Construction of a DLBCL Prognostic Signature Based on Tumor Microenvironment.

BACKGROUNDS: Diffuse large B-cell lymphoma (DLBCL) is a common curable non-Hodgkin's lymphoma. Patients with this disease can be cured after the R-CHOP immunochemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). Nonetheless, most cured patients will relapse again and have dismal prognosis. In this study, we aim to identify a potential biomarker by analyzing gene expression data, and to predict patient's survival rate by constructing a risk model.

METHODS

Firstly, mRNA chip data (GSE87371) and clinical data of DLBCL patients were obtained from Gene Expression Omnibus (GEO). Samples were scored with estimate package. The obtained stromal score (P < 0.05) and ESTIMATE score (P < 0.05) were significantly correlated with the prognosis. Differentially expressed genes (DEGs) screened through the above two scoring methods were intersected and 279 DEGs were obtained. Next, five feature genes (CD163, CLEC4A, COL15A1, GABRB2, IFIT3) were identified by univariate Cox, LASSO and multivariate Cox regression analyses to establish a risk evaluation model. Thereafter, the 5-gene risk model was validated on a validation set. ROC and survival analyses were performed to assess the performance of the model.

RESULTS

Further analysis showed that the risk model was capable of independently determining the prognosis of patients, and a nomogram was sequentially established.

CONCLUSIONS

Authors screened DEGs related to ESTIMATE and stromal scores from GEO database, and established a 5-gene prognostic signature through Cox regression analysis and LASSO analysis. The risk model and nomogram will help individuals accurately predict the prognosis of DLBCL patients.