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

Abstract

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