Abstract
Aiming to find key genes and events, we analyze a large data set on diffuse large B-cell lymphoma (DLBCL) gene-expression (248 patients, 12196 spots). Applying the loess normalization method on these raw data yields improved survival predictions, in particular for the clinical important group of patients with medium survival time. Furthermore, we identify a simplified prognosis predictor, which stratifies different risk groups similarly well as complex signatures. We identify specific, activated B cell-like (ABC) and germinal center B cell-like (GCB) distinguishing genes. These include early (e.g. CDKN3) and late (e.g. CDKN2C) cell cycle genes. Independently from previous classification by marker genes we confirm a clear binary class distinction between the ABC and GCB subgroups. An earlier suggested third entity is not supported. A key regulatory network, distinguishing marked over-expression in ABC from that in GCB, is built by: ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5. It predicts and supports the aggressive behaviour of the ABC subgroup. These results help to understand target interactions, improve subgroup diagnosis, risk prognosis as well as therapy in the ABC and GCB DLBCL subgroups.
Highlights
Diffuse large B-cell lymphomas (DLBCL) are the most frequent B cell Non-Hodgkin’s lymphomas
Statistical validation of the DLBCL subgroups activated B cell-like (ABC) DLBCL and germinal center B cell-like (GCB) DLBCL Both subgroups were originally introduced on the basis of gene expression profiling
Cell cycle genes are differently expressed in ABC and GCB Cell cycle is critical for cancer cell proliferation and we investigated by Prediction Analysis of Microarrays” (PAM) analysis whether the functional group of cell cycle genes alone could separate the two B-cell lymphoma groups
Summary
Diffuse large B-cell lymphomas (DLBCL) are the most frequent B cell Non-Hodgkin’s lymphomas. Starting with 36 well known DLBCL prognosis genes from the literature, Lossos et al (2004) found a six gene based outcome predictor and applied it to the data sets of Alizadeh et al (2000) and Rosenwald et al (2002).
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