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Annals of Translational Medicine logoLink to Annals of Translational Medicine
. 2025 Oct 27;13(5):53. doi: 10.21037/atm-25-87

Construction of a novel ferroptosis-related long non-coding RNAs model for predicting prognosis and tumor immune microenvironment in endometrial cancer

Hikaru Murakami 1,✉, Junlong Wang 1, Herbert Yu 1
PMCID: PMC12592011  PMID: 41211107

Abstract

Background

Endometrial cancer (EC) is the most common gynecological cancer. Ferroptosis is a novel type of programmed cell death that is dependent on iron, and mounting evidence suggests that ferroptosis plays an important role in cancer. Long non-coding RNAs (lncRNAs) are known to regulate ferroptosis; however, little is known about the involvement of ferroptosis-related lncRNAs (FerlncRNAs) in EC. This study aimed to determine a FerlncRNA-based prognostic signature associated with the overall survival (OS) and clinicopathological characteristics of patients with EC.

Methods

Tumor transcriptomes and corresponding clinical data from patients with EC were downloaded from The Cancer Genome Atlas (TCGA) database, and the ferroptosis database, FerrDb, was used to identify ferroptosis-related genes (FRGs) (mRNAs). FerlncRNAs in EC were selected based on their correlations with FRGs. Univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were conducted to construct a prognostic model based on the FerlncRNAs signature. The EC patients were grouped into high- and low-risk categories based on the prognostic model risk score. Kaplan-Meier (K-M) survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the prognostic value of the risk scores. A predictive nomogram was then established. Gene set enrichment analysis (GSEA) was performed to explore the enriched pathways in the two risk groups. Finally, we compared the proportion of infiltrating immune cells and the expression of potential immune checkpoints between the two groups to understand the tumor immunological microenvironment associated with signature FerlncRNAs.

Results

We constructed a FerlncRNAs model to predict the prognosis of patients with EC. K-M analysis demonstrated that patients in the high-risk group had a worse OS. According to the ROC curves, our prognostic model had a better ability to predict the prognosis of patients with EC than other clinical factors. Moreover, the predictive nomogram suggested that our model could offer an independent prognostic evaluation with high accuracy. GSEA identified several enriched pathways in both groups. Finally, the immune microenvironment, including the infiltrating immune cells and immune checkpoints, showed several differences between the two groups.

Conclusions

This study revealed that a prognostic model based on 10 ferroptosis-related lncRNAs is useful for predicting the prognosis of patients with EC. Our findings provide novel directions for prognostic assessments, immunotherapies, and targeted treatments of EC.

Keywords: Endometrial cancer (EC), ferroptosis, long non-coding RNA (lncRNA), immune cell infiltration, prognostic model

Highlight box.

Key findings

• A novel ferroptosis-related long non-coding RNA (lncRNA) prognostic model may be useful for predicting the prognosis and tumor immune microenvironment in endometrial cancer (EC).

What is known and what is new?

• Ferroptosis, an iron-dependent type of programmed cell death, plays an important role in various cancer processes and is thought to be regulated by lncRNAs. Ferroptosis-related lncRNAs have been highlighted for their diagnostic utility and survival prediction.

• We constructed a novel ferroptosis-related lncRNA prognostic model to predict the prognosis and tumor immune microenvironment in EC. These results offer an important foundation for the future study of ten ferroptosis-related lncRNAs potentially involved in EC.

What is the implication, and what should change now?

• This ferroptosis-related lncRNA model can be used to predict the prognosis and tumor immune microenvironment in EC. However, more investigations are needed to confirm the reliability of the risk model.

Introduction

Endometrial cancer (EC) is the most common gynecological cancer and the sixth most common malignancy among women worldwide (1). According to the World Health Organization, 420,368 women were newly diagnosed with EC, and 97,723 cases of EC were fatal, in 2022 [Cancer Tomorrow (International Agency for Research on Cancer). Available online: https://gco.iarc.fr/tomorrow/ (accessed May 1, 2025)]. The incidence of EC has been increased in recent years, and this trend is no exception in younger patients (2,3). Although a large number of patients are diagnosed with EC at an early stage and surgical treatment enables a good prognosis, some patients with early stage EC still develop recurrent or metastatic diseases; the 5-year overall survival (OS) rate the latter group of patients has decreased dramatically (4,5). In recent years, the utility of preoperative magnetic resonance imaging-radiomic analysis has been reported to stratify high-risk EC and tailor surgical and adjuvant therapy (6). Nevertheless, there are limited methods for identifying patients with EC who are at a high risk of recurrence or metastasis, which makes it difficult to apply individualized adjuvant therapies for patients with a high risk of relapse (7). Thus, there is an urgent need to identify biomarkers to improve EC prognosis (8).

Ferroptosis, an iron-dependent programmed cell death mechanism identified in 2012, is triggered by the excessive accumulation of reactive oxygen species (ROS) and lipid peroxidation, which ultimately leads to membrane damage (9). Many genes are involved in the regulation of ferroptosis, including glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11) (10). Several studies have shown that ferroptosis plays an important role in cancer diagnosis, prognosis, carcinogenesis, and treatment (11,12). Recent studies have reported that activating ferroptosis in cancer cells may be a new treatment strategy, especially for drug-resistant cancer cells after conventional therapy (13,14). Moreover, ferroptosis plays a vital role in the immune microenvironment of tumors (15). For instance, cluster of differentiation (CD)8+ T cells activated by immunotherapy can kill cancer cells, wherein CD8+ T cells secrete interferon gamma (IFN-γ) that downregulates the expression of SLC7A11 and solute carrier family 3 member 2 (SLC3A2), resulting in ROS accumulation, lipid peroxidation, and ferroptosis (16). We previously reported that medroxyprogesterone acetate (MPA)-resistant EC cells are susceptible to ferroptosis inducers and suggested that ferroptosis induction via suppression of the SLC7A11/GPX4 pathway may be a new fertility-preserving treatment approach for managing MPA-resistant EC (17). Still, the mechanism of ferroptosis in EC needs to be further explored to make ferroptosis inducers efficacious against EC.

Long non-coding RNAs (lncRNAs) are non-coding RNA greater than 200 nucleotides in length (18). Studies have shown that lncRNAs are involved in multiple biological processes (BPs), including tumor occurrence, progression, and metastasis (19-21). Furthermore, lncRNAs regulate ferroptosis through various mechanisms. We previously reported that the lncRNA P53RRA promotes ferroptosis in lung cancer by increasing the retention of p53 in the nucleus (22). Zhang et al. found that LINC02936 suppresses ferroptosis and promotes tumor progression by interacting with the sine oculis homeobox 1 (SIX1)/ceruloplasmin (CP) axis (23). Moreover, lncRNAs play a vital role in the tumor immune microenvironment. Fan et al. found that KRT19P3 suppresses breast cancer progression via the regulation of local immunity, in which this lncRNA decreases the expression of programmed cell death 1 ligand 1 (PD-L1) and enhances the activity of CD8+ T cells (24). In addition, some reports have indicated that ferroptosis-related lncRNAs can predict the prognosis of certain types of cancer (25,26). However, few studies have systematically evaluated the characteristics of ferroptosis-related lncRNAs and their prognostic value in patients with EC.

In this study, we constructed a ferroptosis-related lncRNA signature to predict the OS of patients with EC based on The Cancer Genome Atlas (TCGA) and ferroptosis databases. We then established a nomogram containing a ferroptosis-related lncRNA signature to gain a deeper understanding of the molecular and signaling pathways involved in ferroptosis in EC. We also explored the roles of immune cell infiltration and immune checkpoints in EC based on the prognosis of patients with EC. Thus, our study provides a novel gene signature for predicting OS to guide the treatment of patients with EC, and offers an important basis for future investigations of ferroptosis-related lncRNAs in EC. We present this article in accordance with the TRIPOD reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-25-87/rc).

Methods

The flowchart of the study approach is illustrated in Figure 1.

Figure 1.

Figure 1

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The flowchart of this study. CPTAC, Clinical Proteomic Tumor Analysis Consortium; DCA, decision curve analysis; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; K-M, Kaplan-Meier; LASSO, least absolute shrinkage and selection operator; lncRNAs, long non-coding RNAs; PCA, principal component analysis; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Data collection

RNA-seq data from tumor samples of 544 patients with EC were downloaded from The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) (https://xenabrowser.net/datapages/). The corresponding clinical data of EC patients, including survival status, age at diagnosis, disease stage, tumor grade, and OS time, were also obtained from TCGA-UCEC. We also downloaded the RNA-seq data of 23 normal endometrial tissue samples from TCGA-UCEC. RNA-seq data included the transcriptomes of mRNAs and lncRNAs. We used org.Hs.eg.db (version 3.18.0, Ensemble version 2023-May10), a human genome annotation library in R software (version 4.3.3, USA), to distinguish mRNAs and lncRNAs and obtain gene symbols from the ensemble IDs. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, which contains transcriptomic data of 213 EC tumor samples with clinical information on survival status, age at diagnosis, disease stage, tumor grade, and OS time, was downloaded from the University of California Santa Cruz (UCSC) Xena (https://xenabrowser.net/datapages/) for external validation. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of ferroptosis-related lncRNAs (FerlncRNAs)

A total of 489 ferroptosis-related genes (FRGs) were identified in FerrDb (https://www.zhouzan.org/ferrdb/), which is the most comprehensive FRG database. To identify FerlncRNAs, we performed a correlation analysis between FRGs and lncRNAs using Pearson correlation coefficient analysis and selected the lncRNAs with a Pearson correlation coefficient |R| >0.35 and P<0.001 for further analysis.

Screening for differentially expressed FRGs (DEFRGs) and differentially expressed FerlncRNAs (DEFerlncRNAs)

We screened for DEFRGs and DEFerlncRNAs between EC samples and normal endometrial tissue samples using the R package “limma” [P<0.05, |log fold change (log FC)| >1].

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for DEFRGs

GO and KEGG pathway analyses of the DEFRGs were performed to evaluate the biological function associated with DEFRGs using the R package “clusterProfiler”. The R package “ggplot2” was applied to visualize the enriched pathways.

Establishment of a prognostic model based on FerlncRNAs signatures

DEFerlncRNAs were subjected to univariate Cox regression analysis to identify lncRNAs associated with the OS of EC patients (P<0.05). Next, least absolute shrinkage and selection operator (LASSO) regression analysis with 10-fold cross-validation was performed to further select useful predictive features using the R package “glmnet”. Multivariate Cox regression analysis was performed to determine regression coefficients in the prognostic model. Finally, we identified ten FerlncRNAs for predicting OS in patients with EC. To visualize the correlation between the signature FerlncRNAs and FRGs, we constructed a map of the co-expression network. The ten FerlncRNAs were used to develop a risk score. According to previous studies (27,28), the risk score for each patient was calculated using the following formula: risk score = β1 (lncRNA1) × expression (lncRNA1) + β2 (lncRNA2) × expression (lncRNA2) + … + β10 (lncRNA10) × expression (lncRNA10). Based on the median risk score, the patients with EC were divided into high- and low-risk groups. A heat map of the expression of signature FerlncRNAs between the two risk groups was constructed using the R package “pheatmap”. The risk curve and survival status scatter plots are shown with the heat map. Kaplan-Meier (K-M) survival analysis and log-rank test were performed to evaluate the OS difference between the two risk groups using the R package “survminer”. Time-dependent receiver operating characteristic (ROC) curves were generated using the R package “survival ROC” to evaluate the accuracy of the prognostic model. In addition, principal component analysis (PCA) was conducted using the R package “stats,” and t-distributed stochastic neighbor embedding (t-SNE) was implemented using the R package “Rtsne” to reveal the dimensionality reduction of signature FerlncRNAs data.

Internal validation of the FerlncRNAs prognostic model

To avoid overfitting and to enhance the predictive accuracy of the prognostic model, we performed K-fold cross-validation as an internal validation before creating a prognostic model based on the full sample (29,30). The total tumor samples were equally divided into five folds so that the OS was roughly equivalent for each fold. The remaining samples, excluding the validation fold, were used as training data, and a risk score model based on the training data was created using the method mentioned above. Subsequently, a multi-index ROC curve analysis was performed to compare the area under the curve (AUC) values of the risk model and other clinical factors. Next, a model based on the training data was validated, and the risk score was calculated. Time-dependent ROC curves were generated and AUC values were evaluated. This process was repeated five times to ensure that all five validation sets were verified. The penalty weight for the coefficient in multivariate Cox analysis was adjusted using an Elastic Net to obtain an AUC >0.55 for all validation folds.

External validation of the signature FerlncRNAs

For external validation of the ten signature FerlncRNAs, we analyzed the data of 213 patients with EC from the CPTAC as an independent dataset. A heat map illustrating the expression of the signature FerlncRNAs was created. Signature FerlncRNAs were subjected to univariate Cox regression analysis to verify their association with OS. We also sorted the 213 patients into two or three groups based on their age at diagnosis, tumor grade, or disease stage and compared the expression of the ten signature FerlncRNAs between the groups. The Mann-Whitney U test was used to compare two groups, and the Kruskal-Wallis test was used to compare three groups. K-M survival analysis and log-rank tests were performed to evaluate the OS between high- and low-expression levels for each of the ten FerlncRNAs.

Construction of the predictive nomogram

We conducted univariate and multivariate Cox regression analyses to assess the independence of FerlncRNAs from traditional clinical characteristics, such as age at diagnosis, tumor grade, and disease stage, in predicting the risk of death from EC. The R packages “limma” and “ggupbr” were applied to draw forest maps. A nomogram was constructed using the R package “rms”. Calibration curves were used to assess the accuracies and benefits of the models. ROC curves were used to evaluate the sensitivity and specificity of the models. Decision curve analysis (DCA) was also conducted to integrate the preferences of patients or decision makers into the analysis to support clinical decision making.

Gene Set Enrichment Analysis (GSEA)

GSEA was conducted with GSEA software (version 4.3.3) to compare biological pathway differences between the high- and low-risk groups. We used the “c2.cp.kegg.v7.4.symbols.gmt” gene set downloaded from the Molecular Signatures Database (http://www.gsea-msigdb.org/).

Analysis of the tumor immune microenvironment

We assessed the relative percentages of infiltrating immune cells in the tumor samples of high- and low-risk groups of patients with EC using the CIBERSORT algorithm, which uses the expression signature of 547 genes to estimate 22 immune cell types in tumor samples (31). We also compared gene expression levels of potential immune checkpoints between the two groups. Potential immune checkpoints were identified from previous literature (32,33). The Mann-Whitney U test was used to compare the ratio of tumor-infiltrating immune cells and the gene expression levels of immune checkpoints between the two groups.

Statistical analysis

R software, version 4.3.3, was used for the statistical analysis in this study. Pearson’s correlation analysis was used to identify the correlations. Comparisons of quantitative variables between the two groups were performed using the Mann-Whitney U test. The Kruskal-Wallis test was used to compare the three groups. K-M survival curves were generated to illustrate survival differences, and the log-rank test was used to compare the survival curves. All P values were two-sided. Statistical significance was set at P<0.05, unless otherwise specified.

Results

Identification of DEFRGs and DEFerlncRNAs

As shown in Figure 1, we analyzed the RNA-seq data of patients with EC and identified 13,860 lncRNAs. We used 489 FRGs downloaded from FerrDB to identify FerlncRNAs. The expression of 8,842 lncRNAs was correlated with the expression of FRGs (Pearson correlation coefficient |R| >0.35, P<0.001). Among them, 87 FerlncRNAs were differentially expressed between EC and normal endometrial tissues. Moreover, we identified 19 DEFRGs between EC and normal endometrial tissues (P<0.05, |log FC| >1).

GO and KEGG enrichment analyses for DEFRGs

The DEFRGs were subjected to GO and KEGG functional enrichment analysis, and the results are presented in Figure 2. BPs were mainly enriched in response to oxidative stress, nutrient levels, and chemical stress. Molecular functions (MFs) were mainly enriched for DNA-binding, transcription factor binding and activator activity, protein serine/threonine kinase activity, ubiquitin protein ligase binding, and ubiquitin-like protein ligase binding. Cellular components (CCs) were mainly enriched in the apical part of the cell, nuclear envelope, apical plasma membrane, mitochondrial matrix and outer membrane (Figure 2A). Based on KEGG pathway analysis, DEFRGs were mainly enriched in pathways relating to phosphoinositide 3-kinase (PI3K)-Akt signaling pathway, autophagy, lipids and atherosclerosis, microRNAs in cancer, mechanistic target of rapamycin (mTOR) signaling, and chemical carcinogenesis-reactive oxygen species (Figure 2B).

Figure 2.

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Functional enrichment analysis of differentially expressed ferroptosis-related genes. (A) GO analysis of differentially expressed ferroptosis-related genes showed the top ten enriched pathways. (B) KEGG analysis of differentially expressed ferroptosis-related genes showed the top thirty enriched pathways. BP, biological process; CC, cell component; COVID-19, coronavirus disease 2019; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; mTOR, mechanistic target of rapamycin; NOD, nucleotide-binding oligomerization domain.

Identification of FerlncRNAs associated with EC survival and construction of a FerlncRNA model for EC prognosis

We performed univariate Cox regression analysis for the 87 DEFerlncRNAs. Among these, 12 DEFerlncRNAs were significantly associated with the OS of patients with EC (ranging from P<0.001 to P=0.006) (Figure 3A). Furthermore, LASSO regression analysis indicated that ten DEFerlncRNAs were more significant (Figure 3B,3C). Finally, the ten FerlncRNAs (AL592494.3, LINC01833, AL023803.2, LINC01224, LHFPL3-AS2, AC084866.1, AL358075.2, AC009005.1, AC026336.3, and AP003306.1) were selected to construct a risk-score model (Figure 3D). The correlation network between the selected FerlncRNAs and FRGs is shown in Figure 4 (Pearson’s correlation coefficient |R| >0.35, P<0.001).

Figure 3.

Figure 3

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Identification of target ferroptosis-related lncRNAs. (A) Forest plot of univariate Cox regression analysis confirmed 12 ferroptosis-related lncRNAs. (B,C) LASSO Cox regression analysis confirmed 10 ferroptosis-related lncRNAs. (D) Forest plot of multivariate Cox regression analysis of the 10 ferroptosis-related lncRNAs. CI, confidence interval; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; lncRNAs, long non-coding RNAs.

Figure 4.

Figure 4

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The correlation network between the target ferroptosis-related lncRNAs and FRGs. FRGs, ferroptosis-related genes; lncRNAs, long non-coding RNAs.

Analysis of EC patients by the FerlncRNA risk score

Patients with EC were categorized into high- or low-risk groups based on their risk score values, using the median risk score as the cutoff. The resulting heat map demonstrated that the expression of five lncRNAs (LHFPL3-AS2, LINC01224, AL023803.2, LINC01833, and AL592494.3) was significantly upregulated in the high-risk group, whereas that of five lncRNAs (AP003306.1, AC026336.3, AC009005.1, AL358075.2, and AC084866.1) was downregulated in the high-risk group compared to the low-risk group (Figure 5A). The high-risk group had a higher mortality rate than the low-risk group (Figure 5B,5C). Moreover, the K-M survival analysis showed that the OS of the high-risk group was significantly shorter than that of the low-risk group (P<0.001) (Figure 5D). Time-dependent ROC curve analysis showed that the AUC values of the FerlncRNA prognostic model were 0.731, 0.719, and 0.717 at 1, 3, and 5 years, respectively (Figure 5E). PCA and t-SNE suggested that the grouping conditions were satisfactory (Figure 5F,5G).

Figure 5.

Figure 5

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Prognostic analysis of the ferroptosis-related lncRNAs model in training cohort. (A) Heat map of expression of the 10 ferroptosis-related lncRNAs. (B) Risk score distribution. (C) Survival status scatter plots. (D) K-M curve of the patients in the high- and low-risk groups. (E) Time-dependent ROC curve analysis. (F) PCA. (G) t-SNE. K-M, Kaplan-Meier; lncRNAs, long non-coding RNAs; PCA, principal component analysis; ROC, receiver operating characteristic; t-SNE, t-distributed stochastic neighbor embedding.

Internal validation of the prognostic model

In the K-fold cross-validation, the risk score model showed the highest AUC values compared to the other clinical factors in all five validations (Figure 6A). The ROC curves showed that the AUC values of the prognostic model were similar among all folds, and the prognostic model was sufficiently useful to predict prognosis in all five validation folds (Figure 6B,6C). Thus, there were no major differences in prediction accuracy among the five folds, indicating that the modeling method was reasonable, versatile, and reliable for all samples.

Figure 6.

Figure 6

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K-fold cross-validation. (A) Multi-index ROC curve analysis compared the AUC values of the risk prognosis model and the clinical index prognosis model in five validations. (B) ROC curve analysis for five validation folds. (C) Time-dependent ROC curve analysis for five validation folds. AUC, area under the ROC curve; ROC, receiver operating characteristic.

External validation of the signature FerlncRNAs in CPTAC

We validated the ten signature FerlncRNAs by analyzing the data of patients with EC from the CPTAC. A heat map of the expression of the ten FerlncRNAs is shown in Figure 7A. The forest plot of the univariate Cox regression analysis of the ten FerlncRNAs indicated that the direction of the hazard ratios (HRs) for all ten FerlncRNAs was consistent between TCGA and CPTAC (Figure 7B). The correlation between each of the ten FerlncRNAs and patients’ clinical features was also measured. All ten FerlncRNAs were significantly associated with patient tumor grade, and the trend of increased and decreased lncRNA expression was consistent with the positive and negative coefficients of the prognostic model, respectively (Figure 7C). The survival curves for each lncRNA are shown in Figure 7D. The trend in survival time according to high and low expression levels of each lncRNA was consistent with the positive and negative coefficients of the prognostic model, respectively. These outcomes were consistent with the findings of TCGA analyses and supported the validity of the variable selection and coefficient derivation for the prognostic model.

Figure 7.

Figure 7

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The validation analyses of the 10 ferroptosis-related lncRNAs in CPTAC. (A) Heat map of expression of the 10 ferroptosis-related lncRNAs. (B) Forest plot of the univariate Cox regression analysis of the 10 ferroptosis-related lncRNAs. (C) Comparison of the expression of the 10 ferroptosis-related lncRNAs in patients grouped by age, grade, and stage. (D) K-M curves of the 10 ferroptosis-related lncRNAs and overall survival. Coef, the coefficient of lncRNA in the prognostic model. CI, confidence interval; CPTAC, Clinical Proteomic Tumor Analysis Consortium; HR, hazard ratio; K-M, Kaplan-Meier; lncRNAs, long non-coding RNAs; TPM, transcripts per million.

Independent prognostic value of the FerlncRNA model and nomogram construction

To determine whether the risk score had an independent effect on the prognosis of patients with EC, the risk score and clinicopathological characteristics, namely age, grade, and stage, were subjected to univariate and multivariate Cox regression analyses. The univariate analysis indicated that age, grade, stage, and risk score were associated with OS (Figure 8A). Additionally, multivariate analysis indicated that age, grade, stage, and risk score could serve as independent predictors of OS (Figure 8B). A nomogram was established to predict the probability of 1-, 3-, and 5-year OS. As shown in Figure 8C, the risk score had an important effect on OS. The calibration curve of the nomogram demonstrated good agreement between the nomogram prediction and the actual probability (Figure 8D). ROC curves showed that the risk score had an AUC of 0.693, which was greater than that of any other clinical factor (Figure 8E). DCA also showed that the risk score had a favorable predictive effect and good clinical application value (Figure 8F).

Figure 8.

Figure 8

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Correlation between risk score and clinicopathological characteristics. (A) Forest plot of the univariate Cox regression analysis. (B) Forest plot of the multivariate Cox regression analysis. (C) A nomogram integrating the risk score and clinicopathological factors. (D) The calibration plot for the prediction of nomogram. (E) Multi-index ROC curve analysis compared the AUC values of the risk prognosis model and the clinical index prognosis models. (F) DCA curves for the risk score and clinicopathological factors. AUC, area under the ROC curve; CI, confidence interval; DCA, decision curve analysis; HR, hazard ratio; ROC, receiver operating characteristic.

Differences of biological pathways between the high- and low-risk groups

The differentially expressed genes (DEGs) between the high- and low-risk groups were subjected to GSEA to explore the enriched pathways in each group. Accordingly, cell cycle, DNA replication, mismatch repair, homologous recombination, one-carbon pool by folate, oocyte meiosis, non-small cell lung cancer, proximal tubule bicarbonate reclamation, and tight junctions were significantly enriched in the high-risk group (Figure 9A). In contrast, alpha-linolenic acid, ether lipid, tyrosine, linoleic acid, complement, and coagulation cascades were significantly enriched in the low-risk group (Figure 9B).

Figure 9.

Figure 9

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GSEA for the high- and low-risk groups. (A) GSEA results for the significantly enriched pathways in the high-risk group. (B) GSEA results for the significantly enriched pathways in the low-risk group. GSEA, Gene Set Enrichment Analysis.

Differences in the tumor immune microenvironment between the high- and low-risk groups

To analyze the connection between FerlncRNAs and antitumor immunity, we used CIBERSORT to estimate the immune cell subtypes in EC tumors. A comparison of the 22 immune cell infiltration levels between the high- and low-risk groups indicated that the percentage of memory B cells, activated dendritic cells, M1 macrophages, and plasma cells were significantly higher in the high-risk group, whereas resting dendritic cells, eosinophils, and regulatory T cells (Tregs) were significantly lower in the high-risk group (Figure 10A). The expression levels of 21 immune-checkpoint genes crucial for immunotherapy were also explored. We found significant differences in the expression levels of CD244, CD40, CD40LG, CD44, CD48, CTLA4, IDO2, TNFRSF8, TNFSF15, and TNFSF4 between the two groups (Figure 10B).

Figure 10.

Figure 10

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Immune microenvironment analysis. (A) Comparison of 22 immune cell infiltration levels between the high- and low-risk groups. (B) Comparison of 21 immune checkpoints expression levels between the high- and low-risk groups. TPM, transcripts per million.

Discussion

Ferroptosis is a form of regulated cell death that depends on iron, and is characterized by iron accumulation, ROS production, and lipid peroxidation. There is a strong association between ferroptosis and many diseases, especially tumors (34). Several studies have demonstrated that ferroptosis is involved in the initiation, progression, and metastasis of various types of tumors, including EC (35). Recent studies have shown that lncRNAs regulate ferroptosis in tumor cells through multiple mechanisms and may influence or ameliorate tumor resistance to chemotherapeutic agents (36). Thus, there is no doubt that lncRNAs have an important role in ferroptosis. However, few studies have investigated the ferroptosis-based lncRNAs involved in EC.

In this study, 87 DEFerlncRNAs and 19 DEFRGs were identified between EC tumors and normal endometrial tissues by analyzing the transcriptome of TCGA-UCEC. Functional analysis of DEFRGs using GO and KEGG showed that these genes were mainly enriched in cancer- and immune-associated pathways, such as those related to the oxidative stress response, PI3K-Akt signaling, autophagy, human papillomavirus infection (37), human immunodeficiency virus 1 infection, microRNAs in cancer, mTOR signaling, hepatitis C (38), human T-cell leukemia virus 1 infection, proteoglycans in cancer, hepatocellular carcinoma, central carbon metabolism in cancer, and hypoxia-inducible factor 1 (HIF-1) signaling (39). Moreover, these genes were implicated in ferroptosis-associated pathways such as the response to oxidative, chemical stress, and metal ions, protein serine/threonine kinase activity, mitochondrial outer membrane, the PI3K-Akt signaling pathway, and chemical carcinogenesis-reactive oxygen species (8). The disruption of glutathione-dependent lipid peroxide repair systems causes ferroptosis through the accumulation of lipid-based ROS, accompanied by intracellular iron accumulation and disruption of the mitochondrial bilayer (17). Collectively, our results strongly suggest that these genes may affect tumor initiation and progression in a ferroptotic manner and affect the immune system.

We identified 12 FerlncRNAs associated with OS in patients with EC. Subsequently, we constructed a model with ten FerlncRNAs to predict the prognosis of patients with EC and compared it with traditional clinical factors. Additionally, we established a nomogram to predict the prognosis of patients with EC and support clinical decision-making. Our prognostic model had a better capability than the clinical factors in predicting the prognosis of patients with EC. We then divided the patients into high- and low-risk groups based on the risk score of the prognostic model and compared the prognosis and enriched biological pathways between the two groups. Our results indicated that patients with EC in the high-risk group had worse OS, and an enrichment of the cell cycle, DNA replication, mismatch repair, homologous recombination, one-carbon pool by folate, oocyte meiosis, non-small cell lung cancer, proximal tubule bicarbonate reclamation, and tight junction pathways. The GSEA results also suggested that the ferroptosis-related lncRNA prognostic model mainly involved cancer-related pathways including the cell cycle, DNA replication, mismatch repair, and homologous recombination. Our findings indicate that these biological pathways may play important roles in the tumorigenesis of EC. Internal validation was performed to validate the proposed prognostic model. In addition, we conducted external validation to evaluate the ten signature FerlncRNAs and found that these FerlncRNAs were similarly associated with EC prognosis. Our analysis showed that this FerlncRNA prognostic model offers an independent prognostic evaluation with high accuracy for EC.

Finally, we compared the percentages of different immune cells and the gene expression of potential immune checkpoints between patients with different FerlncRNA risk scores to understand the tumor immune microenvironment in relation to signature FerlncRNAs. In the high-risk group, memory B cells, activated dendritic cells, M1 macrophages, and plasma cells were significantly upregulated, and Tregs were significantly downregulated. B cells are pluripotent stem cells derived from the bone marrow that can differentiate into plasma cells when stimulated by antigens. Some studies have shown that the presence of B cells in the tumor microenvironment can promote cancer progression and worsen the prognosis of several cancers, such as bladder, kidney, and prostate cancers (40-43). Moreover, Mohammed et al. reported that patients with tumors comprised of more plasma cells and increased CD138+ B cell infiltration had shorter recurrence-free survival (44). These reports support the findings of this study. Dendritic cells are instrumental in the generation of specific T cell-mediated antitumor effector responses that control tumor growth and tumor cell dissemination (45). M1 macrophages are classically activated macrophages that promote antitumor immunity (46); these macrophages can kill a wide range of tumor cells (47). Additionally, the depletion of Tregs or the inhibition of Treg function enhances antitumor effects (48). In this study, dendritic cells and M1 macrophages were upregulated and Tregs were downregulated in the high-risk group, which is inconsistent with previous reports. Thus, our results indicated some differences in the immune microenvironment between the high- and low-risk groups, which may partly explain the significant difference in prognosis between the two groups. Immune checkpoints that were significantly differentially expressed between the two groups may be useful in guiding immunotherapy for patients with EC. However, the relationship between ferroptosis and the immune system in EC remains unknown and requires further exploration.

The ten FerlncRNAs used to establish the prognostic model in this study were AL592494.3, LINC01833, AL023803.2, LINC01224, LHFPL3-AS2, AC084866.1, AL358075.2, AC009005.1, AC026336.3, and AP003306.1. Sun et al. reported that AL592494.3 worsened the prognosis of EC; however, they did not report any association with ferroptosis (49). LINC01833 is involved in the inhibition of ferroptosis in bladder cancer by acting as a competing endogenous RNA and soaking miR-129-5p. This increases prominin-2 (PROM2) expression and promotes iron export through ferritin-containing exosomes (50). LINC01833 also affects the prognosis of lung cancer, bladder cancer, cervical cancer, and EC (51-54). Among the lncRNAs identified in this study, LINC01224 was the most frequently reported. Zuo et al. found that LINC01224 promotes cell proliferation and inhibits apoptosis by regulating AKT serine/threonine kinase 3 (AKT3) expression via targeting miR-485-5p in EC (55). Sang et al. reported that LINC01224 promotes malignant behavior in breast cancer by regulating the miR-193a-5p/nucleoporin-210 (NUP210) axis (56). Moreover, Zhang et al. reported that LINC01224 is a ferroptosis-related lncRNA that affects hepatocellular cancer prognosis (57). The lncRNA LHFPL3-AS2 is a tumor suppressor in lung cancer (58). However, there are no reports on the role of LHFPL3-AS2 in EC. Although described only in hepatocellular cancer, AC009005.1, as an autophagy-related lncRNA that influences the immune microenvironment, has been reported to affect prognosis (59,60). There is only one report on AC026336.3, in which AC026336.3 was identified as an lncRNA with diagnostic and prognostic value in EC (61). There have been no reports of other lncRNAs in our model. Thus, reports on ferroptosis-related lncRNAs in EC are limited, and further investigation is required.

In 2020, ECs were re-classified and divided into the following four classes with individual recurrence risk and progression-free survival: DNA polymerase epsilon (POLE), microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR), copy-number-low/TP53-wild-type (CNL), copy-number-high/TP53-mutant (CNH/p53abn). Patients with high-stage EC may benefit from tailored postoperative therapies based on molecular analysis, especially for cases of advanced MSI-H/dMMR. However, there is no strong evidence supporting the use of molecular profiling for the choice of postsurgical management in patients with early stage EC (62). The choice of treatment, especially for patients of childbearing age with early stage EC, should be made carefully as fertility-sparing treatments must be considered (63). Fertility-sparing treatments include pre-chemotherapy egg and embryo freezing, which may affect not only the risk of recurrence in patients with EC but also the neonatal outcomes and long-term prognosis of future children (64,65). Studies have reported that assisted reproductive techniques may affect the neuropsychomotor area of newborns and increase the risk of congenital heart disease (66,67). Given these risks, the decision to initiate fertility preservation treatment should be made with caution, and more accurate prognostic tools are needed to help patients with EC make this decision (64,68). Under these circumstances, L1 cell adhesion molecule (L1CAM) has recently gained attention as a promising potential prognostic factor for early stage EC (69). A systematic review reported that L1CAM plays a prognostic role in stage I EC, providing a potentially useful tool for tailoring adjuvant therapies (70). In this study, the prognostic model had a better ability to predict the prognosis of patients with EC than other clinical factors, suggesting that the model may be useful for determining management strategies for patients with early stage EC. However, further investigations are required to select the appropriate treatment for patients with early stage EC.

This study had some limitations. First, only data obtained from TCGA were used to construct the prognostic model, which may limit the generalizability of our findings. Second, public databases with transcriptomic data on non-coding RNAs are limited. We found only one independent dataset, CPTAC, for external validation; however, this dataset had an insufficient number of cases, few deaths, and a short follow-up period, which limited our ability to validate the risk model. Prospective multicenter clinical studies with larger patient populations are required to validate our results and eliminate selection bias. Third, since the analyses of biological mechanisms in our study were based on bioinformatics predictions, in vitro and in vivo experiments are needed to further elucidate the biological functions and regulation of FerlncRNAs in EC.

Conclusions

We established a novel model using ten ferroptosis-related lncRNAs (AL592494.3, LINC01833, AL023803.2, LINC01224, LHFPL3-AS2, AC084866.1, AL358075.2, AC009005.1, AC026336.3, and AP003306.1) to predict the prognosis and immune response of patients with EC. Our results demonstrate that the prognostic model could offer an independent prognostic evaluation with high accuracy for EC. This study may contribute to the optimization of risk stratification for survival outcomes and personalized management of patients with EC.

Supplementary

The article’s supplementary files as

atm-13-05-53-rc.pdf (88.3KB, pdf)
DOI: 10.21037/atm-25-87
atm-13-05-53-coif.pdf (149.5KB, pdf)
DOI: 10.21037/atm-25-87

Acknowledgments

None

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Footnotes

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-25-87/rc

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-25-87/coif). The authors have no conflicts of interest to declare.

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    atm-13-05-53-rc.pdf (88.3KB, pdf)
    DOI: 10.21037/atm-25-87
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    DOI: 10.21037/atm-25-87

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