Integrated Identification and Immunotherapy Response Analysis of the Prognostic Signature Associated With m6A, Cuproptosis-Related, Ferroptosis-Related lncRNA in Endometrial Cancer

Abstract

Background: Endometrial cancer (EC) stands as the predominant gynecological malignancy impacting the female reproductive system on a global scale. N6-methyladenosine, cuproptosis- and ferroptosis-related biomarker is beneficial to the prognostic of tumor patients. Nevertheless, the correlation between m6A-modified lncRNAs and ferroptosis, copper-induced apoptosis in the initiation and progression of EC remains unexplored in existing literature.

Aims: In this study, based on bioinformatics approach, we identified lncRNAs co-expressing with cuproptosis-, ferroptosis-, m6A- related lncRNAs from expression data of EC. By constructing the prognosis model in EC, we screened hub lncRNA signatures affecting prognosis of EC patients. Furthermore, the guiding value of m6A-modified ferroptosis-related lncRNA (mfrlncRNA) features was assessed in terms of prognosis, immune microenvironment, and drug sensitivity.

Method: Our research harnessed gene expression data coupled with clinical insights derived from The Cancer Genome Atlas (TCGA) collection. To forge prognostic models, we adopted five machine learning approaches, assessing their efficacy through C-index and time-independent ROC analysis. We pinpointed prognostic indicators using the LASSO Cox regression approach. Moreover, we delved into the biological and immunological implications of the discovered lncRNA prognostic signatures.

Results: The survival rate for the low-risk group was markedly higher than that for the high-risk group, as evidenced by a significant log-rank test (p < 0.001). The LASSO Cox regression model yielded concordance indices of 0.76 for the training set and 0.77 for the validation set, indicating reliable prognostic accuracy. Enrichment analysis of gene functions linked the identified signature predominantly to endopeptidase inhibitor activity, highlighting the signature's potential implications. Additionally, immune function and drug density emphasized the importance of early diagnosis in EC.

Conclusion: Five hub lncRNAs in EC were identified through constructing the prognosis model. Those genes might be potential biomarkers to provide valuable reference for targeted therapy and prognostic assessment of EC.

Keywords: cuproptosis; lncRNA; machine learning; survival risk model; women's cancer.

FIGURE 1

The workflow of study.

FIGURE 2

lncRNA co‐expression network.

FIGURE 3

Comparison of C‐index between different model building methods.

FIGURE 4

Construction of prognosis model. (A and B) LASSO Cox regression for optimal predictor selection and complexity reduction. (C) Stepwise Cox regression to establish a prognostic framework. (D) Risk heatmap in test set. (E and F) Survival status of OC patients. Kaplan–Meier (K‐M) curves between high‐risk and low‐risk groups in training set (G), test set (H), and all set (I). Principal component analysis (PCA) differentiation between training (J) and test (K) cohorts.

FIGURE 5

Unveiling the predictive power of the mfclncRNA signature for cancer prognosis. Demonstrating risk score superiority with 5‐year ROC curve analysis against traditional clinical markers in training (A) and test cohorts (B). ROC curve insights for predictive accuracy at 1‐, 3‐, and 5‐year intervals using the refined model in both training (D) and test (E) environments. Prognostic model precision gauged by C‐index across training (C) and testing (F) phases.

FIGURE 6

Development of a nomogram based on mfcrlncRNA signature used to predict the survival of EC patients. (A) Predictive nomograms of EC patients. (B) Corresponding calibration curves of nomograms.

FIGURE 7

Gene function enrichment analysis of different expression signatures in high‐risk and low‐risk group. (A) GO analysis results of DESs in two risks groups. (B) KEGG pathways of DESs in two risks groups. (C) LncSEA enrichment analysis of identified mfclncRNAs.

FIGURE 8

(A and C) Waterfall chart illustrating the mutation profiles of 15 genes across various risk groups. (B) Survival analysis via Kaplan–Meier curves for patients categorized into high and low TMB groups. (D) Survival curves via Kaplan–Meier analysis for EC patients, showcasing differences based on tumor mutational burden (TMB) and assigned risk scores.

FIGURE 9

Comprehensive assessment of immune infiltration and immunotherapy efficacy. (A) Immune function comparison between high‐risk and low‐risk groups. (B) Assessment of nine immune cells' infiltration using CIBERSORT analysis in high‐risk and low‐risk groups. (C) The correlation analysis for prognostic signatures and immune cell profiles. Survival analysis of estimate score (D), immune score (E), and tumor purity (F). (G) Comparison of immune function for high‐risk and low‐risk groups.

FIGURE 10

(A–C) Different expression analysis of immune checkpoint in high‐ and low‐risk group. (D) TIDE score of EC patients in two risk groups. (E–J) Drug sensitivity analysis of EC patients. ***p < 0.001.