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.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2025-11-21
DOI
https://doi.org/10.1097/md.0000000000046102
Primary Topic
Ferroptosis and cancer prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Junlong Wang, Hikaru Murakami, Herbert Yu
Medicine
Ferroptosis and cancer prognosis
article

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

Junlong Wang, Hikaru Murakami, Herbert Yu
article en

Abstract

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.

MedicineVol. 104(47)
University of Hawaiʻi at Mānoa (US), University of Hawaii System (US), Cancer Center of Hawaii (US), University of Hawaii Cancer Center
Openalex Percentile: Top 45%
Ferroptosis and cancer prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.