Whole Transcriptome Data Analysis Reveals Prognostic Signature Genes for Overall Survival Prediction in Diffuse Large B Cell Lymphoma

Abstract

BackgroundWith the improvement of clinical treatment outcomes in diffuse large B cell lymphoma (DLBCL), the high rate of relapse in DLBCL patients is still an established barrier, as the therapeutic strategy selection based on potential targets remains unsatisfactory. Therefore, there is an urgent need in further exploration of prognostic biomarkers so as to improve the prognosis of DLBCL.MethodsThe univariable and multivariable Cox regression models were employed to screen out gene signatures for DLBCL overall survival (OS) prediction. The differential expression analysis was used to identify representative genes in high-risk and low-risk groups, respectively, where student t test and fold change were implemented. The functional difference between the high-risk and low-risk groups was identified by the gene set enrichment analysis.ResultsWe conducted a systematic data analysis to screen the candidate genes significantly associated with OS of DLBCL in three NCBI Gene Expression Omnibus (GEO) datasets. To construct a prognostic model, five genes (CEBPA, CYP27A1, LST1, MREG, and TARP) were then screened and tested using the multivariable Cox model and the stepwise regression method. Kaplan–Meier curve confirmed the good predictive performance of this five-gene Cox model. Thereafter, the prognostic model and the expression levels of the five genes were validated by means of an independent dataset. High expression levels of these five genes were significantly associated with favorable prognosis in DLBCL, both in training and validation datasets. Additionally, further analysis revealed the independent value and superiority of this prognostic model in risk prediction. Functional enrichment analysis revealed some vital pathways responsible for unfavorable outcome and potential therapeutic targets in DLBCL.ConclusionWe developed a five-gene Cox model for the clinical outcome prediction of DLBCL patients. Meanwhile, potential drug selection using this model can help clinicians to improve the clinical practice for the benefit of patients.

Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

“…S7 ). Furthermore, the comparison of ROC curves with existing gene expression-based classifiers [ 27 , 28 , 41 ] demonstrated that our 24-gene risk score had the best performance in predicting two-year outcomes (Fig. S8 ).…”

“…However, the focus has been largely on OS, rather than specifically addressing features associated with early disease progression. Some studies have developed risk models by incorporating OS data from patients treated with CHOP and R-CHOP [ 27 , 28 ], whereas some risk signatures were developed without validation cohorts or based on limited sample sizes [ 26 , 29 ]. Moreover, none of these risk models/signatures attempted to integrate both genetic and transcriptomic data in their evaluation.…”

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

“…However, it is worth noting that ACSM3 has demonstrated tumor-immunosuppressive properties in high-grade serous ovarian cancer, and ENPP7 activity has been relatively low in diseases with an increased risk of liver tumorigenesis [ 39 , 40 ]. Among the 16 genes, only FASN and CYP27A1 have been previously reported in DLBCL [ 15 , 16 , 41 , 42 ]. Nevertheless, the biological processes in which these 16 genes are involved have been well-established in DLBCL, including biosynthesis, transport, and beta-oxidation of fatty acids, as well as acyl-CoA flux within cells and lipid metabolism [ 16 , 17 , 43 – 45 ].…”