Genes In Diffuse Large B-cell Lymphoma Research Articles

High-grade B-cell lymphoma not otherwise specified, with diffuse large B-cell lymphoma gene expression signatures: Genomic analysis and potential therapeutics.

High-grade B-cell lymphoma not otherwise specified (HGBCL, NOS) has overlapping morphological and genetic features with diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL), leading to uncertainty in its diagnosis and clinical management. Using functional genomic approaches, we previously characterized HGBCL and NOS, that demonstrate gene expression profiling (GEP), and genetic signatures similar to BL. Herein, we characterize distinct HGBCL, NOS, cohort (n = 55) in adults (n = 45) and in children (n = 10), and compared the GEP, genomic DNA copy number (CN), and mutational spectrum with de novo DLBCL (n = 85) and BL (n = 52). This subgroup, representing ~60% of HGBCL, NOS, lack gene-expression signature of BL and double hit/dark zone lymphoma, but express DLBCL like signatures and are characterized by either GCB- or ABC-like mRNA signatures and exhibit higher genomic complexity, similar to de novo DLBCL, and show alteration in genes regulating B-cell activation (CD79B, MYD88, PRDM1, TBLIXR1, CARD11), epigenome (KMT2D, TET2) and cell cycle transition (TP53, ASPM). However, recurrent mutations in genes often mutated in BL (DDX3X, GNA13, CCND3), but rare in DLBCL, are also present in HGBCL-NOS, highlighting genetic heterogeneity. Consistent with mutation spectrum, frequent genomic CN alterations in genes regulating B-cell activation (del-PRDM1, gain-BCL6, -REL, -STAT3) and cell cycle regulators (del-TP53, del-CDKN2A, del-RB1, gain-CCND3) were observed. Pediatric cases showed GCB-DLBCL-like mRNA signatures, but also featured hallmark mutations of pediatric BL. Frequent oncogenic PIM1 mutations were present in adult HGBCL, NOS. In vitro analyses with pharmacologic or genetic inhibition of PIM1 expression triggered B-cell activation and NF-κB-induced apoptosis, suggesting that PIM1 is a rational therapeutic target.

Reconstruction of Canine Diffuse Large B-cell Lymphoma Gene Regulatory Network: Detection of Functional Modules and Hub Genes

Lymphoma is one of the most common malignancies in dogs. Canine lymphoma is similar to human non-Hodgkin's lymphoma (NHL) with shared clinical presentation and histopathological features. This study reports the construction of a comprehensive gene regulatory network (GRN) for canine diffuse large B-cell lymphoma (DLBCL), the most common type of canine lymphoma, and performs analysis for detection of major functional modules and hub genes (the most important genes in a GRN). The canine DLBCL GRN was reconstructed from gene expression data (NCBI GEO dataset: GSE30881) using the STRING and MiMI interaction databases. Reconstructed GRNs were then assessed, using various bioinformatics programmes, in order to analyze network topology and identify major pathways and hub genes. The resultant network from both interaction databases had a logically scale-free pattern. Gene ontology (GO) analysis revealed cell activation, cell cycle phase, immune effector process, immune system development, immune system process, integrin-mediated signalling pathway, intracellular protein kinase cascade, intracellular signal transduction, leucocyte activation and differentiation, lymphocyte activation and differentiation as major GO terms in the biological processes of the networks. Moreover, bioinformatics analysis showed E2F1, E2F4, PTEN, CDKN1A, PCNA, DKC1, MNAT1, NDUFB4, ATP5J, PRKDC, BRCA1, MYCN, RFC4 and POLA1 as the most important hub genes. The phosphatidyl inositol signalling system, P53 signalling pathway, Rac CycD pathway, G1/S checkpoint, chemokine signalling pathway and telomere maintenance were the main signalling pathways in which the protein products of the hub genes are involved.

Identifying Functional Modules of Diffuse Large B-cell Lymphoma Gene Co-expression networks by Hierarchical Clustering Method Based on Random Matrix Theory

In this work, we propose an original method, the Random matrix theory (RMT)-based hierarchical clustering method, to identify functional gene networks of diffuse large B-cell Lymphoma (DLBCL) gene co-expression networks. Comparing topological approach, the RMT-based hierarchical clustering method is effective in representing not only the strong correlations between genes inside the modules (the modularity and independency of networks), but also the weak correlations between different modules (the hierarchy of networks). We show that missing expression values among microarray dataset should not be neglected, and different imputation methods result in different performances. We suggest LLS to estimate missing values for better performance in accuracy and stability. Based on the RMT, the random noises are separated from DLBCL gene expression data. We use normalized root mean squared error (NRMSE) ratio method to identify a transition zone of NNSDs, and for DLBCL networks it is [0.71, 0.84].