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

Mengmeng Pan; Pingping Yang; Fangce Wang; Xiu Luo; Bing Li; Yi Ding; Huina Lu; Yan Dong; Wenjun Zhang; Bing Xiu; Aibin Liang

doi:10.3389/fgene.2021.648800

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

Front Genet. 2021 Jun 9:12:648800. doi: 10.3389/fgene.2021.648800. eCollection 2021.

Authors

Mengmeng Pan^{1

2}, Pingping Yang¹, Fangce Wang¹, Xiu Luo¹, Bing Li¹, Yi Ding¹, Huina Lu¹, Yan Dong¹, Wenjun Zhang¹, Bing Xiu¹, Aibin Liang¹

Affiliations

¹ Department of Hematology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
² National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: With 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.

Methods: The 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.

Results: We 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.

Conclusion: We 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.

Keywords: biomarkers; diffuse large B cell lymphoma; overall survival; prognosis; risk score.