Marker gene identification for gene expression analysis — Review and new findings for Diffused Large B Cell Lymphoma (DLBCL)

2016 ◽  
Author(s):  
Xi-Guang Wei ◽  
Ho-Chun Wu ◽  
Shing-Chow Chan
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Analysis ◽  
Cell Lymphoma ◽  
Marker Gene ◽  
B Cell Lymphoma ◽  
Gene Identification ◽  
Large B Cell Lymphoma ◽  
New Findings ◽  
Large B Cell

scholarly journals Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Selin Merdan ◽  
Kritika Subramanian ◽  
Turgay Ayer ◽  
Johan Van Weyenbergh ◽  
Andres Chang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cell ◽  
Risk Prediction ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Signatures ◽  
Large B Cell Lymphoma ◽  
Large B Cell

AbstractThe clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.

scholarly journals Genome-wide DNA methylation and RNA-seq analyses identify genes and pathways associated with doxorubicin resistance in a canine diffuse large B-cell lymphoma cell line

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250013
Author(s):  
Chia-Hsin Hsu ◽  
Hirotaka Tomiyasu ◽  
Chi-Hsun Liao ◽  
Chen-Si Lin
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Rna Seq ◽  
Doxorubicin Resistance ◽  
Large B Cell Lymphoma ◽  
Genome Wide ◽  
Large B Cell

Doxorubicin resistance is a major challenge in the successful treatment of canine diffuse large B-cell lymphoma (cDLBCL). In the present study, MethylCap-seq and RNA-seq were performed to characterize the genome-wide DNA methylation and differential gene expression patterns respectively in CLBL-1 8.0, a doxorubicin-resistant cDLBCL cell line, and in CLBL-1 as control, to investigate the underlying mechanisms of doxorubicin resistance in cDLBCL. A total of 20289 hypermethylated differentially methylated regions (DMRs) were detected. Among these, 1339 hypermethylated DMRs were in promoter regions, of which 24 genes showed an inverse correlation between methylation and gene expression. These 24 genes were involved in cell migration, according to gene ontology (GO) analysis. Also, 12855 hypermethylated DMRs were in gene-body regions. Among these, 353 genes showed a positive correlation between methylation and gene expression. Functional analysis of these 353 genes highlighted that TGF-β and lysosome-mediated signal pathways are significantly associated with the drug resistance of CLBL-1. The tumorigenic role of TGF-β signaling pathway in CLBL-1 8.0 was further validated by treating the cells with a TGF-β inhibitor(s) to show the increased chemo-sensitivity and intracellular doxorubicin accumulation, as well as decreased p-glycoprotein expression. In summary, the present study performed an integrative analysis of DNA methylation and gene expression in CLBL-1 8.0 and CLBL-1. The candidate genes and pathways identified in this study hold potential promise for overcoming doxorubicin resistance in cDLBCL.

scholarly journals An NCCN-IPI based immune-related gene prognostic model for diffuse large B-cell lymphoma

2021 ◽  
Author(s):  
Shidai Mu ◽  
Deyao Shi ◽  
Lisha Ai ◽  
Fengjuan Fan ◽  
Fei Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Significance ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Risk Patients ◽  
Large B Cell

AbstractBackgroundAn enhanced International Prognostic Index (NCCN-IPI) was built to better discriminate diffuse large B-cell lymphoma (DLBCL) patients in the rituximab era. However, there is an urgent need to identify novel valuable biomarkers in the context of targeted therapies, such as immune checkpoint blockade (ICB) therapy.MethodsGene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. 73 immune-related hub genes in DLBCL patients with different IPI levels were identified by weighted gene co-expression network analysis (WGCNA), and 4 genes were selected to construct an IPI-based immune-related prognostic model (IPI-IPM). Afterward, the genetic, somatic mutational and molecular profiles of IPI-IPM subgroups were analyzed, as well as the potential clinical response of ICB in different IPI-IPM subgroups.ResultsThe IPI-IPM was constructed base on the expression of LCN2, CD5L, NLRP11 and SERPINB2, where high-risk patients had shorter overall survival (OS) than low-risk patients, consistent with the results in the GEO cohorts. The comprehensive results showed that a high IPI-IPM risk score was correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, high infiltration of CD8+ T cells and macrophages (M1, M2), as well as up-regulation of inhibitory immune checkpoints including PD-L1, LAG3 and BTLA, indicating more potential response to ICB therapy.ConclusionThe IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms on tumor progression and drug resistance, also sheds a light on developing immunotherapy for DLBCL.

Cell-of-Origin in Diffuse Large B-Cell Lymphoma: Are the Assays Ready for the Clinic?

2015 ◽  
pp. e458-e466 ◽  
Author(s):  
David W. Scott
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Practical Implementation ◽  
Cell Of Origin ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma worldwide and consists of a heterogeneous group of cancers classified together on the basis of shared morphology, immunophenotype, and aggressive clinical behavior. It is now recognized that this malignancy comprises at least two distinct molecular subtypes identified by gene expression profiling: the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB) groups—the cell-of-origin (COO) classification. These two groups have different genetic mutation landscapes, pathobiology, and outcomes following treatment. Evidence is accumulating that novel agents have selective activity in one or the other COO group, making COO a predictive biomarker. Thus, there is now a pressing need for accurate and robust methods to assign COO, to support clinical trials, and ultimately guide treatment decisions for patients. The “gold standard” methods for COO are based on gene expression profiling (GEP) of RNA from fresh frozen tissue using microarray technology, which is an impractical solution when formalin-fixed paraffin-embedded tissue (FFPET) biopsies are the standard diagnostic material. This review outlines the history of the COO classification before examining the practical implementation of COO assays applicable to FFPET biopsies. The immunohistochemistry (IHC)-based algorithms and gene expression–based assays suitable for the highly degraded RNA from FFPET are discussed. Finally, the technical and practical challenges that still need to be addressed are outlined before robust gene expression–based assays are used in the routine management of patients with DLBCL.

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