scholarly journals Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chao Ma ◽  
Haoyu Li

Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues ( P < 0.01 ). Survival analysis showed that patients with high APOE and CTSD were associated with better OS ( P < 0.01 ). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10269
Author(s):  
Lingna Zhou ◽  
Liya Ding ◽  
Yuqi Gong ◽  
Jing Zhao ◽  
Gong Xin ◽  
...  

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.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8843
Author(s):  
Dongmei Guo ◽  
Hongchun Wang ◽  
Li Sun ◽  
Shuang Liu ◽  
Shujing Du ◽  
...  

Purpose Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL. Methods The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein–protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan–Meier method and were compared using the log-rank test. Results Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05). Conclusions The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1548-1548
Author(s):  
Christoffer Hother ◽  
Ditte Reker ◽  
Konstantions Dimopoulos ◽  
Steen Knudsen ◽  
Thomas Jensen ◽  
...  

Abstract Abstract 1548 Introduction: The introduction of Rituximab as supplement to chemotherapy has significantly improved outcome in diffuse large B-cell lymphoma (DLBCL). Still, a fraction of patients are resistant or relapse shortly after treatment. Improved stratification of patients with DLBCL for standard immunochemotherapy or alternative treatment strategies is therefore urgently needed. Although DLBCL profiling based on mRNA expression may be helpful, this has not proven clinically efficient, and the prognostic value of immunohistochemical algorithms is controversial. In addition, novel therapeutic options are essential since the current alternative treatment modalities are often not curative. MicroRNAs (miRs) are particularly attractive molecules for clinical use since they are well conserved in formalin fixed paraffin embedded (FFPE) tissue, and novel data imply that they may be targeted directly in the patients. Materials and methods: RNA was extracted from diagnostic biopsies from DLBCL patients (n=97) treated uniformly with immunochemotherapy (R-CHOP n=80 or R-CHOEP n=17). GCB/non-GCB profiling was done by immunohistochemistry according to the Hans classification. MiR profiles were generated using Affymetrix microRNA version 1.0 arrays. Data analyses were performed using R/biocondutor and the webtool “SignS” that uses parallel computing for finding survival related genes and signatures from gene-expression datasets. Survival analysis was performed in R using the survival package. Univariate analysis was performed by comparing Kaplan-Meier survival estimates using Log-rank test. Cox proportional hazards regression model was used for multivariate analysis. Results and discussion: The median follow-up time for all patients was 3.4 years. The estimated 3-year over all survival probability was 82.8% (95% CI: 75.4%-90.9%). No difference in survivability was observed between the R-CHOP and the R-CHOEP treated cohort (P=0.145). High IPI (> 2) was significantly associated with inferior overall survival (OS, P = 0.038), but not progression free survival (PFS, P = 0.083). Univariate analysis showed that in this cohort the Hans classification was not prognostic (P=0.73; (52 GBC and 37 non-GCB subtypes; 8 NA)). Seven miRs were differentially regulated between GCB and non-GCB using a cutoff of P< 0.05. Five miRs were upregulated in non-GCB lymphomas: miR-625, miR-222, miR-221, miR-155 and miR-503, two were downregulated (miR-181a, miR-181b). For survival analysis, we initially applied a multivariate approach (Robust likelihood-based survival modeling, RBsurv), which identified a subset of miRs that significantly associates with poor survival. These include one upregulated miR, and four down regulated miRs. In order to obtain cross validated survival estimates, we applied three different algorithms; FCSM(SignS), TGD(SignS) and GLMboost(SignS) to the same sample set. These combined bioinformatic models identified a total of 17 deregulated miRs that significantly associate with survival. Among these, 9 are predicted by more that one algorithm, and interestingly, all 4 models identify a novel upregulated and potential oncogenic miR in patients treated by immunochemotherapy. When the cross-validated predictors were combined into a unified robust “miR-survival-predictor”, the performance is as good as, or even better, than the IPI. In addition, our model is a superior predictor of survival than the GCB/non-GCB classification according to Hans. Our data are currently being validated in a test set of 60 patients, and functional studies of the novel putative oncomiR are ongoing. Disclosures: No relevant conflicts of interest to declare.


2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Huang ◽  
Jinkun Lin ◽  
Surong Huang ◽  
Jianzhen Shen

Background: It has been verified that deficiency of Qi, a fundamental substance supporting daily activities according to the Traditional Chinese Medicine theory, is an important symptom of cancer. Qi-invigorating herbs can inhibit cancer development through promoting apoptosis and improving cancer microenvironment. In this study, we explored the potential mechanisms of Qi-invigorating herbs in diffuse large B cell lymphoma (DLBCL) through network pharmacology and in vitro experiment.Methods: Active ingredients of Qi-invigorating herbs were predicted from the Traditional Chinese Medicine Systems Pharmacology Database. Potential targets were obtained via the SwissTargetPrediction and STITCH databases. Target genes of DLBCL were obtained through the PubMed, the gene-disease associations and the Malacards databases. Overlapping genes between DLBCL and each Qi-invigorating herb were collected. Hub genes were subsequently screened via Cytoscape. The Gene Ontology and pathway enrichment analyses were performed using the DAVID database. Molecular docking was performed among active ingredients and hub genes. Hub genes linked with survival and tumor microenvironment were analyzed through the GEPIA 2.0 and TIMER 2.0 databases, respectively. Additionally, in vitro experiment was performed to verify the roles of common hub genes.Results: Through data mining, 14, 4, 22, 22, 35, 2, 36 genes were filtered as targets of Ginseng Radix et Rhizoma, Panacis Quinquefolii Radix, Codonopsis Radix, Pseudostellariae Radix, Astragali Radix, Dioscoreae Rhizoma, Glycyrrhizae Radix et Rhizoma for DLBCL treatment, respectively. Then besides Panacis Quinquefolii Radix and Dioscoreae Rhizoma, 1,14, 10, 14,13 hub genes were selected, respectively. Molecular docking studies indicated that active ingredients could stably bind to the pockets of hub proteins. CASP3, CDK1, AKT1 and MAPK3 were predicted as common hub genes. However, through experimental verification, only CASP3 was considered as the common target of Qi-invigorating herbs on DLBCL apoptosis. Furthermore, the TIMER2.0 database showed that Qi-invigorating herbs might act on DLBCL microenvironment through their target genes. Tumor-associated neutrophils may be main target cells of DLBCL treated by Qi-invigorating herbs.Conclusion: Our results support the effects of Qi-invigorating herbs on DLBCL. Hub genes and immune infiltrating cells provided the molecular basis for each Qi-invigorating herb acting on DLBCL.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12394
Author(s):  
Alice Charwudzi ◽  
Ye Meng ◽  
Linhui Hu ◽  
Chen Ding ◽  
Lianfang Pu ◽  
...  

Background Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined. Aim We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL. Methods We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset. Results We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes’ main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4+ T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment. Conclusion Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1427-1427
Author(s):  
Varsha Gupta ◽  
Vinit Singh ◽  
Ravneet Bajwa ◽  
Trishala Meghal ◽  
Shuvendu Sen ◽  
...  

Abstract Introduction Incidence of Non-Hodgkin lymphoma (NHL) has been increasing steadily. Diffuse large B Cell Lymphoma (DLBCL) constitutes 30% of all NHLs. It can present as nodal or extra nodal disease. Based on other primary sites of origin, extra nodal DLBCL (EN-DLBCL) may have a distinct clinical outcome. Apart from site of origin, factors including demographics, staging and presence of other primary malignancy may also affect the outcome in these patients. Purpose of this study is to identify prognostically distinct groups, to lay the foundation for future studies aiming at tailored treatment based on the site of involvement. Methods We identified patients in the Surveillance, Epidemiology and End Results (SEER) database who were diagnosed with DLBCL and EN-DLBCL from 2000 through 2016 based on the WHO ICD-O-3 classification. A total of 34565 EN-DLBCL patients were identified. Patients with unknown survival duration were excluded and accordingly 34515 patients were included for survival analysis. Survival analysis variables included gender, ethnicity, age of onset (Early ≤ 50, late &gt;50 years), site of origin, staging of the tumor as early-stage (stage 1 and stage 2) or late-stage tumors (stage 3 and stage 4), and history of prior malignancy (first primary tumor or second/later primary tumor). There were 267 sites based on primary site code in the SEER database, which were grouped based on organ systems. Survival analysis was done using a cox-proportional hazard regression model. Site specific survival curves were estimated by the Kaplan-Meier method and were compared using the log-rank test for bone marrow, heart/mediastinum and nervous system, as these sites had worse outcomes with gastrointestinal tract as reference (Figure - 1). Site specific median overall survival was reported with 95% confidence interval. Results The percentage of EN-DLBCL of all DLBCL is 34.35%. Demographically, EN-DLBCL was most seen in Male (54.73%) and Non-Hispanic white (71.06%). In terms of clinical characteristics, patients with EN-DLBCL mostly had late age of onset, i.e. &gt;/= 50 years (82.07%), Stage I disease (42.96%) and presentation as first primary cancer (81.46%). Higher risk of mortality was seen in Hispanic population (HR 1.36, 1.292-1.422) with non-Hispanic white as reference, late age of onset (HR 1.12, 1.078-1.167), late stage (III and IV) of presentation (HR 1.07, 1.026-1.118) and with history of any other malignancy (HR 1.18, 1.119-1.238) (Table - 1). Among EN-DLBCL, gastrointestinal tract (27.79%), nervous system (13.61%), head and neck (12.79%) and skin and soft tissue (9.71%) were the most common sites of origin. In the survival analysis, the risk of all-cause mortality was found to be higher in individuals with involvement of bone marrow (HR 2.34, 2.086-2.633), heart and mediastinum (HR 1.83, 1.693-1.978) and nervous system (HR 1.58, 1.482-1.691), with GI/Alimentary tract as reference (Table - 2). Conclusion Primary site of disease is an important prognostic factor for patients with EN-DLBCL after adjusting for confounders like age, sex, race and stage. Possible explanations to our findings include degree of vital organs affected, treatment modalities involved, risk of central nervous system (CNS) recurrence, immune status, and molecular biology. Unlike the typical DLBCL, Primary mediastinal/thymic large B cell lymphoma and primary CNS lymphoma has already been recognized as a separate entity in WHO classification, because of its specific behavior. Based on our population-based study, we conclude that there is a need for separate guidelines and tailored treatment for other primary extra nodal sites as well. Our study showed a worse outcome in Hispanic population when compared to other races. There is a need for further prospective studies to evaluate racial association with existing prognostic factors. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Xiao ◽  
Xuemei Wang ◽  
Haitao Bai

Objective. Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive malignant tumor, accounting for 30-40% of non-Hodgkin’s lymphoma. Our aim was to construct novel prognostic models of candidate genes based on clinical features. Methods. RNA-seq and clinical data of DLBCL were retrieved from TCGA database. Coexpression modules were constructed by WGCNA. Then, we investigated the interactions between modules and clinical features. By overall survival analysis, prognostic candidate genes from modules of interest were identified. A coexpression network of prognostic candidate genes was then constructed through WGCNA. GEPIA was used to analyze the expression of a candidate gene between DLBCL and normal samples. Results. 19 coexpression modules were constructed by 12813 genes from 52 DLBCL samples. The number of genes in modules ranged from 34 to 5457. We found that the purple module was significantly related with histological type (p value = 1e-04). Overall survival analysis revealed that MAFA-AS1, hsa-mir-338, and hsa-mir-891a were related with prognosis of DLBCL (p value = 0.027, 0.039, and 0.022, respectively). A coexpression network was constructed for the three prognostic genes. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA, we found that MAFA-AS1 showed low expression in DLBCL samples (p<0.01). Conclusion. We constructed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers and therapeutic targets of DLBCL.


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