Hikaru Murakami

Discovery of a Ferroptosis-Related lncRNA–miRNA–mRNA Gene Signature in Endometrial Cancer Through a Comprehensive Co-Expression Network Analysis

Background: As a newly recognized type of cell death implicated in cancer, ferroptosis plays multiple roles in tumor biology. Here, we sought to construct a prognostic framework for EC on the basis of ferroptosis-related long non-coding RNAs (FerlncRNAs), microRNAs (FermiRNAs), and mRNAs (FRGs) for endometrial cancer (EC). Methods: Transcriptomic profiles of tumors and matched clinical data for 544 EC patients were retrieved from TCGA-UCEC. A prognostic framework was generated through Cox regression, integrating ferroptosis-linked lncRNAs, miRNAs, and mRNAs. EC cases were stratified into groups with high or low predicted risk based on ferroptosis-related gene expression. The model’s prognostic utility was examined through Kaplan–Meier (K–M) analysis and receiver operating characteristic curves. Results: A prognostic model based on 16 RNAs, including 10 FerlncRNAs, 2 FermiRNAs, and 4 FRGs, was developed. Analysis using K–M plots showed that high-risk patients experienced shorter overall survival than their low-risk counterparts (p < 0.001). The model’s area under curve (AUC) values were 0.731, 0.749, and 0.768 at 1-, 3-, and 5-year time points, surpassing those of standard clinical parameters. Furthermore, in an external validation cohort, these signature RNAs were associated with EC prognosis. Conclusions: The novel ferroptosis-related lncRNA–miRNA–mRNA prognostic model provides a basis to assess clinical prognosis in EC patients.

A novel ferroptosis-related microRNAs signature for predicting prognosis in endometrial cancer: An observational study

Ferroptosis plays an important role in various cancer processes and is regulated by microRNAs. This study aimed to establish a prognostic model based on ferroptosis-related microRNAs (FermiRNAs) to predict the prognosis of endometrial cancer (EC). Tumor transcriptomes and corresponding clinical data of 544 EC patients were downloaded from the cancer genome atlas database, and the ferroptosis database (FerrDb) was used to identify ferroptosis-related genes (mRNAs). FermiRNAs in EC were selected based on their correlation with ferroptosis-related genes. Univariate and multivariate Cox regression analyses were conducted to construct a prognostic model based on the miRNA signature. EC patients were grouped into high- and low-risk categories based on the risk score of the prognostic model. Kaplan–Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the prognostic value of risk scores. A predictive nomogram was then established. Finally, we compared the proportion of infiltrating immune cells and the expression of potential immune checkpoints between the 2 groups to understand the tumor immune microenvironment associated with signature FermiRNAs. A prognostic model based on the 2 FermiRNAs (miR-4635 and miR-3131) was developed. Kaplan–Meier survival analysis indicated that patients with high-risk scores had worse overall survival ( P  < .001). ROC curves showed that the area under curve values of the prognostic model were 0.621, 0.712, and 0.696 for 1, 3, and 5 years, respectively, indicating good predictive ability. ROC curves also indicated that the prognostic model had a better capability to predict the prognosis of patients with EC than the other clinical factors. A predictive nomogram suggested that the risk model could offer independent prognostic evaluation with high accuracy. The tumor immune microenvironment, including infiltrating immune cells and immune checkpoints, showed several differences between patients with high- and low-risk scores. In an external validation cohort (213 EC patients from the clinical proteomic tumor analysis consortium dataset), 2 FermiRNAs were confirmed to be associated with EC prognosis in the same manner. A novel ferroptosis-related miRNA prognostic model is useful for predicting the prognosis of patients with EC.