Dataset Information

Immunological microenvironment gene expression in patients with diffuse large B cell non Hodgkin lymphoma.

ABSTRACT: Background:Non Hodgkin lymphoma (NHL) is one of the immune system cancers. The occurrence and progression of malignant lymphomas depends on cellular pathways deregulation. Understanding the relationship between the immune system at the genetic level and malignant transformation is critical to reach its etiology. Objective:The aim of this work is to evaluate the expression of five immune related genes (PD-1, FOXP3, GrA, GrB and CD11c) in patients with diffuse large B cell non Hodgkin lymphoma (DLBCL). Materials and methods:This study was conducted on fifty patients with DLBCL and fifty sex and age matched apparently healthy subjects. The participants were subjected to these laboratory investigations: complete blood count, serum lactate dehydrogenase and ?2microglobulin (?2M) levels and determination of PD-1, FOXP3, GrA, GrB and CD11c gene expressions. Results:The results of this study revealed that PD-1, FOXP3, GrA, GrB and CD11c gene expressions were significantly increased in DLBCL patients. Conclusion:Patients with DLBCL have variablePD-1, FOXP3,GrA, GrB and CD11cgene expressions levels, which are correlated with the overall survival (OS) indicating that they can be good predictors of outcome in these patients.

SUBMITTER: Elhelbawy NG

PROVIDER: S-EPMC6997500 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Immunological microenvironment gene expression in patients with diffuse large B cell non Hodgkin lymphoma.

Elhelbawy Nesreen G NG Nassar Adel A H AAH Eltorgoman Abd ElMonem A AEA Saber Safa M SM Badr Eman Ae EA

Biochemistry and biophysics reports 20200203

<h4>Background</h4>Non Hodgkin lymphoma (NHL) is one of the immune system cancers. The occurrence and progression of malignant lymphomas depends on cellular pathways deregulation. Understanding the relationship between the immune system at the genetic level and malignant transformation is critical to reach its etiology.<h4>Objective</h4>The aim of this work is to evaluate the expression of five immune related genes (PD-1, FOXP3, GrA, GrB and CD11c) in patients with diffuse large B cell non Hodgk ...[more]

PMID: 32025577

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Project description:Background: Diffuse large B-cell lymphoma (DLBCL) is a common aggressive B-cell non-Hodgkin lymphoma (B-NHL). While combined chemotherapy has improved the outcomes of DLBCL, it remains a highly detrimental disease. Pyroptosis, an inflammatory programmed cell death, is considered to have both tumor-promoting and tumor-suppressing effects. The role of pyroptosis in DLBCL has been gradually appreciated, but its value needs further investigation. Methods: We analyzed mutations and copy number variation (CNV) alterations of pyroptosis-related genes (PRGs) from The Cancer Genome Atlas (TCGA) cohort and evaluated the differences in expression in normal B cells and DLBCL patients in two Gene Expression Omnibus (GEO) datasets (GSE12195 and GSE56315). Based on the expression of 52 PRGs, we divided 421 DLBCL patients from the GSE31312 dataset into distinct clusters using consensus clustering. The Kaplan-Meier method was used to prognosis among the three clusters, and GSVA was used to explore differences in the biological functions. ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used to analyze the tumor immune microenvironment (TME) in different clusters. A risk score signature was developed using a univariate survival analysis and multivariate regression analysis, and the reliability and validity of the signature were verified. By combining the signature with clinical factors, a nomogram was established to predict the prognosis of DLBCL patients. The alluvial diagram and correlation matrix were used to explore the relationship between pyroptosis risk score, clinical features and TME. Results: A large proportion of PRGs are dysregulated in DLBCL and associated with the prognosis. We found three distinct pyroptosis-related clusters (cluster A, B, and C) that differed significantly with regard to the prognosis, biological process, clinical characteristics, chemotherapeutic drug sensitivity, and TME. Furthermore, we developed a risk score signature that effectively differentiates high and low-risk patients. The nomogram combining this signature with several clinical indicators showed an excellent ability to predict the prognosis of DCBCL patients. Conclusions: This work demonstrates that pyroptosis plays an important role in the diversity and complexity of the TME in DLBCL. The risk signature of pyroptosis is a promising predictive tool. A correct and comprehensive assessment of the mode of action of pyroptosis in individuals will help guide more effective treatment.

Project description:Although risk factors for diffuse large B-cell lymphoma (DLBCL) have been suggested, their independent effects, modification by sex, and association with anatomical sites are largely unknown.In a pooled analysis of 4667 cases and 22639 controls from 19 studies, we used stepwise logistic regression to identify the most parsimonious multivariate models for DLBCL overall, by sex, and for selected anatomical sites.DLBCL was associated with B-cell activating autoimmune diseases (odds ratio [OR] = 2.36, 95% confidence interval [CI] = 1.80 to 3.09), hepatitis C virus seropositivity (OR = 2.02, 95% CI = 1.47 to 2.76), family history of non-Hodgkin lymphoma (OR = 1.95, 95% CI = 1.54 to 2.47), higher young adult body mass index (OR = 1.58, 95% CI = 1.12 to 2.23, for 35+ vs 18.5 to 22.4 kg/m(2)), higher recreational sun exposure (OR = 0.78, 95% CI = 0.69 to 0.89), any atopic disorder (OR = 0.82, 95% CI = 0.76 to 0.89), and higher socioeconomic status (OR = 0.86, 95% CI = 0.79 to 0.94). Additional risk factors for women were occupation as field crop/vegetable farm worker (OR = 1.78, 95% CI = 1.22 to 2.60), hairdresser (OR = 1.65, 95% CI = 1.12 to 2.41), and seamstress/embroider (OR = 1.49, 95% CI = 1.13 to 1.97), low adult body mass index (OR = 0.46, 95% CI = 0.29 to 0.74, for <18.5 vs 18.5 to 22.4 kg/m(2)), hormone replacement therapy started age at least 50 years (OR = 0.68, 95% CI = 0.52 to 0.88), and oral contraceptive use before 1970 (OR = 0.78, 95% CI = 0.62 to 1.00); and for men were occupation as material handling equipment operator (OR = 1.58, 95% CI = 1.02 to 2.44), lifetime alcohol consumption (OR = 0.57, 95% CI = 0.44 to 0.75, for >400 kg vs nondrinker), and previous blood transfusion (OR = 0.69, 95% CI = 0.57 to 0.83). Autoimmune disease, atopy, and family history of non-Hodgkin lymphoma showed similar associations across selected anatomical sites, whereas smoking was associated with central nervous system, testicular and cutaneous DLBCLs; inflammatory bowel disease was associated with gastrointestinal DLBCL; and farming and hair dye use were associated with mediastinal DLBCL.Our results support a complex and multifactorial etiology for DLBCL with some variation in risk observed by sex and anatomical site.

Project description:BackgroundThirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.MethodsGene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.ResultsTwo hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).ConclusionOur results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Dataset Information

Immunological microenvironment gene expression in patients with diffuse large B cell non Hodgkin lymphoma.

Publications

Immunological microenvironment gene expression in patients with diffuse large B cell non Hodgkin lymphoma.

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