The Identification and Analysis of a Novel Model Based on Ferroptosis-Related Genes for Predicting the Prognosis of Diffuse Large B-Cell Lymphomas

Abstract

Backgroud: Diffuse large B-cell lymphomas (DLBCLs) are featured as phenotypically and genetically heterogeneous. Ferroptosis is a newly discovered regulated cell death pathway that plays a crucial role in the occurrence and progression of tumors. We aim to identify a ferroptosis-related gene (FRG) prognostic signature for DLBCLs by systematic analysis of transcriptional profiles. Methods: This study retrospectively analysed the transcriptome profiles and clinical parameters of 604 DLBCL patients from 3 public datasets. A series of bioinformatic approaches including univariate and multivariate Cox regression analysis, function analysis, immune infiltration analysis, differential expression analysis, ROC curve analysis, Kaplan–Meier survival curve and the least absolute shrinkage and selection operator (LASSO) method by the corresponding R packages in R software were combined to explored the heterogenicity of FRG based clusters and to built prognostic model. Immunohistochemistry was used to exam the protein expression of six FRGs in different type of DLBCL.Results: We first identified 19 FRGs with potential prognostic values and classfied the patients into two subgroups (named cluster 1 and cluster 2), Results showed that there were different patterns of immune cell infiltration among patients in the two clusters. Furthermore, the LASSO was used to generated a six genes (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1) risk signature which constructed a risk score formula and prognostic model for the overall survival (OS) of DLBCL patients. Kaplan–Meier survival analysis proved that poorer OS was exhibited in higher risk patients stratified by the prognostic model in both the training cohort and test cohort. In addtion, we constructed nomograms to predict the OS of DLBCL patients. Both the decision curve(DCA) and the calibration plots showed that the nomogram had good predictive performance. Finally, the validation by immunohistochemistry indicated the GCLC, LPCAT3, NFE2L2, SLC1A5, and GOT1 were high expressed in DLBCL with various prognostic adverse molecular factor. Conclusion: In sum, we built a new FRG-based prognostic model which will help improve diagnosis and treatment for DLBCL patients.