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首页> 外文期刊>Cancer Informatics >Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival
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Germinal Center B Cell-Like (GCB) and Activated B Cell-Like (ABC) Type of Diffuse Large B Cell Lymphoma (DLBCL): Analysis of Molecular Predictors, Signatures, Cell Cycle State and Patient Survival

机译:弥漫性大B细胞淋巴瘤(DLBCL)的生发中心B细胞样(GCB)和活化B细胞样(ABC)类型:分子预测因子,特征,细胞周期状态和患者存活率的分析

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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 specifi c, 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.
机译:为了找到关键基因和事件,我们分析了弥漫性大B细胞淋巴瘤(DLBCL)基因表达的大数据集(248例患者,12196个斑点)。在这些原始数据上应用黄土归一化方法可以提高生存预测,特别是对于具有中等生存时间的临床重要患者群。此外,我们确定了一种简化的预后预测因子,可将不同的风险组以及复杂的特征进行分层。我们确定特定的,激活的B细胞样(ABC)和生发中心B细胞样(GCB)区分基因。这些包括早期(例如CDKN3)和晚期(例如CDKN2C)细胞周期基因。独立于以前的标记基因分类,我们确认了ABC和GCB亚组之间明显的二元分类。不支持较早建议的第三实体。关键的监管网络是由ASB13,BCL2,BCL6,BCL7A,CCND2,COL3A1,CTGF,FN1,FOXP1,IGHM,IRF4,LMO2,LRMP,MAPK10,MME建立的,用以区分ABC和GCB中明显的过表达。 ,MYBL1,NEIL1和SH3BP5。它预测并支持ABC小组的攻击行为。这些结果有助于了解目标相互作用,改善ABC和GCB DLBCL亚组的亚组诊断,风险预后以及治疗。

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