Novel prognostic genes of diffuse large B-cell lymphoma revealed by survival analysis of gene expression data

Abstract

Objective: This study aimed to identify prognostic genes for diffuse large B-cell lymphoma (DLBCL), using bioinformatic methods. Methods: Five gene expression data sets were downloaded from the Gene Expression Omnibus database. Significance analysis of microarrays algorithm was used to identify differentially expressed genes (DEGs) from two data sets. Functional enrichment analysis was performed for the DEGs with the Database for Annotation, Visualization and Integration Discovery (DAVID). Survival analysis was performed with the Kaplan–Meier method using function survfit from package survival of R for the other three data sets. Cox univariate regression analysis was used to further screen out prognostic genes. Results: Thirty-one common DEGs were identified in the two data sets, mainly enriched in the regulation of lymphocyte activation, immune response, and interleukin-mediated signaling pathway. Combined with 47 DLBCL-related genes acquired by literature retrieval, a total of 78 potential prognostic genes were obtained. Cases from the other three data sets were used in hierarchical clustering, and the 78 genes could cluster them into several subtypes with significant differences in survival curves. Cox univariate regression analysis revealed 45, 33, and eleven prognostic genes in the three data sets, respectively. Five common prognostic genes were revealed, including LCP2, TNFRSF9, FUT8, IRF4, and TLE1, among which LCP2, FUT8, and TLE1 were novel prognostic genes. Conclusion: Five prognostic genes of DLBCL were identified in this study. They could not only be used for molecular subtyping of DLBCL but also be potential targets for treatment.