E and Figure S1C-D ). Construct the prognostic characteristics of cuproptosis in patients with Then we calculated the risk score of each patient, and the patients were stratified into high-risk group or low-risk group according to the median cut-off value. The expression levels of the 7 CRLs and the distribution of clinicopathological features in high- and low-risk groups were presented in the heatmap (Fig. 3 A). The results revealed that the clinical features, including cancer status, peritoneal cytology, recurrence, tumor grade, menopausal status, histological type, stage, age, and living status are significant differently distributed in two risk groups. The EC patients were then divided into low- and high-risk groups based on the median risk score. Compared with patients in the low-risk group, those in the high-risk group indicated poorer survival probability based on our risk model (Fig. 3 B). The K-M curve indicated patients in the low-risk group had a significantly better overall survival (OS) compared to those in the high-risk group (Fig. 3 C). As shown in the time-dependent ROC analysis, the area under the curve (AUC) of the risk model reached 0.781 (Fig. 3 D). Univariate and multivariate Cox regression analyses were carried out among the available variables to determine whether the predictive ability of the 7-CRL signature in predicting OS was independent of other traditional clinicopathological properties. It proved that the risk score of the prognostic signature (HR = 1.908, 95% CI = 1.179 − 3.089, P < 0.009) was significantly associated with OS in multivariate Cox regression analysis (Fig. 3 E). Immune characterization and functional analysis in different risk signatures To verify the feasibility of the grouping strategy, we conducted an ESTIMATE analysis to profile the immune characteristics of EC based on the expression of immune cell types. Stromal, immune, and ESTIMATE scores were calculated and then we conducted a correlation analysis between these scores and risk scores. The results showed that the risk score decreased with the increase of stromal score (Fig. 4 A), immune score (Fig. 4 B), and ESTIMATE score (Fig. 4 C). The violin plot also further verified that there the high risk signature was significantly positively correlated with the ESTIMATE and immune and stromal scores (Fig. 4 D). To further explore the biological behaviors among the two immune subtypes, we performed GSVA enrichment analysis. As shown in Fig. 4 E, low risk group was related to starch and sucrose metabolism, folate biosynthesis, fructose and mannose metabolism, amino sugar and nucleotide sugar metabolism, and butanoate metabolism. High risk group was enriched in glycosphingolipid biosynthesis ganglio series and autoimmune thyroid disease. GSEA was then performed to test whether the genes of patients in different risk were enriched in previously defined biological pathways. We found that the genes of patients in low risk was enriched in cell cycle, durg metabolism cytochrome 450, mismatch repair, regulation of the cytoskeleton, and ubiquitin mediated proteolysis. Patients in high risk group were enriched in endometrial cancer, fatty acid metabolism, glycerolipid metabolism, type I diabetes, tyrosine metabolism (Fig. 4 F). Next, to investigate the correlation between TIICs and risk signature in EC, we used CIBERSORT to calculate infiltration of 22 immune cells in the EC cases. Then, we compared the infiltration of 22 immune cells in distinct signature. The differentially analytical results showed that CD8 T cells, T cells CD4 memory activated, Tregs, NK cells resting, NK cells activated, and Mast cells resting were significantly different in two groups (Fig. 4 G). Distribution of risk score and correlation with mRNSsi and TMB We next analyzed the distribution of risk score in different clinicopathological features, named cancer status, grade, lymph node metastasis, recurrence, peritoneal cytology, and tumor stage (Fig. 5 A-F). The results revealed that invasive and poor clinical features had higher risk scores, such as patients with tumor, tumor grade 3, positive LNM, recurrence, positive peritoneal cytology, and tumor stage IV, which is significant. Correlative analysis indicated that mRNAsi is positive associated with risk score (Fig. 5 G), while tumor mutation burden (TMB) is negative related with risk score (Fig. 5 H). We then combined the risk signature with mRNAsi or TMB score, and divided the cohort into for subgroups according to its corresponding features. The results showed that patients in low risk group and high mRNAsi subgroup had the best prognosis, and patients from high risk group with high mRNAsi subgroup tended to survive shorter than the patients from other subgroups (Fig. 5 I). On the other analysis, patients with low risk score and high TMB had a longer survival time, while high risk score with low TMB patients had a worse prognosis (Fig. 5 J). Construction And Evaluation Of The 7-crl Related Nomogram Based on the multivariate Cox regression analyses, age, tumor grade, stage, and 7-CRL signature were proved to be independent predictors for OS. Then a nomogram that integrated the risk score model and these clinicopathological characteristics was constructed to quantify the predictive results of OS probability of the EC patients (Fig. 6 A). The C-index for the nomogram was 0.760, and the calibration curves of the nomogram showed great consistency between the predicted OS rates and actual survival situation at 1, 3, and 5 years (Fig. 6 B). We calculated the total score of each patient according to the nomogram (Table 3 ) and then categorized the patients into three subgroups evenly as low-, moderate-, and high score subgroups in the basis of total score. Additionally, ROC curve analysis showed that the nomogram provided a more accurate prediction for OS at 1-year survival (AUC = 0.729), 3-year survival(AUC = 0.792) and 5-year survival (AUC = 0.822, Fig. 6 C). Kaplan-Meier analysis indicated that the survival status of the patients in the low score group was significantly better than that in the patient of medium-risk group and high-risk group ( p < 0.001, Fig. 6 D). To further evaluate and test the nomogram, we conducted the survival analysis in different clinical subgroups, including tumor grade (G1-2, G3) and FIGO stage (Stage I and Stage II-IV). The test of survival model showed the similar results, and these patients presented the same predictive tendencies, which suggested that this nomogram could accurately differentiate patients in the whole groups and in different clinicopathological subgroups (Fig. 6 E-H). These results indicated that our nomogram not only have high accuracy in predicting OS for EC patients, but also can find specific patients in different clinicopathological characteristics. Table 3 Corresponding risk score for each variable and total score. Variables Score age < 60 0 ≥ 60 30 tumor grade G1 0 G2 35 G3 100 FIGO stage I 0 II 20 III 50 IV 75 risk score low 0 high 70 total score low 0-100 moderate 110–160 high ≥ 170 FIGO, International Federation of Gynecology and Obstetrics. In Vitro Experimental Validation For Linc01615 We first performed the western blot (WB) validation in different cell lines following the steps described above. We chose LINC01615 as our target because this molecule is the only one that have obvious promotion function for cancer progression. We then verified the expression of LINC01615 in five cell lines including HEC-1A, HEC-1B, AN3CA, ishikawa, and RL-952, and find that the expression of LINC01615 is higher in HEC-1B, AN3CA, and ishikawa (Fig. 7 A). We then knockdown LINC01615 with siRNA in AN3CA (Fig. 7 B), and conducted transwell experiments to find whether LINC01615 had an influence on the invasion of EC cell lines. The result showed that knockdown of LINC01615 significantly inhibited the invasive ability of EC cell line AN3CA (Fig. 7 C). Discussion Endometrial carcinoma (EC) is one of the most common gynecologic malignancies in the clinic. Although surgery and radiotherapy can relieve symptoms in most patients, the prognosis of advanced endometrial cancer is relatively poor [ 16 ]. Therefore, it is urgent to explore new molecule for targeted therapy, which can help to prevent EC more effectively and have important theoretical and practical significance for improving the quality of life of women in China. In this study, we first investigated the correlation among cuproptosis-related lncRNAs and conducted difference analysis to find the key CRLs. Furthermore, the predictive model of EC prognosis based on 7 CRLs was constructed and verified by narrowing the scope through LASSO, and the expression of these lncRNAs in tumor tissues was then found to be different from that in normal tissues and the survival analysis is also different. Studies have confirmed that the prediction model has a high accuracy in predicting the prognosis of EC. After incorporating the 7-CRL risk model into the nomogram, the accuracy of prediction is further improved. The tumor immune microenvironment and potential association with immune targets of high and low risk groups were also investigated. Finally, we verified the expression and function of LINC01615 in vitro. Cuproptosis is a copper-dependent cell death presented by a recent article, as a novel form of Programmed cell death (PCD). Recently, cuproptosis has been considered as a copper-triggered mode of mitochondrial cell death[ 17 ]. Copper can be involved in angiogenesis, tumor growth and metastasis, and cancer cells can adapt to adverse microenvironment by regulating copper metabolism [ 18 ]. CRLs can be used to predict the prognosis and immune microenvironment of patients suffering from bladder cancer, which high risk patients were enriched in several immune-related pathways such as cell proliferation, nucleotide metabolism, and inflammatory immune response [ 19 ]. The cuproptosis is closely related to immune system [ 20 ]. In cancer cells, copper supplements increase the function and level of PD-L1 at mRNA and protein levels. Copper modulates key signaling pathways that control PD-L1 induced cancer immune escape and promote mediated degradation of PD-L1 [35545174]. It may therefore be possible to change copper metabolism and improve immune function by regulating copper metabolism-related lncRNAs [ 21 ]. Previous studies have shown that lncRNAs play an important regulatory role in the development and progression of EC [ 22 ]. For example, m6A-related lncRNAs play an important role and could accurately forecast the prognosis of patients with EC [ 23 ]. Mechanically, lncRNAs play a crucial role in EC progression by multiple patterns such as signaling, decoying, scaffolding, and guidance [ 24 , 25 ]. Cuproptosis-associated lncRNA was a good predictor of prognosis in patients with many types of tumors, such as hepatocellular carcinoma. [ 26 , 27 ], cervical cancer [ 28 ], and melanoma [ 29 ]. Prognosis of patients with EC had not been studied by constructing predictive features of lncRNA associated with cuproptosis. Therefore, it was essential to identify predictive features of lncRNA associated with cuproptosis in patients with EC. However, the association between copper metabolism and endometrial cancer remains uncertain. Especially, little is currently known about the mechanisms of action of cuproptosis-related lncRNAs in EC. In our study, we found the significant correlation between CRLs and the prognosis, which mechanism might function through immune, metabolism, and enhanced invasive ability. CRL related nomogram can better predict the survival of EC. Several studies have reported that LINC01615 was associated with diverse tumors. In one study, LINC01615 was found potentially affected the extracellular matrix and had further impacts on the metastasis of hepatocellular carcinoma [ 30 ]. In triple-negative breast cancer, it was found that the high expression of SIPA1 can promote the level of LINC01615, and therefore up-regulation of LINC01615 can promote the migration and invasion cancer cells through MMP9 [ 31 ]. LINC01615 can also competitively bind with miR-3653-3p to regulate ZEB2 and promote tumorigenesis of colon cancer cells [ 32 ]. LINC01615 is also involved in prognostic network for cancers in predictive models. For example, in a ferroptosis-related-lncRNA model, LINC01615 is revealed to be highly expressed in gastric cancer cell lines and tissues. A nuclear-cytoplasmic fractionation assay confirmed that most LINC01615 was enriched in the cytoplasm in gastric cancer cell lines. Bioinformatics further predicts four potential target miRNAs of LINC01615 and then figured out 26 target ferroptosis-related mRNAs, which could act as a ceRNA to get involved in ferroptosis [ 33 ]. In the current research, LINC01615 is the only lncRNA that is highly expressed in EC tissues, and the overall survival of patients with high expression of LIN01615 is poor. Our in vitro experiment also confirmed that knock down the expression of LINC01615 can inhibit the invasion of endometrial cancer cells, and the specific molecular mechanism remains to be further clarified. Since the prognosis of patients in the high-risk and the low-risk cohorts was diverse, enrichment analysis was enforced to study possible differences between the two risk subgroups. GSEA identified that cytoskeleton, metabolism, and cell cycle were might be differentially expressed between the low-risk group and the high-risk group. New evidences are presented for differences in metabolism between healthy and EC cells, which has increased considerably in recent years. Alterations in lipid or sugar metabolism affect important processes such as cell growth, proliferation, and differentiation [ 34 ]. The relationship between metastasis and cytoskeleton are reviewed by some studies. TRPV4 and calcium influx acted on the cytoskeleton via the RhoA/ROCK1 pathway, ending with LIMK/cofilin activation, which had an impact on two cytoskeletal levels, F-actin and paxillin (PXN) [ 35 ]. Cytoskeleton was also found to be associated with mechanical stimulus-related genes in EC. Xu et al built a mechanical-related signature and it outperformed in predicting OS of EC patients. Further experimental results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree [ 36 ]. Nonetheless, our study has certain limitations. Firstly, publicly available data such as RNA-seq and clinical data, may have certain drawbacks when analyzing the prognostic performance of gene signatures. Moreover, because no other databases have available lncRNA expression and clinical data, the model had to be validated in one database. Finally, the mechanisms of cuproptosis and the functions of CRLs, especially LINC01615 in vitro and in vivo are not clear, which demands for further in-depth studies for EC. Conclusion In summary, multiple bioinformatics methods were used to identify cuproptosis-related signature and a seven cuproptosis-related lncRNAs signature was revealed. A risk model and a predictive nomogram were built and further selected as prediction to distinguish between high risk and low-risk groups and identify the differences in immune infiltration and TMB values. Furthermore, we proved that LINC01615 could be acted as a potential therapeutic target for EC following in vitro studies. The findings of this study may provide new strategies for exploring the mechanisms of cuproptosis and expand current insights into therapeutic approaches for EC patients. Declarations Acknowledgment We are grateful to all donors who participated in this research. Author Contribution Conceived and designed the experiments: Xuecheng Pang; Performed the data collection: Sumin Qian; Analyzed the data: Xuecheng Pang; Contributed reagents/materials/analysis tools: Xuecheng Pang; Contributed to the writing of the manuscript: Xuecheng Pang and Sumin Qian; All authors reviewed and approved the manuscript. Ethics Statement -Approval of the research protocol by an Institutional Reviewer Board. N/A. - Informed Consent.* N/A. - Registry and the Registration No. of the study/trial. N/A. - Animal Studies. N/A. Conflict of interest No potential conflict of interest was disclosed. Funding information The authors received no specific funding for this work. Data availability statement The datasets presented in this study can be found in online databases. The names of the databases and accession number(s) can be found in the article. References Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J Clin. 2022, 72 , 7–33. Jiang, X.; Tang, H.; Chen, T. Epidemiology of gynecologic cancers in China. J Gynecol Oncol. 2018, 29 , e7. Steiner, E.; Eicher, O.; Sagemuller, J.; Schmidt, M.; Pilch, H.; Tanner, B.; Hengstler, J.G.; Hofmann, M.; Knapstein, P.G. Multivariate independent prognostic factors in endometrial carcinoma: a clinicopathologic study in 181 patients: 10 years experience at the Department of Obstetrics and Gynecology of the Mainz University. Int J Gynecol Cancer. 2003, 13 , 197–203. Evans, J.R.; Feng, F.Y.; Chinnaiyan, A.M. The bright side of dark matter: lncRNAs in cancer. J Clin Invest. 2016, 126 , 2775–2782. Quinn, J.J.; Chang, H.Y. Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet. 2016, 17 , 47–62. Li, Z.; Hong, S.; Liu, Z. LncRNA LINC00641 predicts prognosis and inhibits bladder cancer progression through miR-197-3p/KLF10/PTEN/PI3K/AKT cascade. Biochem Biophys Res Commun. 2018, 503 , 1825–1829. Xie, P.; Cao, H.; Li, Y.; Wang, J.; Cui, Z. Knockdown of lncRNA CCAT2 inhibits endometrial cancer cells growth and metastasis via sponging miR-216b. Cancer Biomark. 2017, 21 , 123–133. Dong, P.; Xiong, Y.; Yue, J.; Xu, D.; Ihira, K.; Konno, Y.; Kobayashi, N.; Todo, Y.; Watari, H. Long noncoding RNA NEAT1 drives aggressive endometrial cancer progression via miR-361-regulated networks involving STAT3 and tumor microenvironment-related genes. J Exp Clin Cancer Res. 2019, 38 , 295. Cobine, P.A.; Brady, D.C. Cuproptosis: Cellular and molecular mechanisms underlying copper-induced cell death. Mol Cell. 2022, 82 , 1786–1787. Ruiz, L.M.; Libedinsky, A.; Elorza, A.A. Role of Copper on Mitochondrial Function and Metabolism. Front Mol Biosci. 2021, 8 , 711227. Dankner, M.; Rose, A.A.N.; Rajkumar, S.; Siegel, P.M.; Watson, I.R. Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations. Oncogene. 2018, 37 , 3183–3199. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43 , e47. Liu, S.; Xie, X.; Lei, H.; Zou, B.; Xie, L. Identification of Key circRNAs/lncRNAs/miRNAs/mRNAs and Pathways in Preeclampsia Using Bioinformatics Analysis. Med Sci Monit. 2019, 25 , 1679–1693. Liberzon, A.; Birger, C.; Thorvaldsdottir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015, 1 , 417–425. Li, X.C.; Cheng, Y.; Yang, X.; Zhou, J.Y.; Dong, Y.Y.; Shen, B.Q.; Wang, J.Q.; Zhao, L.J.; Wang, Z.Q.; Li, X.P.; et al. Decreased expression of TRPM4 is associated with unfavorable prognosis and aggressive progression of endometrial carcinoma. Am J Transl Res. 2020, 12 , 3926–3939. Hussein, Y.R.; Soslow, R.A. Molecular insights into the classification of high-grade endometrial carcinoma. Pathology. 2018, 50 , 151–161. Tang, D.; Chen, X.; Kroemer, G. Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res. 2022, 32 , 417–418. Denoyer, D.; Masaldan, S.; La Fontaine, S.; Cater, M.A. Targeting copper in cancer therapy: 'Copper That Cancer'. Metallomics. 2015, 7 , 1459–1476. Zhang, Y.; Li, X.; Li, X.; Zhao, Y.; Zhou, T.; Jiang, X.; Wen, Y.; Meng, W.; Li, S. Comprehensive analysis of cuproptosis-related long noncoding RNA immune infiltration and prediction of prognosis in patients with bladder cancer. Front Genet. 2022, 13 , 990326. Wang, Y.; Huang, X.; Chen, S.; Jiang, H.; Rao, H.; Lu, L.; Wen, F.; Pei, J. In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma. Curr Oncol. 2022, 29 , 6573–6593. Zhou, Y.; Shu, Q.; Fu, Z.; Wang, C.; Gu, J.; Li, J.; Chen, Y.; Xie, M. A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma. Front Genet. 2022, 13 , 959456. Dong, P.; Xiong, Y.; Konno, Y.; Ihira, K.; Kobayashi, N.; Yue, J.; Watari, H. Long non-coding RNA DLEU2 drives EMT and glycolysis in endometrial cancer through HK2 by competitively binding with miR-455 and by modulating the EZH2/miR-181a pathway. J Exp Clin Cancer Res. 2021, 40 , 216. Shan, L.; Lu, Y.; Xiang, C.C.; Zhu, X.; Zuo, E.D.; Cheng, X. Identification of Five m6A-Related lncRNA Genes as Prognostic Markers for Endometrial Cancer Based on TCGA Database. J Immunol Res . 2022, 2022 , 2547029. Liu, H.; Wan, J.; Chu, J. Long non-coding RNAs and endometrial cancer. Biomed Pharmacother. 2019, 119 , 109396. Zhang, L.; Wan, Y.; Zhang, Z.; Jiang, Y.; Gu, Z.; Ma, X.; Nie, S.; Yang, J.; Lang, J.; Cheng, W.; et al. IGF2BP1 overexpression stabilizes PEG10 mRNA in an m6A-dependent manner and promotes endometrial cancer progression. Theranostics. 2021, 11 , 1100–1114. Chen, S.; Liu, P.; Zhao, L.; Han, P.; Liu, J.; Yang, H.; Li, J. A novel cuproptosis-related prognostic lncRNA signature for predicting immune and drug therapy response in hepatocellular carcinoma. Front Immunol. 2022, 13 , 954653. Zhu, H.; Mao, F.; Wang, K.; Feng, J.; Cheng, S. Cuproptosis-related lncRNAs predict the clinical outcome and immune characteristics of hepatocellular carcinoma. Front Genet. 2022, 13 , 972212. Liu, X.; Zhou, L.; Gao, M.; Dong, S.; Hu, Y.; Hu, C. Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer. Front Genet. 2022, 13 , 989646. Yang, X.; Wang, X.; Sun, X.; Xiao, M.; Fan, L.; Su, Y.; Xue, L.; Luo, S.; Hou, S.; Wang, H. Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma. Front Genet. 2022, 13 , 972899. Ji, D.; Chen, G.F.; Liu, X.; Zhu, J.; Sun, J.Y.; Zhang, X.Y.; Lu, X.J. Identification of LINC01615 as potential metastasis-related long noncoding RNA in hepatocellular carcinoma. J Cell Physiol. 2019, 234 , 12964–12970. Xiang, Y.; Feng, L.; Liu, H.; Liu, Y.; Li, J.; Su, L.; Liao, X. SIPA1 Regulates LINC01615 to Promote Metastasis in Triple-Negative Breast Cancer. Cancers (Basel) . 2022, 14 . Hu, Z.; Yang, C.; Guo, S.; Li, Y.; Li, Y. LINC01615 activates ZEB2 through competitively binding with miR-3653-3p to promote the carcinogenesis of colon cancer cells. Cell Cycle. 2022, 21 , 228–246. Zhang, S.; Zheng, N.; Chen, X.; Du, K.; Yang, J.; Shen, L. Establishment and Validation of a Ferroptosis-Related Long Non-Coding RNA Signature for Predicting the Prognosis of Stomach Adenocarcinoma. Front Genet. 2022, 13 , 818306. Efsun Antmen, S.; Canacankatan, N.; Gurses, I.; Aytan, H.; Erden Erturk, S. Relevance of lipogenesis and AMPK/Akt/mTOR signaling pathway in endometrial cancer. Eur Rev Med Pharmacol Sci. 2021, 25 , 687–695. Li, X.; Cheng, Y.; Wang, Z.; Zhou, J.; Jia, Y.; He, X.; Zhao, L.; Dong, Y.; Fan, Y.; Yang, X.; et al. Calcium and TRPV4 promote metastasis by regulating cytoskeleton through the RhoA/ROCK1 pathway in endometrial cancer. Cell Death Dis. 2020, 11 , 1009. Xu, X.; Li, X.; Zhou, J.; Wang, J. Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer. Front Oncol. 2021, 11 , 753910. Additional Declarations No competing interests reported. Supplementary Files FigureS1.docx FigureS1.jpg TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-2255910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":152889099,"identity":"647c9045-bf1e-4c7b-8d92-4dbfa6017284","order_by":0,"name":"Xuecheng Pang","email":"","orcid":"","institution":"Cangzhou Central Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuecheng","middleName":"","lastName":"Pang","suffix":""},{"id":152889100,"identity":"79b4a22e-4525-41fb-b861-c6cc6471c53d","order_by":1,"name":"Sumin Qian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYHACxgMMFRJy/CTpOcBwxsJYsoEkLYxtFYkbiNbCd/50wqGb8yQYNzAwP3x0gxgtkjdyNxzO3SbBbM7AZmycQ4wWgxu8YC1slg08bNLEaTl/FqhljgSPwQGitRwAOaxBQoJ4LWC/5ByTMJBsJtYvfOfPbnycU1NX38/e/PAxUVqA8QgFzEQpR9EyCkbBKBgFowAXAACUhDRZsjeA2AAAAABJRU5ErkJggg==","orcid":"","institution":"Cangzhou Central Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sumin","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2022-11-09 14:59:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2255910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2255910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29338580,"identity":"5ac91b70-fd71-4102-b0da-934eab292aae","added_by":"auto","created_at":"2022-11-21 15:35:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1539889,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and screening of cuproptosis-related lncRNA in TCGA-EC cohort (A) Co-expression analysis of cuproptosis related locRNAs. B-C. volcano plot and heatmap of differentially expressed cuproptosis-related lncRNAs.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/79ab914fd8d7805676ba5293.jpg"},{"id":29338577,"identity":"52ae00a3-ce5f-42c0-a96c-102ea306d453","added_by":"auto","created_at":"2022-11-21 15:35:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1581838,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of a signature for predicting features of cuproptosis-related lncRNA. (A) Cross validation diagram. (B) LASSO coefficients of prognostic lncRNAs. C. Correlation relationships among the 7 key prognostic lncRNAs. D. Expression of 5 key lncRNAs between normal and EC tissues, including AC008073.2, AC009229.1, AF131215.7, CDKN2A.DT, LINC01615. E. Kaplan-Meier survival curve between low- and high-expression of 5 key lncRNAs.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/a65509995ab42a9a70a196d4.jpg"},{"id":29338584,"identity":"1811ed32-7b5d-4068-83a3-51dca34f5443","added_by":"auto","created_at":"2022-11-21 15:35:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1966461,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the cuproptosis-related lncRNA model in predicting the survival of EC patients. A. Cluster heat map of 7 cuproptosis -related lncRNAs and clinical characteristics stratified by the CRL-related risk model. Red represents the expression of lncRNAs were upregulated while blue indicates downregulated. The relationship between risk level and each clinicopathological characteristic were measured with the χ2 test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. B. Rank of risk score and distribution of groups. Patients with EC were divided into low- and high-risk subgroups based on the median value of the risk score calculated (above). The survival status and survival time of patients with EC ranked by risk score (below). C. Kaplan–Meier curve of the two risk groups according to risk score. D time-dependent ROC analysis of risk model. E. Univariate and multivariate regression model with clinical features and 7-CRL related risk model for evaluating the independent prognostic value.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/2b09bf60d27943849685a45c.jpg"},{"id":29340783,"identity":"8c64dfab-25a7-440d-943c-c45587048c40","added_by":"auto","created_at":"2022-11-21 15:59:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2457490,"visible":true,"origin":"","legend":"\u003cp\u003eImmune and function analysis for the risk model. A. Correlation between risk score and stromal score. B. Correlation between risk score and immune score. C. Correlation between risk score and ESTIMATE score. D. Stromal score, immune score, and ESTIMATE score in low and high-risk groups. E. Heatmap of GSVA and analysis of KEGG pathway in two risk groups. F. Gene set enrichment analysis (GSEA) results of significant biological functions in different risk model. G. The abundance of each TME infiltrating cell in different risk models. The upper and lower ends of the boxes represented interquartile range of values. The lines in the boxes represented median value, and black dots showed outliers. The asterisks represented the statistical P-value (*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; ****P \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/24de9beb527d0ed332efb4e2.jpg"},{"id":29339700,"identity":"af395344-c2c0-4833-9a02-f0165ea0d184","added_by":"auto","created_at":"2022-11-21 15:43:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1463453,"visible":true,"origin":"","legend":"\u003cp\u003eEstimate of the risk model with clinicopathological features and survival. Distribution of risk scores in different A. cancer status, B. grade, C. LNM, D. recurrence, E. peritoneal cytology, F. stage. G. Correlation between risk score and mRNAsi. H. Correlation between risk score and TMB. I. Survival curve stratified by risk level and mRNAsi. J. Survival curve stratified by risk level and TMB.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/a7b76437777aa66c47e87f7d.jpg"},{"id":29338586,"identity":"bd5d8225-679a-472b-8534-6144615dc3d3","added_by":"auto","created_at":"2022-11-21 15:35:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1636563,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of nomogram predicting 1-, 3- or 5-year OS in EC. A. Nomogram for predicting 1-, 3-, 5-year OS in EC patients. There were four factors containing age, grade, stage, and risk score in the nomogram. Each of them generates points according to the line drawn upward, and the total points of the seven components of an individual patient lie on “Total Points” axis which corresponds to the probability of 1- year, 3-year and 5-year OS rate plotted on the two axes below. B. The calibration plots for predicting patient 1-, 3-, and 5-year OS. C. Predictive accuracy evaluating by area under the ROC curve (AUC) for 1-, 3-. And 5-year OS. D. Survival curve of patients classified by the nomogram as low-, moderate-, and high-score. Survival curve in different subgroups including E. G1-2, F. G3, G. Stage I, H. Stage II-IV.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/385a81048b232838908055c7.jpg"},{"id":29339701,"identity":"c132d2d1-2f54-497b-811d-b8d48ade87c1","added_by":"auto","created_at":"2022-11-21 15:43:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":253017,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of key lncRNA from the 7-CRL related risk model. A. Expression of LINC01615 in five EC cell lines, including HEC-1A, HEC-1B, AN3CA, ishikawa, and RL-952. B. Knockdown efficiency for LINC01615 in AN3CA cells. C. Transwell experiment in negative control (NC) and si-LINC01615 groups.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/36a7190a1b023cfcf918d37f.jpg"},{"id":31201074,"identity":"da60be4f-8169-4232-92ac-7753c53fb646","added_by":"auto","created_at":"2023-01-06 06:44:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1628043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/3a25b549-d363-4757-bc2a-4ea66748b9ca.pdf"},{"id":29338583,"identity":"a2540f12-66c8-4a36-b4e2-4bb4902f45e3","added_by":"auto","created_at":"2022-11-21 15:35:16","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":14145,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/846d867b0e11ead7432ef657.docx"},{"id":29339704,"identity":"828ebc36-fbe2-49b4-a880-a3a4094b1c6d","added_by":"auto","created_at":"2022-11-21 15:43:16","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":428555,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/742df9e651ca94e776ffd73d.jpg"},{"id":29340267,"identity":"918b5bc0-44f4-487e-a7f0-05944bdf1622","added_by":"auto","created_at":"2022-11-21 15:51:16","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":12681,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2255910/v1/e68c925d883cc8949bc8c61a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is the most common cancer of the female reproductive system worldwide, ranking second among female genital cancers, with a mortality rate of 2.7 per 100,000 in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although most endometrial cancers are diagnosed at early and treatable stages, late diagnosis of endometrial cancers at advanced stages remains challenging to treat. According to the International Federation of Gynecology and Obstetrics (FIGO) staging system, the five-year survival for EC is over 90% for phase I, 70% for phase II, 60% for phase III, and 8% for stage IV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Advanced EC, which is characterized by highly aggressive and easily metastatic clinical behavior, shows poor outcomes. Therefore, it is imperative to find more effective biomarkers to facilitate novel therapeutic methods.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are regulatory RNA transcripts longer than 200 nucleotides without coding capacity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It played an important regulatory role in immune response processes, such as immune cell infiltration, antigen recognition, antigen exposure, and tumor clearance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Increasing research suggested that abnormal expression of lncRNA impairs homeostasis in organisms and may promote or inhibit some cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, it has been demonstrated that lncRNA CCAT2 was highly expressed in endometrial cancer tissues. Knockdown of CCAT2 inhibited cell growth and metastasis of endometrial cancer cells by sponging miR-216b [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. LncRNA NEAT1 functions as an oncogenic sponge for the tumor suppressor miR-361, which suppresses proliferation, invasion, and sphere formation by directly targeting the oncogene STAT3. Furthermore, NEAT1 also suppressed the expression of multiple metastatic genes and tumor microenvironment-related genes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although there are more and more studies on the role of lncRNA in cancer, our understanding of the role of lncRNA in the occurrence is less.\u003c/p\u003e \u003cp\u003eCuproptosis is a new regulatory cell death pattern, distinguished from other regulatory cell death features like pyroptosis, ferroptosis and apoptosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The most important element for cuproptosis is copper, which is an indispensable cofactor for all organisms to maintain life activities, as it plays an important role in biological processes such as mitochondrial respiration, antioxidant/detoxification, and iron uptake [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Copper is also an essential micronutrient with both beneficial and detrimental functions. It is reported that copper could promote cell proliferation through the activation of RAS-RAF-MEK-ERK signaling cascade [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The roles of cuproptosis-related lncRNAs (CRLs) in the development and outcome of EC have not yet been determined. In this study, we screened cuproptosis-related lncRNAs in EC patients to construct and validate a novel prognostic signature. We further analyzed its underlying value in predicting the diagnosis, prognosis, and tumor immune infiltration of patients with EC. The signature outperformed in predicting the prognosis of EC patients and benefits patients in guiding individualized immune therapy.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample selection and data processing\u003c/h2\u003e \u003cp\u003eThe mRNA-sequencing data were downloaded for 532 EC and 35 normal tissues with corresponding clinicopathological features from the TCGA database. The clinical data included age, menopause, FIGO stage, tumor grade, histological type, recurrence, peritoneal cytology, and lymph node metastasis, and overall survival. Patients without complete clinical data were excluded. We further differentiated the transcriptomic data of TCGA-EC from mRNA and lncRNA and collected 20,475 mRNAs and 14,142 lncRNAs in EC. We first reviewed the literature and summarized the cuproptosis-related genes, and obtained a total of 19 genes (see \u003cb\u003eTable S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eNext, the expression of cuproptosis-related lncRNAs (CRLs) was obtained by the R \u0026ldquo;limma\u0026rdquo; package [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. According to the correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.3 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 73 CRLs and their expression were identified. Finally, the co-expression of cuproptosis-related genes and CRLs were analyzed using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;ggalluvial\u0026rdquo; R package [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to observe the interaction. The Institutional Ethics Committee (Human Research) of our hospital approved this study. We confirm that all methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction And Verification Of Risk Model\u003c/h3\u003e\n\u003cp\u003eWe use the LASSO regression analysis to narrow the number of lncRNAs by the R package\u0026ldquo;glmnet\u0026rdquo;. We also explored the correlation among these selected CRLs with the \u0026ldquo;corrplot\u0026rdquo; R package. Finally, the risk signature was constructed with hub genes and the coefficient for each gene was obtained through the penalizing process (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The formula to calculate risk score was listed as following: risk model= (exprgene1 \u0026times; coefficientgene1) + (exprgene2 \u0026times; coefficientgene2) + ⋯ + (expression of gene n\u0026times; coefficient gene n), where n is the number of CRLs modules. According to the optimal cut-off value investigated by using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo;, the patients were stratified into high-risk and low-risk groups according to the median of the risk score. Kaplan-Meier (K-M) survival curves and time-dependent receptor operating characteristic (ROC) curves were plotted to evaluate the predictive accuracy of the risk score model on OS. Expression of each CRL was also compared in different risk groups. Furthermore, survival curve was also plotted according to the median value of each CRL.\u003c/p\u003e \n\u003ch3\u003eConstruction And Evaluation Of A Predictive Nomogram And External Validation\u003c/h3\u003e\n\u003cp\u003eWe carried out univariate and multivariate Cox regression analyses were performed to confirm whether the prognostic signature was independent of other clinical characteristics in predicting OS of patients with EC. Hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable were calculated. A predictive nomogram was constructed with these independent prognostic factors using \u0026ldquo;rms\u0026rdquo; and \u0026ldquo;Hmisc\u0026rdquo; R packages. The consistency index (C-Index) and calibration were used to evaluate the prediction accuracy of the nomogram. The whole group was divided into three subgroups including high-, moderate, and low-score subgroups by the prognostic risk value calculated using the nomogram. K-M survival curve was performed to evaluate predictive performance of nomogram and ROC curve analysis was to validate the sensitivity and specificity of the nomogram compared to the single risk score model in predicting OS. A subgroup analysis was conducted by classifying the patients into different clinicopathological stratifications (tumor grade and FIGO stage).\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eWe performed the GO function enrichment analysis and KEGG pathway enrichment analysis to functionally annotate the differentially expressed genes. Both of them were conducted by the \u0026ldquo;clusterProfiler\u0026rdquo; package. We also conducted GSEA to study the functions associated with different risk groups of EC, in which |NES| \u0026gt; 1 and nom \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\n\u003ch3\u003eGene Set Variation Analysis (Gsva)\u003c/h3\u003e\n\u003cp\u003eTo explore the variation in biological processes between distinct stemness-mediated subtypes, we used the ssGSEA method plus the Gene Set Variation Analysis (GSVA) package in R to evaluate infiltration levels of different immune cells, the related expression pathways, and the activity of immune-related functions. The gene set \u0026ldquo;c2.cp.kegg.v7.4.symbols.gmt\u0026rdquo; derived from MsigDB database was used as the well-defined biological signature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Besides, to assess the enrichment level of 23 immune cell subtypes, we conducted ssGSEA and normalized the abundance score to unity distribution from 0 to 1. The bio-similarity of tumor-infiltrating immune cells was estimated using the multidimensional scaling and Gaussian fitting model.\u003c/p\u003e\n\u003ch3\u003eImmune Cell Infiltration And The Tumor Microenvironment Analysis\u003c/h3\u003e\n\u003cp\u003eCIBERSORT is a deconvolution method for expression matrices of immune cell subsets. LM22 is a gene signature matrix that specifies the content of immune cell types. We used the CIBERSORT package in R to calculate the number of immune cells per sample, setting the permutation to 1000 and selecting P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the screening threshold. Moreover, immune scores of different subgroups were calculated with the package \u0026ldquo;estimate\u0026rdquo;, and plot histograms of differences in immune scores, stromal scores, ESTIMATE score, and tumor purity of each EC tumor sample. Tumor mutational burden (TMB) in each tumor sample was referred to the number of mutated bases in per million bases, which included missense mutation, nonsense mutation, frameshift mutation and so on. We computed the TMB values with the number of variants at the length of the human exons (38\u0026nbsp;million) for each sample by Perl scripts.\u003c/p\u003e\n\u003ch3\u003eIn Vitro Validation Of Hub Genes\u003c/h3\u003e\n\u003cp\u003eFive EC cell lines, HEC-1A, HEC-1B, AN3CA, ishikawa, and RL-952 were obtained from the National Collection of Authenticated Cell Culture of Chinese Academy of Sciences. All cells were cultured with DMEM containing 10% fetal bovine serum (FBS; Gibco, Gaithersburg, MD, USA), 100 U/ml penicillin, and 100 \u0026micro;g/ml streptomycin in an incubator with 95% humidified air containing 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C. siRNA for LINC01615 was transferred to AN3CA according to the manufacturer\u0026rsquo;s instructions. Total protein of EC cell lines were extracted and used for western blot as described earlier. GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) was taken as an internal control. Transwell experiment is conducted as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eStudent\u0026rsquo;s two-sided \u003cem\u003et\u003c/em\u003e-tests, nonparametric tests, Chi-square tests, and one-way ANOVA tests were used to compare the differences between two groups. The log-rank test was applied in Kaplan-Meier analysis to compare the OS between different groups. Univariate and multivariate Cox regression analyses were implemented to identify hazard ratio (HR) and its 95% confidence interval (CI). All statistical analyses were performed with R software (The R Foundation; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 3.6.3). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cuproptosis-related lncRNAs and differential analysis\u003c/h2\u003e \u003cp\u003eA total of 532 EC samples and 35 normal samples from the TCGA database were finally enrolled. The detailed clinical characteristics of the 532 EC patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In total, 19 curoptosis-related genes were identified. The landscape and connections of the interactions among the 19 cuproptosis related genes and its related lncRNAs were illustrated in the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The differentially expressed CRLs (DE-CRLs) in the patients with EC were visualized in a volcano map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). As the figure shown, there were 18 up-regulated and 55 down-regulated DE-CRLs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients from TCGA dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTCGA cohort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e༞=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e༜60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFIGO stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epost-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epre-menopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePeritoneal cytology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCancer status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith tumer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFIGO, International Federation of Gynecology and Obstetrics; G, grade; EEA, endometrioid endometrial adenocarcinoma; LNM, lymph node metastasis.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHub genes and correlated coeffificient value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuproptosis-related gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoeffificient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC008073.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03360684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC009229.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11364294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF131215.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00094354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL807761.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0015656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2A.DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15255722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07124071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0138985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003erisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow: \u0026lt; 1.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh: \u0026ge; 1.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eConstruction And Validation Of A Prognostic Model By The Crls\u003c/h3\u003e\n\u003cp\u003eUsing LASSO Cox regression analysis, we finally determined that 7 prognostic CRLs (AL807761.3, AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, LINC01166) were significantly associated with prognosis in EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). The signature, which consisted of the 7 key prognostic CRLs, was constructed using the respective regression coefficients. The risk scores of EC patients were calculated according to the following formula: Risk score = (AC008073.2\u0026times;0.033606845)+ (AC009229.1\u0026times;0.113642941) + (AF131215.7\u0026times;0.000943541) + (AL807761.3\u0026times;-0.001565595) + (CDKN2A.DT\u0026times;0.152557217)+ (LINC01166\u0026times;0.071240712)+ (LIN01615\u0026times;-0.013898506). Then a prognostic model was built according to the coefficient for each which was obtained through the penalizing process. The relationships among the nine genes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, and LINC01166 were positively correlated with each other, but AL807761.3 was negatively correlated with the other six genes. The expression of the 7 CRLs was compared between normal and tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cb\u003eFigure S1A-B\u003c/b\u003e). The results showed that AC008073.2, AC009229.1, AF131215.7, AL807761.3, and LINC01166 were significantly up-regulated in normal groups, while CDKN2A.DT and LINC01615 were down-regulated in the normal groups. Survival curves revealed that patients in high expression of AC008073.2, AC009229.1, AF131215.7, and CDKN2A.DT have a better OS compared with low expression group. On the other hand, the survival of high expression of LINC01615 was poor. The survival curve had no significant difference in AL807761.3 and LINC01166 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cb\u003eFigure S1C-D\u003c/b\u003e).\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruct the prognostic characteristics of cuproptosis in patients with\u003c/h2\u003e \u003cp\u003eThen we calculated the risk score of each patient, and the patients were stratified into high-risk group or low-risk group according to the median cut-off value. The expression levels of the 7 CRLs and the distribution of clinicopathological features in high- and low-risk groups were presented in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The results revealed that the clinical features, including cancer status, peritoneal cytology, recurrence, tumor grade, menopausal status, histological type, stage, age, and living status are significant differently distributed in two risk groups. The EC patients were then divided into low- and high-risk groups based on the median risk score. Compared with patients in the low-risk group, those in the high-risk group indicated poorer survival probability based on our risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The K-M curve indicated patients in the low-risk group had a significantly better overall survival (OS) compared to those in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). As shown in the time-dependent ROC analysis, the area under the curve (AUC) of the risk model reached 0.781 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Univariate and multivariate Cox regression analyses were carried out among the available variables to determine whether the predictive ability of the 7-CRL signature in predicting OS was independent of other traditional clinicopathological properties. It proved that the risk score of the prognostic signature (HR\u0026thinsp;=\u0026thinsp;1.908, 95% CI\u0026thinsp;=\u0026thinsp;1.179\u0026thinsp;\u0026minus;\u0026thinsp;3.089, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.009) was significantly associated with OS in multivariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmune characterization and functional analysis in different risk signatures\u003c/h2\u003e \u003cp\u003eTo verify the feasibility of the grouping strategy, we conducted an ESTIMATE analysis to profile the immune characteristics of EC based on the expression of immune cell types. Stromal, immune, and ESTIMATE scores were calculated and then we conducted a correlation analysis between these scores and risk scores. The results showed that the risk score decreased with the increase of stromal score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), immune score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and ESTIMATE score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The violin plot also further verified that there the high risk signature was significantly positively correlated with the ESTIMATE and immune and stromal scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). To further explore the biological behaviors among the two immune subtypes, we performed GSVA enrichment analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, low risk group was related to starch and sucrose metabolism, folate biosynthesis, fructose and mannose metabolism, amino sugar and nucleotide sugar metabolism, and butanoate metabolism. High risk group was enriched in glycosphingolipid biosynthesis ganglio series and autoimmune thyroid disease. GSEA was then performed to test whether the genes of patients in different risk were enriched in previously defined biological pathways. We found that the genes of patients in low risk was enriched in cell cycle, durg metabolism cytochrome 450, mismatch repair, regulation of the cytoskeleton, and ubiquitin mediated proteolysis. Patients in high risk group were enriched in endometrial cancer, fatty acid metabolism, glycerolipid metabolism, type I diabetes, tyrosine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Next, to investigate the correlation between TIICs and risk signature in EC, we used CIBERSORT to calculate infiltration of 22 immune cells in the EC cases. Then, we compared the infiltration of 22 immune cells in distinct signature. The differentially analytical results showed that CD8 T cells, T cells CD4 memory activated, Tregs, NK cells resting, NK cells activated, and Mast cells resting were significantly different in two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of risk score and correlation with mRNSsi and TMB\u003c/h2\u003e \u003cp\u003eWe next analyzed the distribution of risk score in different clinicopathological features, named cancer status, grade, lymph node metastasis, recurrence, peritoneal cytology, and tumor stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-F). The results revealed that invasive and poor clinical features had higher risk scores, such as patients with tumor, tumor grade 3, positive LNM, recurrence, positive peritoneal cytology, and tumor stage IV, which is significant. Correlative analysis indicated that mRNAsi is positive associated with risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), while tumor mutation burden (TMB) is negative related with risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). We then combined the risk signature with mRNAsi or TMB score, and divided the cohort into for subgroups according to its corresponding features. The results showed that patients in low risk group and high mRNAsi subgroup had the best prognosis, and patients from high risk group with high mRNAsi subgroup tended to survive shorter than the patients from other subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). On the other analysis, patients with low risk score and high TMB had a longer survival time, while high risk score with low TMB patients had a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction And Evaluation Of The 7-crl Related Nomogram\u003c/h3\u003e\n\u003cp\u003eBased on the multivariate Cox regression analyses, age, tumor grade, stage, and 7-CRL signature were proved to be independent predictors for OS. Then a nomogram that integrated the risk score model and these clinicopathological characteristics was constructed to quantify the predictive results of OS probability of the EC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The C-index for the nomogram was 0.760, and the calibration curves of the nomogram showed great consistency between the predicted OS rates and actual survival situation at 1, 3, and 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). We calculated the total score of each patient according to the nomogram (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and then categorized the patients into three subgroups evenly as low-, moderate-, and high score subgroups in the basis of total score. Additionally, ROC curve analysis showed that the nomogram provided a more accurate prediction for OS at 1-year survival (AUC\u0026thinsp;=\u0026thinsp;0.729), 3-year survival(AUC\u0026thinsp;=\u0026thinsp;0.792) and 5-year survival (AUC\u0026thinsp;=\u0026thinsp;0.822, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Kaplan-Meier analysis indicated that the survival status of the patients in the low score group was significantly better than that in the patient of medium-risk group and high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To further evaluate and test the nomogram, we conducted the survival analysis in different clinical subgroups, including tumor grade (G1-2, G3) and FIGO stage (Stage I and Stage II-IV). The test of survival model showed the similar results, and these patients presented the same predictive tendencies, which suggested that this nomogram could accurately differentiate patients in the whole groups and in different clinicopathological subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-H). These results indicated that our nomogram not only have high accuracy in predicting OS for EC patients, but also can find specific patients in different clinicopathological characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorresponding risk score for each variable and total score.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003etumor grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFIGO stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003erisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003etotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u0026ndash;160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFIGO, International Federation of Gynecology and Obstetrics.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eIn Vitro Experimental Validation For Linc01615\u003c/h3\u003e\n\u003cp\u003eWe first performed the western blot (WB) validation in different cell lines following the steps described above. We chose LINC01615 as our target because this molecule is the only one that have obvious promotion function for cancer progression. We then verified the expression of LINC01615 in five cell lines including HEC-1A, HEC-1B, AN3CA, ishikawa, and RL-952, and find that the expression of LINC01615 is higher in HEC-1B, AN3CA, and ishikawa (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We then knockdown LINC01615 with siRNA in AN3CA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), and conducted transwell experiments to find whether LINC01615 had an influence on the invasion of EC cell lines. The result showed that knockdown of LINC01615 significantly inhibited the invasive ability of EC cell line AN3CA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eEndometrial carcinoma (EC) is one of the most common gynecologic malignancies in the clinic. Although surgery and radiotherapy can relieve symptoms in most patients, the prognosis of advanced endometrial cancer is relatively poor [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, it is urgent to explore new molecule for targeted therapy, which can help to prevent EC more effectively and have important theoretical and practical significance for improving the quality of life of women in China. In this study, we first investigated the correlation among cuproptosis-related lncRNAs and conducted difference analysis to find the key CRLs. Furthermore, the predictive model of EC prognosis based on 7 CRLs was constructed and verified by narrowing the scope through LASSO, and the expression of these lncRNAs in tumor tissues was then found to be different from that in normal tissues and the survival analysis is also different. Studies have confirmed that the prediction model has a high accuracy in predicting the prognosis of EC. After incorporating the 7-CRL risk model into the nomogram, the accuracy of prediction is further improved. The tumor immune microenvironment and potential association with immune targets of high and low risk groups were also investigated. Finally, we verified the expression and function of LINC01615 in vitro.\u003c/p\u003e \u003cp\u003eCuproptosis is a copper-dependent cell death presented by a recent article, as a novel form of Programmed cell death (PCD). Recently, cuproptosis has been considered as a copper-triggered mode of mitochondrial cell death[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Copper can be involved in angiogenesis, tumor growth and metastasis, and cancer cells can adapt to adverse microenvironment by regulating copper metabolism [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. CRLs can be used to predict the prognosis and immune microenvironment of patients suffering from bladder cancer, which high risk patients were enriched in several immune-related pathways such as cell proliferation, nucleotide metabolism, and inflammatory immune response [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The cuproptosis is closely related to immune system [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In cancer cells, copper supplements increase the function and level of PD-L1 at mRNA and protein levels. Copper modulates key signaling pathways that control PD-L1 induced cancer immune escape and promote mediated degradation of PD-L1 [35545174]. It may therefore be possible to change copper metabolism and improve immune function by regulating copper metabolism-related lncRNAs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown that lncRNAs play an important regulatory role in the development and progression of EC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, m6A-related lncRNAs play an important role and could accurately forecast the prognosis of patients with EC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mechanically, lncRNAs play a crucial role in EC progression by multiple patterns such as signaling, decoying, scaffolding, and guidance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cuproptosis-associated lncRNA was a good predictor of prognosis in patients with many types of tumors, such as hepatocellular carcinoma. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], cervical cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and melanoma [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Prognosis of patients with EC had not been studied by constructing predictive features of lncRNA associated with cuproptosis. Therefore, it was essential to identify predictive features of lncRNA associated with cuproptosis in patients with EC. However, the association between copper metabolism and endometrial cancer remains uncertain. Especially, little is currently known about the mechanisms of action of cuproptosis-related lncRNAs in EC. In our study, we found the significant correlation between CRLs and the prognosis, which mechanism might function through immune, metabolism, and enhanced invasive ability. CRL related nomogram can better predict the survival of EC.\u003c/p\u003e \u003cp\u003eSeveral studies have reported that LINC01615 was associated with diverse tumors. In one study, LINC01615 was found potentially affected the extracellular matrix and had further impacts on the metastasis of hepatocellular carcinoma [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In triple-negative breast cancer, it was found that the high expression of SIPA1 can promote the level of LINC01615, and therefore up-regulation of LINC01615 can promote the migration and invasion cancer cells through MMP9 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. LINC01615 can also competitively bind with miR-3653-3p to regulate ZEB2 and promote tumorigenesis of colon cancer cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. LINC01615 is also involved in prognostic network for cancers in predictive models. For example, in a ferroptosis-related-lncRNA model, LINC01615 is revealed to be highly expressed in gastric cancer cell lines and tissues. A nuclear-cytoplasmic fractionation assay confirmed that most LINC01615 was enriched in the cytoplasm in gastric cancer cell lines. Bioinformatics further predicts four potential target miRNAs of LINC01615 and then figured out 26 target ferroptosis-related mRNAs, which could act as a ceRNA to get involved in ferroptosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the current research, LINC01615 is the only lncRNA that is highly expressed in EC tissues, and the overall survival of patients with high expression of LIN01615 is poor. Our \u003cem\u003ein vitro\u003c/em\u003e experiment also confirmed that knock down the expression of LINC01615 can inhibit the invasion of endometrial cancer cells, and the specific molecular mechanism remains to be further clarified.\u003c/p\u003e \u003cp\u003eSince the prognosis of patients in the high-risk and the low-risk cohorts was diverse, enrichment analysis was enforced to study possible differences between the two risk subgroups. GSEA identified that cytoskeleton, metabolism, and cell cycle were might be differentially expressed between the low-risk group and the high-risk group. New evidences are presented for differences in metabolism between healthy and EC cells, which has increased considerably in recent years. Alterations in lipid or sugar metabolism affect important processes such as cell growth, proliferation, and differentiation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The relationship between metastasis and cytoskeleton are reviewed by some studies. TRPV4 and calcium influx acted on the cytoskeleton via the RhoA/ROCK1 pathway, ending with LIMK/cofilin activation, which had an impact on two cytoskeletal levels, F-actin and paxillin (PXN) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Cytoskeleton was also found to be associated with mechanical stimulus-related genes in EC. Xu et al built a mechanical-related signature and it outperformed in predicting OS of EC patients. Further experimental results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, our study has certain limitations. Firstly, publicly available data such as RNA-seq and clinical data, may have certain drawbacks when analyzing the prognostic performance of gene signatures. Moreover, because no other databases have available lncRNA expression and clinical data, the model had to be validated in one database. Finally, the mechanisms of cuproptosis and the functions of CRLs, especially LINC01615 in vitro and in vivo are not clear, which demands for further in-depth studies for EC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, multiple bioinformatics methods were used to identify cuproptosis-related signature and a seven cuproptosis-related lncRNAs signature was revealed. A risk model and a predictive nomogram were built and further selected as prediction to distinguish between high risk and low-risk groups and identify the differences in immune infiltration and TMB values. Furthermore, we proved that LINC01615 could be acted as a potential therapeutic target for EC following in vitro studies. The findings of this study may provide new strategies for exploring the mechanisms of cuproptosis and expand current insights into therapeutic approaches for EC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all donors who participated in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the experiments: Xuecheng Pang; Performed the data collection: Sumin Qian; Analyzed the data: Xuecheng Pang; Contributed reagents/materials/analysis tools: Xuecheng Pang; Contributed to the writing of the manuscript: Xuecheng Pang and Sumin Qian; All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-Approval of the research protocol by an Institutional Reviewer Board.\u003c/p\u003e\n\u003cp\u003eN/A.\u003c/p\u003e\n\u003cp\u003e- Informed Consent.* \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN/A.\u003c/p\u003e\n\u003cp\u003e- Registry and the Registration No. of the study/trial.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;N/A.\u003c/p\u003e\n\u003cp\u003e- Animal Studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN/A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was disclosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online databases. The names of the databases and accession number(s) can be found in the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J Clin. 2022,\u003cem\u003e72\u003c/em\u003e, 7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, X.; Tang, H.; Chen, T. Epidemiology of gynecologic cancers in China. J Gynecol Oncol. 2018,\u003cem\u003e29\u003c/em\u003e, e7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteiner, E.; Eicher, O.; Sagemuller, J.; Schmidt, M.; Pilch, H.; Tanner, B.; Hengstler, J.G.; Hofmann, M.; Knapstein, P.G. Multivariate independent prognostic factors in endometrial carcinoma: a clinicopathologic study in 181 patients: 10 years experience at the Department of Obstetrics and Gynecology of the Mainz University. Int J Gynecol Cancer. 2003,\u003cem\u003e13\u003c/em\u003e, 197\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans, J.R.; Feng, F.Y.; Chinnaiyan, A.M. The bright side of dark matter: lncRNAs in cancer. J Clin Invest. 2016,\u003cem\u003e126\u003c/em\u003e, 2775\u0026ndash;2782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinn, J.J.; Chang, H.Y. Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet. 2016,\u003cem\u003e17\u003c/em\u003e, 47\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z.; Hong, S.; Liu, Z. LncRNA LINC00641 predicts prognosis and inhibits bladder cancer progression through miR-197-3p/KLF10/PTEN/PI3K/AKT cascade. Biochem Biophys Res Commun. 2018,\u003cem\u003e503\u003c/em\u003e, 1825\u0026ndash;1829.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, P.; Cao, H.; Li, Y.; Wang, J.; Cui, Z. Knockdown of lncRNA CCAT2 inhibits endometrial cancer cells growth and metastasis via sponging miR-216b. Cancer Biomark. 2017,\u003cem\u003e21\u003c/em\u003e, 123\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, P.; Xiong, Y.; Yue, J.; Xu, D.; Ihira, K.; Konno, Y.; Kobayashi, N.; Todo, Y.; Watari, H. Long noncoding RNA NEAT1 drives aggressive endometrial cancer progression via miR-361-regulated networks involving STAT3 and tumor microenvironment-related genes. J Exp Clin Cancer Res. 2019,\u003cem\u003e38\u003c/em\u003e, 295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCobine, P.A.; Brady, D.C. Cuproptosis: Cellular and molecular mechanisms underlying copper-induced cell death. Mol Cell. 2022,\u003cem\u003e82\u003c/em\u003e, 1786\u0026ndash;1787.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz, L.M.; Libedinsky, A.; Elorza, A.A. Role of Copper on Mitochondrial Function and Metabolism. Front Mol Biosci. 2021,\u003cem\u003e8\u003c/em\u003e, 711227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDankner, M.; Rose, A.A.N.; Rajkumar, S.; Siegel, P.M.; Watson, I.R. Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations. Oncogene. 2018,\u003cem\u003e37\u003c/em\u003e, 3183\u0026ndash;3199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015,\u003cem\u003e43\u003c/em\u003e, e47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S.; Xie, X.; Lei, H.; Zou, B.; Xie, L. Identification of Key circRNAs/lncRNAs/miRNAs/mRNAs and Pathways in Preeclampsia Using Bioinformatics Analysis. Med Sci Monit. 2019,\u003cem\u003e25\u003c/em\u003e, 1679\u0026ndash;1693.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon, A.; Birger, C.; Thorvaldsdottir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015,\u003cem\u003e1\u003c/em\u003e, 417\u0026ndash;425.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X.C.; Cheng, Y.; Yang, X.; Zhou, J.Y.; Dong, Y.Y.; Shen, B.Q.; Wang, J.Q.; Zhao, L.J.; Wang, Z.Q.; Li, X.P.; et al. Decreased expression of TRPM4 is associated with unfavorable prognosis and aggressive progression of endometrial carcinoma. Am J Transl Res. 2020,\u003cem\u003e12\u003c/em\u003e, 3926\u0026ndash;3939.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussein, Y.R.; Soslow, R.A. Molecular insights into the classification of high-grade endometrial carcinoma. Pathology. 2018,\u003cem\u003e50\u003c/em\u003e, 151\u0026ndash;161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, D.; Chen, X.; Kroemer, G. Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res. 2022,\u003cem\u003e32\u003c/em\u003e, 417\u0026ndash;418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenoyer, D.; Masaldan, S.; La Fontaine, S.; Cater, M.A. Targeting copper in cancer therapy: 'Copper That Cancer'. Metallomics. 2015,\u003cem\u003e7\u003c/em\u003e, 1459\u0026ndash;1476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y.; Li, X.; Li, X.; Zhao, Y.; Zhou, T.; Jiang, X.; Wen, Y.; Meng, W.; Li, S. Comprehensive analysis of cuproptosis-related long noncoding RNA immune infiltration and prediction of prognosis in patients with bladder cancer. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 990326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y.; Huang, X.; Chen, S.; Jiang, H.; Rao, H.; Lu, L.; Wen, F.; Pei, J. In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma. Curr Oncol. 2022,\u003cem\u003e29\u003c/em\u003e, 6573\u0026ndash;6593.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y.; Shu, Q.; Fu, Z.; Wang, C.; Gu, J.; Li, J.; Chen, Y.; Xie, M. A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 959456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, P.; Xiong, Y.; Konno, Y.; Ihira, K.; Kobayashi, N.; Yue, J.; Watari, H. Long non-coding RNA DLEU2 drives EMT and glycolysis in endometrial cancer through HK2 by competitively binding with miR-455 and by modulating the EZH2/miR-181a pathway. J Exp Clin Cancer Res. 2021,\u003cem\u003e40\u003c/em\u003e, 216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan, L.; Lu, Y.; Xiang, C.C.; Zhu, X.; Zuo, E.D.; Cheng, X. Identification of Five m6A-Related lncRNA Genes as Prognostic Markers for Endometrial Cancer Based on TCGA Database. \u003cem\u003eJ Immunol Res\u003c/em\u003e. 2022,\u003cem\u003e2022\u003c/em\u003e, 2547029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H.; Wan, J.; Chu, J. Long non-coding RNAs and endometrial cancer. Biomed Pharmacother. 2019,\u003cem\u003e119\u003c/em\u003e, 109396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L.; Wan, Y.; Zhang, Z.; Jiang, Y.; Gu, Z.; Ma, X.; Nie, S.; Yang, J.; Lang, J.; Cheng, W.; et al. IGF2BP1 overexpression stabilizes PEG10 mRNA in an m6A-dependent manner and promotes endometrial cancer progression. Theranostics. 2021,\u003cem\u003e11\u003c/em\u003e, 1100\u0026ndash;1114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, S.; Liu, P.; Zhao, L.; Han, P.; Liu, J.; Yang, H.; Li, J. A novel cuproptosis-related prognostic lncRNA signature for predicting immune and drug therapy response in hepatocellular carcinoma. Front Immunol. 2022,\u003cem\u003e13\u003c/em\u003e, 954653.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, H.; Mao, F.; Wang, K.; Feng, J.; Cheng, S. Cuproptosis-related lncRNAs predict the clinical outcome and immune characteristics of hepatocellular carcinoma. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 972212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X.; Zhou, L.; Gao, M.; Dong, S.; Hu, Y.; Hu, C. Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 989646.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, X.; Wang, X.; Sun, X.; Xiao, M.; Fan, L.; Su, Y.; Xue, L.; Luo, S.; Hou, S.; Wang, H. Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 972899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, D.; Chen, G.F.; Liu, X.; Zhu, J.; Sun, J.Y.; Zhang, X.Y.; Lu, X.J. Identification of LINC01615 as potential metastasis-related long noncoding RNA in hepatocellular carcinoma. J Cell Physiol. 2019,\u003cem\u003e234\u003c/em\u003e, 12964\u0026ndash;12970.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang, Y.; Feng, L.; Liu, H.; Liu, Y.; Li, J.; Su, L.; Liao, X. SIPA1 Regulates LINC01615 to Promote Metastasis in Triple-Negative Breast Cancer. \u003cem\u003eCancers (Basel)\u003c/em\u003e. 2022,\u003cem\u003e14\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, Z.; Yang, C.; Guo, S.; Li, Y.; Li, Y. LINC01615 activates ZEB2 through competitively binding with miR-3653-3p to promote the carcinogenesis of colon cancer cells. Cell Cycle. 2022,\u003cem\u003e21\u003c/em\u003e, 228\u0026ndash;246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S.; Zheng, N.; Chen, X.; Du, K.; Yang, J.; Shen, L. Establishment and Validation of a Ferroptosis-Related Long Non-Coding RNA Signature for Predicting the Prognosis of Stomach Adenocarcinoma. Front Genet. 2022,\u003cem\u003e13\u003c/em\u003e, 818306.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfsun Antmen, S.; Canacankatan, N.; Gurses, I.; Aytan, H.; Erden Erturk, S. Relevance of lipogenesis and AMPK/Akt/mTOR signaling pathway in endometrial cancer. Eur Rev Med Pharmacol Sci. 2021,\u003cem\u003e25\u003c/em\u003e, 687\u0026ndash;695.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X.; Cheng, Y.; Wang, Z.; Zhou, J.; Jia, Y.; He, X.; Zhao, L.; Dong, Y.; Fan, Y.; Yang, X.; et al. Calcium and TRPV4 promote metastasis by regulating cytoskeleton through the RhoA/ROCK1 pathway in endometrial cancer. Cell Death Dis. 2020,\u003cem\u003e11\u003c/em\u003e, 1009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, X.; Li, X.; Zhou, J.; Wang, J. Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer. Front Oncol. 2021,\u003cem\u003e11\u003c/em\u003e, 753910.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cuproptosis, lncRNA, risk model, LINC01615, ESTIMATE","lastPublishedDoi":"10.21203/rs.3.rs-2255910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2255910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. However, cuproptosis-related lncRNAs (CRLs) have an unclear relationship with endometrial cancer (EC). In this study, a total of 906 CRLs were identified, and 7 specific cuproptosis-related lncRNAs (AL807761.3, AF131215.7, AC008073.2, AC009229.1, CDKN2A.DT, LINC01615, LINC01166) were selected to conduct a risk model. Patients were divided into high- and low-risk groups according to the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, and the predictive accuracy was high (AUC\u0026thinsp;=\u0026thinsp;0.781), indicating the good reliability and specificity of our risk model. According to Gene Set Variation Analysis (GSVA) and GSEA, both metabolism and cytoskeleton have CRL participation. In addition, we found that the CRLs-related scores were associated with the ESTIMATE score. Stratified survival analysis also revealed that the risk signature have has a high prediction accuracy among people with different clinicopathological characteristics. Further in vitro experimental validation indicated that LINC01615 may promote the invasion of EC cells during progression. The efficient risk model based on seven CRLs has a high prognostic accuracy, and LINC01615 may act as a novel biomarker and therapeutic target for EC patients.\u003c/p\u003e","manuscriptTitle":"Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-21 15:35:11","doi":"10.21203/rs.3.rs-2255910/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":"4f2e41aa-672f-4ac2-9fe7-dc3582c4b2a5","owner":[],"postedDate":"November 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17039946,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":17039947,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":17039948,"name":"Biological sciences/Cancer/Cancer therapy"},{"id":17039949,"name":"Biological sciences/Cancer/Oncogenes"},{"id":17039950,"name":"Biological sciences/Cancer/Tumour immunology"}],"tags":[],"updatedAt":"2024-06-06T11:39:48+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-21 15:35:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2255910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2255910","identity":"rs-2255910","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}