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Updated: Oct 9, 2025
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Published on: February 25, 2020
Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related
Dan Xiong1, Mojuan Li2, Chong Zeng3
1Departments of Hematology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong 528300, China.
Backgrounds:
The prognostic significance of ferroptosis-related genes is well known. However, survival- and ferroptosis-related genes are not currently considered in risk scoring models for diffuse large B-cell lymphoma (DLBCL).
Materials And Methods:
Ferroptosis regulators and markers were downloaded from the FerrDb database. The transcriptome profiling data were collected from the cancer genome atlas (TCGA). Transcriptome data and corresponding clinical information of DLBCL were downloaded from the gene expression omnibus (GEO). The validation data were downloaded using the UCSC Xena browser. ConsensusClusterPlus was used to categorize DLBCL samples according to gene expression profiles. The survival function was plotted with the Kaplan-Meier plots. The nomogram was built using multivariate logistic regression analysis and the Cox proportional hazards regression model.
Results:
Based on the GSE11318 dataset of 203 samples and 267 ferroptosis-related gene expression profiles, we identified four clusters. A total of 19 survival-related genes were found associated with ferroptosis. The prognostic risk scoring model was constructed based on the regression coefficients. The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively. DLBCL samples with cluster 2 or cancer stage IV have shorter survival. Correlations between the immune infiltration and risk scores of the 12 immune cells were demonstrated. The response of DLBCL to doxorubicin was effectively validated by the risk scoring model.
Conclusions:
In this study, a ferroptosis-based risk scoring model for patients with DLBCL was constructed and validated in an independent dataset. This risk score model has a better efficacy in predicting survival compared to clinical characteristics.
