Related Experiment Video
Updated: Jul 30, 2025
Author Spotlight: Tracing the Ferroptotic Signatures and Cell Death Dynamics in Medulloblastoma for Advanced Therapeutics
Published on: March 15, 2024
Identification of a novel model based on ferroptosis-related genes for predicting the prognosis of diffuse large
Jiayi Wang1, Hongling Peng2, Guangsen Zhang2
1Department of Anesthesiology, The Second Xiangya hospital, Central South University, Changsha, People's Republic of China.
Background:
Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. We aimed to build a ferroptosis-related gene (FRG) prognostic signature to predict the outcome of DLBCLs.
Methods:
Our study retrospectively investigated the mRNA expression level and clinical data of 604 DLBCL patients from three GEO public datasets. We performed Cox regression analysis to extract the FRGs with prognostic values. ConsensusClusterPlus was used to categorize the DLBCL samples according to gene expression. The least absolute shrinkage and selection operator (LASSO) method and univariate Cox regression were implemented to construct the FRG prognostic signature. The association between the FRG model and clinical characteristics was also investigated.
Results:
We identified 19 FRGs with potential prognostic values and classified the patients into clusters 1 and 2. Cluster 1 showed a shorter overall survival (OS) time than cluster 2. The two clusters had different patterns of infiltrating immune cells. LASSO was used to generate a six-gene risk signature (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1), based on which a risk score formula and prognostic model were constructed for predicting the OS of DLBCL patients. Kaplan-Meier survival analysis proved that poorer OS was exhibited in the higher-risk patients stratified by the prognostic model in both the training and test cohorts. In addition, both the decision curve and the calibration plots showed that the nomogram had good agreement between the predicted results and actual observations.
Conclusions:
We developed and validated a novel FRG-based prognostic model which could help to predict the outcomes of DLBCL patient.
