Ferroptosis-Related Gene Signature: A New Method for Personalized Risk Assessment in Patients with Diffuse Large B-Cell Lymphoma

Abstract

Purpose: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease, which makes prognostic prediction challenging. The rapid development of research on ferroptosis provides the possibility of its use in prognosis in cancer patients. The aim of the current investigation was to perform a systematic study of ferroptosis and DLBCL prognosis to identify prognostic biomarkers in DLBCL.

Materials and methods: A total of 884 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. Univariate Cox regression analysis was used to investigate relationships between gene expression and prognostic values. Ferroptosis-related genes associated with overall survival in the training set were then extracted, and the least absolute shrinkage and selection operator Cox regression model was used to establish an eight-gene signature, comprising ZEB1, PSAT1, NGB, NFE2L2, LAMP2, HIF1A, FH, and CXCL2.

Results: The signature exhibited significant independent prognostic value in both the training set and the validation set. It also exhibited strong prognostic value in subgroup analysis. A nomogram integrating the eight-gene signature and components of the International Prognostic Index facilitated reliable prognostic prediction.

Conclusion: A novel and reliable ferroptosis-related gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of survival rate was developed. It could be used for prognostic prediction in DLBCL patients. Targeting ferroptosis may be a therapeutic alternative in DLBCL.

Keywords: Gene Expression Omnibus database; diffuse large B-cell lymphoma; ferroptosis; prognostic; signature.

Figure 1

LASSO Cox regression (A) performed using 104 ferruginous disease-related genes to obtain the prognostic characteristics of 8 genes (B). A total of 104 ferroptosis-related genes was used to construct the PPI network (C).

Figure 2

Distributions of risk scores (A), death status, survival time (B), and expression levels of the eight genes used to derive the prognostic signature tool (C) were visualized to evaluate the prognostic difference between a high-risk group and a low-risk group after LASSO Cox regression. Time-dependent ROC curve and area under the curve of the signature (D). Kaplan-Meier plots of overall survival in the high-risk and low-risk groups in the training set determined via Log rank testing (E).

Figure 3

Correlations between risk score and age (A), ECOG (B), IPI score (C), LDH (D), DLBCL stage (E), extranodal sites (F).*p < 0.05, ***p < 0.001, ****p < 0.0001, ns, p > 0.05.

Figure 4

Kaplan-Meier plots of overall survival in high-risk and low-risk activated B cell subtype (A), germinal center B cell subtype (D), stage I–II (B), stage III–IV (E), IPI < 2 (C), and IPI ≥ 2 (F) derived via Log rank testing. Red is the high-risk group and blue is the low-risk group.

Figure 5

Kaplan-Meier plots of overall survival in high-risk and low-risk subgroups in the GSE10846 dataset derived via Log rank testing (A). The time-dependent ROC curve and AUC in the validation set (B).

Figure 6

Construction of a nomogram to forecast 12, 36, and 60-month survival using the IPI components and the eight-gene model (A). The calibration chart shows that the predicted survival rate was consistent with the actual survival rate for 12 months (B), 36 months (C), and 60 months (D). IPI, International Prognostic Index.