scholarly journals Identification of Hub Genes and Key Pathways Associated with Two Subtypes of Diffuse Large B-Cell Lymphoma Based on Gene Expression Profiling via Integrated Bioinformatics

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zijian Liu ◽  
Jingshu Meng ◽  
Xiaoqian Li ◽  
Fang Zhu ◽  
Tao Liu ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Large B Cell Lymphoma ◽  
Key Pathways ◽  
Large B Cell

There is a significant difference in prognosis between the germinal center B-cell (GCB) and activated B-cell (ABC) subtypes of diffuse large B-cell lymphoma (DLBCL). However, the signaling pathways and driver genes involved in these disparate subtypes are ambiguous. This study integrated three cohort profile datasets, including 250 GCB samples and 250 ABC samples, to elucidate potential candidate hub genes and key pathways involved in these two subtypes. Differentially expressed genes (DEGs) were identified. After Gene Ontology functional enrichment analysis of the DEGs, protein-protein interaction (PPI) network and sub-PPI network analyses were conducted using the STRING database and Cytoscape software. Subsequently, the Oncomine database and the cBioportal online tool were employed to verify the alterations and differential expression of the 8 hub genes (MME, CD44, IRF4, STAT3, IL2RA, ETV6, CCND2, and CFLAR). Gene set enrichment analysis was also employed to identify the intersection of the key pathways (JAK-STAT, FOXO, and NF-κB pathways) validated in the above analyses. These hub genes and key pathways could improve our understanding of the process of tumorigenesis and the underlying molecular events and may be therapeutic targets for the precise treatment of these two subtypes with different prognoses.

scholarly journals NEK2 Promotes Cell Proliferation and Glycolysis by Regulating PKM2 Abundance via Phosphorylation in Diffuse Large B-Cell Lymphoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lingna Zhou ◽  
Liya Ding ◽  
Yuqi Gong ◽  
Jing Zhao ◽  
Jing Zhang ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
Affinity Purification ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Lymphoid Tissues ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is the most frequent and commonly diagnosed subtype of NHL, which is characterized by high heterogeneity and malignancy, and most DLBCL patients are at advanced stages. The serine/threonine kinase NEK2 (NIMA-related kinase 2), a member of NIMA-related kinase (NEK) family that regulates cell cycle, is upregulated in a variety of malignancies, including diffuse large B-cell lymphoma. However, the role and underlying mechanisms of NEK2 in DLBCL have seldom been discussed. In this study, we identified that NEK2 is upregulated in DLBCL compared to normal lymphoid tissues, and overexpression of NEK2 predicted a worse prognosis of DLBCL patients. Gene set enrichment analysis indicates that NEK2 might participate in regulating glycolysis. Knockdown of NEK2 inhibited growth and glycolysis of DLBCL cells. The interaction between NEK2 and PKM2 was discovered by tandem affinity purification and then was confirmed by immunofluorescence staining, coimmunoprecipitation, and immunoprecipitation. NEK2 bounds to PKM2 and regulates PKM2 abundance via phosphorylation, which increases PKM2 stability. The xenograft tumor model checks the influence of NEK2 on tumor growth in vivo. Thus, NEK2 could be the novel biomarker and target of DLBCL, which remarkably ameliorates the diagnosis and treatment of DLBCL.

scholarly journals Identification of hub genes associated with the pathogenesis of diffuse large B-cell lymphoma subtype one characterized by host response via integrated bioinformatic analyses

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10269
Author(s):  
Lingna Zhou ◽  
Liya Ding ◽  
Yuqi Gong ◽  
Jing Zhao ◽  
Gong Xin ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Host Response ◽  
Cell Lymphoma ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Hub Genes ◽  
Hub Gene ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Background Host response diffuse large B-cell lymphoma (HR DLBCL) shares features of histologically defined T-cell/histiocyte-rich B-cell lymphoma, including fewer genetic abnormalities, frequent splenic and bone marrow involvement, and younger age at presentation. HR DLBCL is inherently less responsive to the standard treatment for DLBCL. Moreover, the mechanism of infiltration of HR DLBCL with preexisting abundant T-cells and dendritic cells is unknown, and their associated underlying immune responses incompletely defined. Here, hub genes and pathogenesis associated with HR DLBCL were explored to reveal molecular mechanisms and treatment targets. Methods Differentially expressed genes were identified in three datasets (GSE25638, GSE44337, GSE56315). The expression profile of the genes in the GSE53786 dataset was used to constructed a co-expression network. Protein-protein interactions analysis in the modules of interest identified candidate hub genes. Then screening of real hub genes was carried out by survival analysis within the GSE53786 and GSE10846 datasets. Expression of hub genes was validated in the Gene expression profiling interactive analysis, Oncomine databases and human tissue specimens. Functional enrichment analysis and Gene set enrichment analysis were utilized to investigate the potential mechanisms. Tumor Immune Estimation Resource and The Cancer Genome Atlas were used to mine the association of the hub gene with tumor immunity, potential upstream regulators were predicted using bioinformatics tools. Results A total of 274 common differentially expressed genes were identified. Within the key module, we identified CXCL10 as a real hub gene. The validation of upregulated expression level of CXCL10 was consistent with our study. CXCL10 might have a regulatory effect on tumor immunity. The predicted miRNA (hsa-mir-6849-3p) and transcription factor (IRF9) might regulate gene expression in the hub module.

scholarly journals Deciphering genes associated with diffuse large B-cell lymphoma with lymphomatous effusions: A mutational accumulation scoring approach

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sina Abdollahi ◽  
Seyedeh Zahra Dehghanian ◽  
Liang-Yi Hung ◽  
Shiang-Jie Yang ◽  
Dao-Peng Chen ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
Stage Iv ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Clinical Samples ◽  
High Accumulation ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Introduction Earlier studies have shown that lymphomatous effusions in patients with diffuse large B-cell lymphoma (DLBCL) are associated with a very poor prognosis, even worse than for non-effusion-associated patients with stage IV disease. We hypothesized that certain genetic abnormalities were associated with lymphomatous effusions, which would help to identify related pathways, oncogenic mechanisms, and therapeutic targets. Methods We compared whole-exome sequencing on DLBCL samples involving solid organs (n = 22) and involving effusions (n = 9). We designed a mutational accumulation-based approach to score each gene and used mutation interpreters to identify candidate pathogenic genes associated with lymphomatous effusions. Moreover, we performed gene-set enrichment analysis from a microarray comparison of effusion-associated versus non-effusion-associated DLBCL cases to extract the related pathways. Results We found that genes involved in identified pathways or with high accumulation scores in the effusion-based DLBCL cases were associated with migration/invasion. We validated expression of 8 selected genes in DLBCL cell lines and clinical samples: MUC4, SLC35G6, TP53BP2, ARAP3, IL13RA1, PDIA4, HDAC1 and MDM2, and validated expression of 3 proteins (MUC4, HDAC1 and MDM2) in an independent cohort of DLBCL cases with (n = 31) and without (n = 20) lymphomatous effusions. We found that overexpression of HDAC1 and MDM2 correlated with the presence of lymphomatous effusions, and HDAC1 overexpression was associated with the poorest prognosis.  Conclusion Our findings suggest that DLBCL associated with lymphomatous effusions may be associated mechanistically with TP53-MDM2 pathway and HDAC-related chromatin remodeling mechanisms.

scholarly journals Proteomic-Based Analysis of Hypoxia- and Physioxia-Responsive Proteins and Pathways in Diffuse Large B-Cell Lymphoma

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 2025
Author(s):  
Kamila Duś-Szachniewicz ◽  
Katarzyna Gdesz-Birula ◽  
Krzysztof Zduniak ◽  
Jacek R. Wiśniewski
Keyword(s):  
B Cell ◽  
Cell Line ◽  
Cell Lymphoma ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Pathway Enrichment Analysis ◽  
Large B Cell Lymphoma ◽  
Long Time ◽  
Proteome Changes ◽  
Large B Cell

Hypoxia is a common feature in most tumors, including hematological malignancies. There is a lack of studies on hypoxia- and physioxia-induced global proteome changes in lymphoma. Here, we sought to explore how the proteome of diffuse large B-cell lymphoma (DLBCL) changes when cells are exposed to acute hypoxic stress (1% of O2) and physioxia (5% of O2) for a long-time. A total of 8239 proteins were identified by LC–MS/MS, of which 718, 513, and 486 had significant changes, in abundance, in the Ri-1, U2904, and U2932 cell lines, respectively. We observed that changes in B-NHL proteome profiles induced by hypoxia and physioxia were quantitatively similar in each cell line; however, differentially abundant proteins (DAPs) were specific to a certain cell line. A significant downregulation of several ribosome proteins indicated a translational inhibition of new ribosome protein synthesis in hypoxia, what was confirmed in a pathway enrichment analysis. In addition, downregulated proteins highlighted the altered cell cycle, metabolism, and interferon signaling. As expected, the enrichment of upregulated proteins revealed terms related to metabolism, HIF1 signaling, and response to oxidative stress. In accordance to our results, physioxia induced weaker changes in the protein abundance when compared to those induced by hypoxia. Our data provide new evidence for understanding mechanisms by which DLBCL cells respond to a variable oxygen level. Furthermore, this study reveals multiple hypoxia-responsive proteins showing an altered abundance in hypoxic and physioxic DLBCL. It remains to be investigated whether changes in the proteomes of DLBCL under normoxia and physioxia have functional consequences on lymphoma development and progression.

scholarly journals Identification and Validation of a Prognostic Gene Signature for Diffuse Large B-Cell Lymphoma Based on Tumor Microenvironment-Related Genes

2021 ◽  
Vol 11 ◽  
Author(s):  
Tao Pan ◽  
Yizi He ◽  
Huan Chen ◽  
Junfei Pei ◽  
Yajun Li ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
B Cell ◽  
Cox Regression ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Large B Cell Lymphoma ◽  
Prognostic Prediction ◽  
Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is an extremely heterogeneous tumor entity, which makes prognostic prediction challenging. The tumor microenvironment (TME) has a crucial role in fostering and restraining tumor development. Consequently, we performed a systematic investigation of the TME and genetic factors associated with DLBCL to identify prognostic biomarkers for DLBCL. Data for a total of 1,084 DLBCL patients from the Gene Expression Omnibus database were included in this study, and patients were divided into a training group, an internal validation group, and two external validation groups. We calculated the abundance of immune–stromal components of DLBCL and found that they were related to tumor prognosis and progression. Then, differentially expressed genes were obtained based on immune and stromal scores, and prognostic TME‐related genes were further identified using a protein–protein interaction network and univariate Cox regression analysis. These genes were analyzed by the least absolute shrinkage and selection operator Cox regression model to establish a seven-gene signature, comprising TIMP2, QKI, LCP2, LAMP2, ITGAM, CSF3R, and AAK1. The signature was shown to have critical prognostic value in the training and validation sets and was also confirmed to be an independent prognostic factor. Subgroup analysis also indicated the robust prognostic ability of the signature. A nomogram integrating the seven-gene signature and components of the International Prognostic Index was shown to have value for prognostic prediction. Gene set enrichment analysis between risk groups demonstrated that immune-related pathways were enriched in the low-risk group. In conclusion, a novel and reliable TME relevant gene signature was proposed and shown to be capable of predicting the survival of DLBCL patients at high risk of poor survival.

scholarly journals A prognostic gene model of immune cell infiltration in diffuse large B-cell lymphoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9658
Author(s):  
Hao Zhou ◽  
Chang Zheng ◽  
De-Sheng Huang
Keyword(s):  
Overall Survival ◽  
B Cell ◽  
Immune Cells ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Penalized Regression ◽  
Gene Set Enrichment Analysis ◽  
Validation Dataset ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Background Immune cells in the tumor microenvironment are an important prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, information on the heterogeneity and risk stratification of these cells is limited. We sought to develop a novel immune model to evaluate the prognostic intra-tumoral immune landscape of patients with DLBCL. Methods The ESTIMATE and CIBERSORT algorithms were used to estimate the numbers of 22 infiltrating immune cells based on the gene expression profiles of 229 patients with DLBCL who were recruited from a public database. The least absolute shrinkage and selection operator (Lasso) penalized regression analyses and nomogram model were used to construct and evaluate the prognostic immunoscore (PIS) model for overall survival prediction. An immune gene prognostic score (IGPS) was generated by Gene Set Enrichment Analysis (GSEA) and Cox regression analysis was and validated in an independent NCBI GEO dataset (GSE10846). Results A higher proportion of activated natural killer cells was associated with a poor outcome. A total of five immune cells were selected in the Lasso model and DLBCL patients with high PIS showed a poor prognosis (hazard ratio (HR) 2.16; 95% CI [1.33–3.50]; P = 0.002). Differences in immunoscores and their related outcomes were attributed to eight specific immune genes involved in the cytokine–cytokine receptor interaction and chemokine signaling pathways. The IGPS based on a weighted formula of eight genes is an independent prognostic factor (HR: 2.14, 95% CI [1.40–3.28]), with high specificity and sensitivity in the validation dataset. Conclusions Our findings showed that a PIS model based on immune cells is associated with the prognosis of DLBCL. We developed a novel immune-related gene-signature model associated with the PIS model and enhanced the prognostic functionality for the prediction of overall survival in patients with DLBCL.

scholarly journals Identification of a 14-Gene Prognostic Signature for Diffuse Large B Cell Lymphoma (DLBCL)

2021 ◽  
Vol 12 ◽  
Author(s):  
Pengcheng Feng ◽  
Hongxia Li ◽  
Jinhong Pei ◽  
Yan Huang ◽  
Guixia Li
Keyword(s):  
High Risk ◽  
B Cell ◽  
Cell Lymphoma ◽  
Killer Cell ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Although immunotherapy is a potential strategy to resist cancers, due to the inadequate acknowledge, this treatment is not always effective for diffuse large B cell lymphoma (DLBCL) patients. Based on the current situation, it is critical to systematically investigate the immune pattern. According to the result of univariate and multivariate cox proportional hazards, LASSO regression and Kaplan-Meier survival analysis on immune-related genes (IRGs), a prognostic signature, containing 14 IRGs (AQP9, LMBR1L, FGF20, TANK, CRP, ORM1, JAK1, BACH2, MTCP1, IFITM1, TNFSF10, FGF12, RFX5, and LAP3), was built. This model was validated by external data, and performed well. DLBCL patients were divided into low- and high-risk groups, according to risk scores from risk formula. The results of CIBERSORT showed that different immune status and infiltration pattern were observed in these two groups. Gene set enrichment analysis (GSEA) indicated 12 signaling pathways were significantly enriched in the high-risk group, such as natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathway, and so on. In summary, 14 clinically significant IRGs were screened to build a risk score formula. This formula was an accurate tool to provide a certain basis for the treatment of DLBCL patients.

scholarly journals Comprehensive Characterization of Cachexia-Inducing Factors in Diffuse Large B-Cell Lymphoma Reveals a Molecular Subtype and a Prognosis-Related Signature

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhixing Kuang ◽  
Xun Li ◽  
Rongqiang Liu ◽  
Shaoxing Chen ◽  
Jiannan Tu
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Molecular Subtypes ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Large B Cell Lymphoma ◽  
Inducing Factors ◽  
Large B Cell

BackgroundCachexia is defined as an involuntary decrease in body weight, which can increase the risk of death in cancer patients and reduce the quality of life. Cachexia-inducing factors (CIFs) have been reported in colorectal cancer and pancreatic adenocarcinoma, but their value in diffuse large B-cell lymphoma (DLBCL) requires further genetic research.MethodsWe used gene expression data from Gene Expression Omnibus to evaluate the expression landscape of 25 known CIFs in DLBCL patients and compared them with normal lymphoma tissues from two cohorts [GSE56315 (n = 88) and GSE12195 (n = 136)]. The mutational status of CIFs were also evaluated in The Cancer Genome Atlas database. Based on the expression profiles of 25 CIFs, a single exploratory dataset which was merged by the datasets of GSE10846 (n = 420) and GSE31312 (n = 498) were divided into two molecular subtypes by using the method of consensus clustering. Immune microenvironment between different subtypes were assessed via single-sample gene set enrichment analysis and the CIBERSORT algorithm. The treatment response of commonly used chemotherapeutic drugs was predicted and gene set variation analysis was utilized to reveal the divergence in activated pathways for distinct subtypes. A risk signature was derived by univariate Cox regression and LASSO regression in the merged dataset (n = 882), and two independent cohorts [GSE87371 (n = 221) and GSE32918 (n = 244)] were used for validation, respectively.ResultsClustering analysis with CIFs further divided the cases into two molecular subtypes (cluster A and cluster B) associated with distinct prognosis, immunological landscape, chemosensitivity, and biological process. A risk-prognostic signature based on CCL2, CSF2, IL15, IL17A, IL4, TGFA, and TNFSF10 for DLBCL was developed, and significant differences in overall survival analysis were found between the low- and high-risk groups in the training dataset and another two independent validation datasets. Multivariate regression showed that the risk signature was an independently prognostic factor in contrast to other clinical characteristics.ConclusionThis study demonstrated that CIFs further contribute to the observed heterogeneity of DLBCL, and molecular classification and a risk signature based on CIFs are both promising tools for prognostic stratification, which may provide important clues for precision medicine and tumor-targeted therapy.

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