Introduction

UCEC occurs as the second common female reproductive system tumors globally. According to the GLOBOCAN database, 417,367 new UCEC cases and 97,370 deaths have occurred worldwide by 2020 (Bray et al. 2018). Postmenopausal women constitute the predominant demographic affected by endometrial cancer. Presently, the incidence of this condition exhibits a yearly rise, which can be attributed to the aging population and the escalating prevalence of obesity among women (Gu et al. 2021). It is thought that as many as 25% of cases were also found before menopause, and even if UCEC patients with early stage accepted surgical therapy got good prognosis, however, for patients with advanced stages or recurrent endometrial cancer, 5-year survival rates were only approximately 17% (Siegel et al. 2021).A lower five-year survival rate is indicated that these present approaches to risk and prognosis prediction based on the clinicopathologic characteristics of patients may be insufficient. As a result, it is of the utmost importance to locate new biomarkers to construct a risk prediction model for UCEC.

Copper is an important trace element because it participates in many metabolic reactions that take place within the human body. However,copper has dual effects on metabolic pathways in all species,excess intracellular copper is poisonous and can kill cells. Cuproptosis, which is thought to be a copper-triggered mechanism of mitochondrial cell death, has recently come into focus.Related studies suggest that compared to healthy populations, cancer patients had greater serum and tumor tissue copper levels (Blockhuys et al. 2017; Ge et al. 2022; Ishida et al. 2013). Intracellular copper levels could impact on the initiation and progression of cancer, and copper overload could cause cytotoxicity (Tchounwou et al. 2008).Copper dysregulation plays a role in the beginning and development of diseases like cancer. Several types of cancer have been found to have increased levels of copper in malignant tissues, including breast, lung, stomach, ovarian, cervical, and leukemia (Denoyer et al. 2015; Saleh et al. 2020).Cuproptosis is a mechanism of neuronal death that differs from previous recognized procedures (such as pyroptosis, apoptosis, and iron death). Cell proliferation, angiogenesis, and metastasis, the three main aspects of cancer progression are all influenced by copper (Hanahan and Weinberg 2011).Furthermore, it has been demonstrated that copper is capable of adhering directly to the lipoacylated elements of the TCA cycle, eventually leading to toxic protein dilatation and death of cells (Shimada et al. 2018; Tsvetkov et al. 2022). Consequently, it is thought that one new therapeutic strategy for killing cancer cells is to increase the buildup of intracellular cancer (Ge et al. 2022).There is convincing evidence linking copper levels to endometrial cancer (Chen 2022). The mechanism states that in UCEC patients, identifying the regulators of the unique type of cell death is essential.

Long non-coding RNAs (lncRNAs) is a term used to describe non-coding RNAs that exceed 200 nucleotides in length (** techniques. The results indicated that six CRLs features, rather than the entire CRLs, cuproptosis-associated mRNAs, or the whole genome could more accurately identify between patients at low and high risk (Fig. 6A–D). To gain insight into the discrepancies in biochemical functions and signaling molecules between the different risk organizations, we identified differential genes with log2|FC |> 1 and FDR < 0.05 for the sake of GO and KEGG enrichment analysis and visualized the leading 15 outcomes.In GO analysis, the main biological process (Supplementary Table 3) in differential gene enrichment is microtubule − based movement, cilium organization, cilium assembly andcilium movement. The molecular function enrichment was mainly focused on the tubulin binding, microtubule motor activity and cytoskeletal motor activity, while the cellular components were mainly plasma membrane bounded cell projection cytoplasm, motile cilium,and cytoplasmic region (Fig. 6E).Furthermore, depending on KEGG analysis, the molecular pathways of neurodegeneration-multiple diseases, muscular atrophy-lateral sclerosis, and neuroactive ligand receptor interaction were considerably enriched (Fig. 6F).

Fig. 6
figure 6

PCA analysis and enrichment analysis. A–D PCA plots for all risk genes, risk score, CRLs, and cuproptosis-related coding genes. E The circle graph demonstrated that the top GO signaling pathways involved biological processes in BP, MF, and CC. F KEGG analysis of differential genes in the prognostic model’s high- and low-risk groups (circle plot)

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We subsequently analyzed the different risk score groups in biological function and pathways by GSEA analysis to further elucidate the variations in biological function between different risk groups.Findings indicated that the calcium signaling pathway, ECM-receptor interaction pathway,neuroactive ligand receptor interaction, heart muscle contraction and dilated cardiomyopathy were more prevalent in the high-risk subgroup (Fig. 7A). Parkinson’s disease, glycerolipid metabolism,oxidative phosphorylation, olfactory transduction, and the ribosome pathway were more prevalent in the low-risk subgroup(Fig. 7B).

Fig. 7
figure 7

Gene set enrichment analysis (GSEA) for different risk subset. A GSEA of the notably enriched pathways in high-risk subset. B GSEA of significantly enriched pathways in low-risk subset

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Different Risk Groups Have Distinct Immune Profiles

We quantified the enrichment scores of the 23 immune cell subsets and their associated immune functions or immune pathways using ssGSEA aim to better explore the differences in immunological status among the various risk score groups. The findings demonstrated that many immune cell types showed substantial differences with low and high risk scores (P < 0.05, Fig. 8A), including activated CD8+ T cells, eosinophils, immature dendritic cells, MDSC, and monocytes. Additionally, the low-risk score group was more prevalent in Type II IFN Response, Cytolytic activity, T cell co-stimulation, and HLA (P < 0.05, Fig. 8B). To further assess the immune infiltration differences between the high and low risk groups,we validated our data correlated with immune cells by using the “TIMER” (http://timer.cistrome.org) analysis tool.Furthermore, we also used the CIBERSORT algorithm to predict immune cell infiltration in tumor samples(Thorsson et al. 2018), with a high overlap between the results of the different algorithms (Fig. S8A, B).For example, those for ssGSEA,CIBERSORT and TIMER multiple immune infiltrations indicated a link between CD8 + T cell enrichment and low risk scores. Previous studies have also showed that the CD8 + T cells are crucial for protective immunity against intracellular infections and malignancies (Kurachi 2019), indicating that knowing the molecular mechanism of T cell depletion is essential for develo** effective immunotherapeutic approaches.However, due to the diversity of algorithms and the difference of immune cell types, the results of each algorithm also are slightly difference.

Fig. 8
figure 8

Immune cell infiltration features in the risk subgroups. A ssGSEA for the relationship between TIICs and related functions in various risk groups. B Heatmap using the ssGSEA approach to show the variations in immune-related functions between the two risk groups. *p < 0.05, **p < 0.01, and ***p < 0.001

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It is generally recognized that TIICs can influence the immunological microenvironment, which in turn can influence tumor formation. To further comprehend the connection between immune cell invasion and lncRNAs associated to cuproptosis.We created a heatmap to illustrate the connection between immune cells and lncRNAs relevant to cuprotosis (Fig. 9A), then demonstrate how the relationship between the risk index and immune infiltrating cells is statistically significant.Co-expression pattern among immune cells and the risk score based on cuproptosis-related lncRNAs prognosis characteristics were significantly correlated with the infiltration levels of resting DCs (R = − 0.26 P = 8.6e-05), activated DCs (R = 0.3, P = 5.3e-06), macrophages M1 (R = 0.15, P = 0.024), and CD8+T cells (R =− 0.15, P = 0.028), regulatory T cells (treg) (R = − 0.15, P = 0.024) (Fig. 9B-F). In conclusion, the immune cell invasion is linked to the cuproptosis-related lncRNAs of UCEC.

Fig. 9
figure 9

The relationships between the risk scores calculated by the 6-lncRNA signature and immune infiltration cells were evaluated. A A corrplot was employed to demonstrate the association between the 19 immune cells, risk ratings, and the six lncRNAs. B–F Risk assessments and the relationship between various tumor immune cells *p < 0.05, **p < 0.01, and ***p < 0.001

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Alteration Landscapes Among High- and Low-Risk Score Categories

To learn more about the variations in tumor mutation status among the various risk categories, we assessed the tumor mutation burden within these groups. A significant variation in TMB status between the two groups was shown by the TMB analysis (Fig. 10A, B).When compared to the high-risk subset, the low-risk subset exhibited a larger percentage of mutations (Fig. 9G). We separated the UCEC samples into subsets with different mutations by the median TMB score. Patients with low mutation rates significantly outlived those with high mutation rates, the level of TBM load and patients’ overall survival rates were found to be correlated by a Kaplan–Meier analysis (Fig. 9E, F). In the examination of tumor mutations, the low-risk group’s tumor mutation rate reached 99.27%, which was higher than 96.85% in the high-risk group. Among those at low risk score group, the PTEN gene mutation was the most common (81%); Among those at high risk, the TP 53 gene mutation was more common (48%). In addition, missense mutations and single-nucleotide polymorphism mutations were most widespread in the two risk groups (C > T), followed by (C > A). (Fig. 10C, D) illustrates somatic mutation details. The MSI status of the tumor might be categorized into three categories: high (MSI-H), low (MSI-L), and stable (MSS). Our data demonstrate lower risk scores in individuals with MSI-H (high microsatellite instability), compared with two other low microsatellite instability phenotypes, such as MSI-L and MSS, in solid tumors, including endometrial cancer, CRC, and gastric cancer (Fig. 9H, I). These findings imply that endometrial cancer patients’ risk scores due to the six cuproptosis-related lncRNAs could present genomic stability.Considering the findings above, we concluded that risk score model has the ability to predict immunotherapy response.

Fig. 10
figure 10

The mutational patterns and MSI of different populations at risk. A, B Somatic mutations between groups of people with different risk score. C, D The somatic mutation profiles of the high-risk group and low-risk group in UCEC patients. E H-TMB and L-TMB patient subsets’ Kaplan–Meier curves for OS. F The OS Kaplan–Meier curve for TMB plus risk. G The bar plot is used to show the relative frequency of the various risk groups among the low and high mutation groups. H TMB discrepancy between high-risk and low-risk individuals on a violin plot. I Boxplot displaying the risk scores for the MSI-H, MSI-L,and MSS

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Sensitivity Testing for Drugs

Given the importance of chemotherapeutic drugs for UCEC, we further performed a sensitivity analysis of chemotherapeutic drugs in different groups and compared the IC50 values of sensitive drugs in patients with the two subtypes. Our findings indicated that low-risk score group had greater MG-132, MS-275 (Entinostat), and Bortezomib IC50 values, while the IC50 values of (5-FU) and PHA-665752 were higher in high-risk score group, which further demonstrated that the statistical significance of differences in commonly used chemotherapy medications divided into risk score categories, which is beneficial to provide a reference for screening sensitive chemotherapeutic drugs in people with diverse risk scores (Fig. 11A, B).

Fig. 11
figure 11

Drug sensitivity testing. A The IC50 of small molecule drugs in two risk populations, drug left to right Bortezomib, MG-132, MS-275 (Entinostat), PHA-665752,5-FU. B Correlation plots Bortezomib, MG-132, MS-275 (Entinostat), PHA-665752,5-FU.IC50, half of the maximum inhibitory concentration

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Verification of lncRNA Expression Associated with Cuproptosis

To further validate the results of the bioinformatics analysis described above, qRT-PCR tests were conducted on various endometrial cancer cell lines. Our findings revealed that the expression of AC079466.2, AC090617.5, AC026202.2, and NRAV was markedly increased in the three common endometrial cancer cell lines (HEC-1A, KLE,and Ishikawa cell lines) compared to the human normal endometrial epithelial (HEEC) and endometrial stromal cell line (HESC) (Fig. 12a–d).In addition, our experimental results further showed that the expression level of LINC01545 and AL450384.1 was up-regulated in non-endometrial cancer cell lines, and between the HEEC and HESC cell lines, there was no statistically significant change in the expression of these two genes (Fig. 12e, f). The sequencing process of the target lncRNA was performed at the multicellular line level, so we have every reason to believe in the reliability of the results of this experiment. Overall, the results of this experiment matched our bioinformatics analysis, and further verified validated the accuracy of our risk measurement based on the six lncRNA associated with cuproptosis. For additional assurance that the target gene is expressed at the same level across most cell lines,we further used the cell line database CCLE (Since The Cancer Cell Line Encyclopedia), the results showed that six lncRNAs were expressed in 28 cervical cancer cell lines and the expression trend was similar to the biocredit analysis (Fig. S12A, B),but there were also differences in expression between the different cell lines.We examined possible causes. Tissues consist of various cells, and lncRNA expression varies among cell lines. The expression in tissues follows the same pattern as in all cells. The cell line we chose may have different lncRNA expression. But what is more noteworthy, we found that only included endometrial cancer cell lines and distant metastatic cell lines lacked non-tumor lines in the CCLE database,it is therefore impossible to contrast the differences between the tumor and non-tumor cell lines.

Fig. 12
figure 12

Verification of the expression level of the six CRLs in UCEC cell lines. a–f Relative expression of six CRLs in tumor cell lines (HEC-1A, KLE, Ishikawa) and normal cell lines (HEEC, HESC).The results of this study were all compared with the expression levels of the HEC-1A cell lines. *p < 0.05, **p < 0.01, and ***p < 0.001

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Discussion

UCEC is best commonly diagnosed cancer of the female reproductive system, with the aging population in recent years and the decreasing fertility rate (Tsilidis et al. 2015; Wang et al. 2022a, b), the increasing incidence of endometrial cancer. The research found that the distant metastasis of endometrial cancer patients 5-year survival rate is only 16% (Chen et al. 2021). Although treatment may continue, due to tumor heterogeneity, even if UCEC patients have similar clinical characteristics and treatment, their prognoses can vary greatly. This shows that clinical stage and pathological classification alone are not enough to determine a patient’s prognosis. Early identification of endometrial cancer is difficult without unambiguous signs. Create accurate prognostic indicators to revive the UCEC prognostic forecast, stratify patients by risk profile, and predict drug susceptibility to give patients customized treatment options is essential.

The acknowledgment of RNA regulation as a pivotal aspect in the process of gene expression and the emergence of phenotypic intricacy originated from novel techniques and biological understandings that were established during the period of the 1970s–1980s (Sharp 2009).Extensive investigations into the transcriptome of mammals have revealed that the quantity and diversity of long non-coding RNAs (lncRNAs) surpass those of messenger RNAs (mRNAs) that encode proteins. Views on eukaryotic gene expression have changed recently due to the discovery that RNA can produce a great deal of variation (Licatalosi and Darnell 2010).When whole genome sequencing became possible, it was discovered that 99.7% of the coding regions in the human and chimpanzee genomes are the same (Calarco et al. 2022). Several lines of evidence indicate that lncRNA NRAV is involved in the immunological reaction against viruses and is a negative regulator of the antiviral response (Li et al. 2020; Zhang et al. 2018). According to research by Wang et al.,NRAV influences the Wnt/β-catenin signaling pathway to cells of hepatocellular carcinoma proliferation and invasion (Peng et al. 2018).Moreover, it has been shown thatNRAV is also a biomarker for clinical prognosis in HCC and low-grade gliomas (Feng et al. 2021; Maimaiti et al. 2021; Xu et al. 2021) LINC01545 is a long interstromal non-coding RNA that has been studied as a biomarker for predicting the development of diffuse large B-cell lymphoma (DLBCL) (Qin et al. 2012).Moreover, recent studies have identified AC090617.5 as a predictive indicator for lung adenocarcinoma and non-small cell lung cancer (Yao et al. 2023), the conclusions concur with our findings, in which AC090617.5 also plays a protective role.So far, the functions and detailed molecular interaction mechanisms of the remaining three lncRNAs have not been further explained.To completely understand the roles performed through these lncRNAs, further research is therefore required.

We adopted GO and KEGG enriched analysis investigated the differences in gene expression between the low and high categories to better understand the molecular mechanisms underlying endometrial cancer-related genes. The findings indicate that the molecular function enrichment of DEGs is primarily concentrated on extracellular structural organization and external encapsulated structural organization.The main manifestations of molecular function enrichment are signaling receptor activator, receptor ligand activity, and sulfur compound binding, the main cellular component is the type I collagen containing extracellular matrix KEGG analysis showed that DEGs in neurodegeneration-a variety of disease pathways, muscle atrophy-lateral sclerosis, neuroactive ligand receptor interaction and other molecular pathways. Tumor cells, stromal cells, and extracellular matrix make up the majority of the TME (ECM). Tumor cells are involved in tumor progression, which is strongly related to other TME components (Pitt et al. 2016), especially the immune cells. And tumor patients for immunotherapy responsiveness, can be evaluated by TMB, TMB reflects the variation of tumor cell genome, high TMB (TMB-H) tumor patients have the potential to get more new antigen, and is related to tumor heterogeneity, theoretically high TMB can enhance tumor immunogenicity and reaction with ICI. Numerous studies have established that a high TMB is linked to a significant benefit from immunotherapy. According to our results, TMB was more prevalent in low-risk score group, and the survival study using TMB data and risk stratification demonstrated that people with high mutation load and low risk score had the best prognosis. When DNA mismatch repair (mismatch repair, MMR) function abnormal, microsatellite replication errors are not corrected and accumulated, makes the microsatellite sequence length or base composition changes, called microsatellite instability (microsatellite instability, MSI), at the same time can lead to genome present high mutation phenotype, and the study found that MSI is a predictor of immunotherapy efficacy in advanced solid tumors. The proportion of microsatellite height instability (MSI-high, MSI-H) was significantly higher in the low-risk category (41% VS 23%) than in the high-risk category, however according our research results. This research backs up our hypothesis that people with the disease with low risk will respond positively to immunotherapy and benefit more from it.

Additionally, we performed a susceptibility analysis on individuals with various risk scores (Zhang et al. 2022), and in the end, we discovered a connection between five chemotherapy medications and the risk score, 5-fluorouracil (5-FU) and PHA-665752 were connected with the lower risk score.Compared to the lower risk category, the greater risk category had higher IC50 values, and the other three drugs’ IC50-value, MG-132, MS-275 (Entinostat), Bortezomib was linked to a lower risk score, and in the low-risk group, the IC50 value was lower than it was in the high-risk group.5-FU is the first antimetabolizing drug synthesized according to certain assumptions, and it is the most widely used anti-pyrimidine drug clinically. It has good efficacy on digestive tract cancer and other solid tumors, and is crucial in the management of medical oncology. A new target for many solid cancers is the c-Met receptor tyrosine kinase (including lung cancer). PHA665752 in mouse xenografts from small cell (NCI-H441 and A549) and non-small cell (NCI-H441 and A549) lung cancer cell lines(NCI-H69), angiogenic conversion was caused by in of c-Met phosphorylation and angiogenesis at the c-Cbl binding site, due to this, thrombsinin-1 synthesis increased whereas vascular endothelial growth factor production dropped (Crosswell et al. 2009).These investigations indicate the effectiveness of competitive small-molecule ATP inhibitors in selective c-Met targeting, and they propose that PHA665752 could offer a potential tumor therapeutic approach. Bortezomib and MG-132 both belong to the proteasome inhibitor class of drugs, which are currently used in the treatment of various tumor diseases, and have achieved outstanding clinical effects. According to numerous studies, bortezomib inhibits the expansion of tumor cells by influencing the proteins associated to apoptosis, such as nuclear transcription factors, intra-cell apoptosis signals, and cell-cycle-related proteins. While Entinostat is a well-tolerated HDAC inhibitor and has shown its therapeutic potential in both solid and hematological tumors. Despite the fact that the effect of some medicines in UCEC has not been studied, our findings might offer fresh suggestions for their treatment.

There have been several previous reports on the establishment of cuproptosis-related lncRNA models for predicting the prognosis of UCEC (Hu et al., 2023; Qi et al. 2023). Compared to a study by Qi et al., our model incorporated new lncRNAs (LINC01545, NRAV, AL450384.1, AC079466.2 and AC090617.5), and we further explored differential expression of lncRNAs in the model as well as CCLE database. Overall, the actual survival and predicted survival of the nomogram in our signature showed good consistency, as indicated by calibration curves. When evaluating survival predictions, AUCs at 1, 3 and 5 year were 0.778, 0.810 and 0.854 for the training group, respectively, which were significantly higher than those of previous studies.Our signature was also valuable for predicting PFS in UCEC patients. In summary, our prognostic model has good and stable prognostic prediction ability.

Finally, the identification of regulatory linkages between mRNA, lncRNA, and miRNA in UCEC holds potential for advancing our understanding of the molecular mechanisms involved in UCEC formation. This knowledge can contribute to the improvement of diagnostic and therapeutic approaches for UCEC, as well as the creation of tailored pharmaceutical interventions (Zhou et al. 2023). The first step in exploring ceRNA is finding miRNA that may bind lncRNA.Therefore, we predicted the downstream target genes of CRLs and visualized the results using the Cytoscape program (Fig.S?).The results showed that AL450384.1 and AC079466.2 may be regulatory hubs, AL450384.1 may exert a biological function by regulating downstream microRNA-181.Previous studies have shown that the miR-181 family is dysregulated in multiple tumor tissues,and plays a key role in carcinogenesis, Rezaei et al. believe that microRNA-181 plays a dual role in the development of human cancer (Rezaei et al. 2020).In addition,our data also reveal a substantial correlation of CRLs with miR-3140, Feng, et al. found Hypoxia-induced circCCDC66 promotes the tumorigenesis of colorectal cancer via the miR-3140/autophagy pathway (Feng et al. 2020) which may open up new UCEC research avenues.However, the impact of these pathways on tumor formation requires further validation through foundational tests.

Furthermore,some limitations of our study remain. For example, more clinical groups may be needed to cross-verify the prognostic accuracy and predictability of the characteristic. Furthermore, additional research is required to understand the mechanisms underlying the association as between cuproptosis-related lncRNAs and the prognosis of the UCEC.

Conclusions

In conclusion,our research establishes a novel predictive model with six genes and speculate that cuproptosis may play a role in the development of UCEC. And we find that CRLs are significantly associated with abnormal immune infiltration expression and may be important genes for immune regulation of UCEC. Moreover, CRLs gene has a high predictive value for the prognostic risk of UCEC patients and can provide a reference for guiding immunotherapy in UCEC patients.