The following is a summary of “Identification of a novel model based on ferroptosis-related genes for predicting the prognosis of diffuse large B-cell lymphomas,” published in the May 2023 issue of Hematology by Wang, et al.
Diffuse large B-cell lymphomas (DLBCLs) are characterized by their phenotypic and genetic heterogeneity. For a study, researchers sought to develop a prognostic signature based on ferroptosis-related genes (FRGs) to predict the outcome of DLBCLs.
In the retrospective study, they analyzed the mRNA expression levels and clinical data of 604 DLBCL patients from three publicly available GEO datasets. Cox regression analysis was performed to identify FRGs with prognostic value. ConsensusClusterPlus was used to classify DLBCL samples based on gene expression patterns. The least absolute shrinkage and selection operator (LASSO) method and univariate Cox regression were employed to construct the FRG prognostic signature. The association between the FRG model and clinical characteristics was also investigated.
Nineteen FRGs with potential prognostic value were identified, and the patients were classified into two clusters (cluster 1 and cluster 2). Cluster 1 exhibited a shorter overall survival (OS) time compared to Cluster 2. The two clusters also showed distinct patterns of infiltrating immune cells. Using LASSO, a six-gene risk signature (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1) was generated. Based on this signature, a risk score formula and prognostic model were constructed to predict the OS of DLBCL patients. Kaplan-Meier survival analysis demonstrated that higher-risk patients, as stratified by the prognostic model, had poorer OS in both the training and test cohorts. Furthermore, decision curve analysis and calibration plots confirmed the good agreement between the predicted results and actual observations.
They developed and validated a novel FRG-based prognostic model that can effectively predict the outcomes of DLBCL patients. The model may be useful in clinical practice for risk stratification and personalized treatment decisions for DLBCL.
Source: tandfonline.com/doi/full/10.1080/16078454.2023.2198862
