Numerous studies have investigated the prognostic values of MYC and/or BCL2 protein overexpression in diffuse large B-cell lymphoma (DLBCL). However, the results still demonstrate discrepancies among different studies. We aimed to do a systematic review and meta-analysis on the relationships between overexpression MYC and/or BCL2 and DLBCLs treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). This study followed the guidelines of PRISMA and Cochrane handbook. The hazard ratios (HRs) for overall survival (OS) were pooled to estimate the main effect size. Twenty studies recruited a total of 5576 patients were available for this meta-analysis. The results showed that MYC (HR = 1.96, 95%CI (confidence interval) = 1.69–2.27)without heterogeneity(I2 = 17.2%, P = 0.280), BCL2 (HR = 1.65, 95%CI = 1.43–1.89, I2 = 20.7%, P = 0.234) protein overexpression, and co-overexpression (HR = 2.58, 95%CI = 2.19–3.04, I2 = 17.2%, P = 0.275) had a poor prognosis in R-CHOP treated DLBCL patients, respectively. The current analysis indicated that MYC and/or BCL2 protein overexpression, and particularly co-overexpression was related to short overall survival in R-CHOP treated DLBCL patients, showing that application of the two new biomarkers can help to better stratify DLBCL patients and guide targeted treatment.
Background Gene expression profiling (GEP) is considered as gold standard for cell-of-origin classification of diffuse large B-cell lymphoma (DLBCL). The high dimensionality of GEP limits its application in clinical practice. Penalized regression was commonly used to determine the optimal gene subset for classification in high dimensional gene data. However, the results of penalized regression methods were affected by the tuning parameters.Results To solve the instability of penalized regression methods, we proposed a strategy to measure the importance of variables with an aggregated index. This strategy was applied to six penalized methods to identify a small gene subset for DLBCL classification. Using a training dataset of 350 DLBCL patients, we identified six genes (MYBL1, TNFRSF13B, MAML3, CYB5R2, BATF, and S1PR2) as the optimal gene subset for DLBCL classification. The AUC was 0.9986 (95%CI 0.9967–1) and discrimination slope (DS) was 0.9442 (95%CI 0.9203–0.9661) in the training dataset. The discriminative performances were further validated in the external dataset with an AUC of 0.9455 (95%CI 0.9298–0.9612) and DS of 0.6211 (95%CI 0.5824–0.6591). Additionally, the calibration and clinical usefulness were apt in both datasets. Subgroups of patients characterized by these six genes showed significantly different prognosis. Furthermore, model comparisons demonstrated that the six-gene model outperformed models constructed by typical penalized regression methods.Conclusions The six genes had considerable clinical usefulness in DLBCL classification and prognosis. Penalized variable importance analysis is an efficient strategy to identify an optimal gene subset with good predictive performance.
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