Prediction of survival of diffuse large B-cell lymphoma patients via the expression of three inflammatory genes.
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ABSTRACT:
SUBMITTER: Zhao S
PROVIDER: S-EPMC4971923 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Several gene-expression signatures predict survival in diffuse large B-cell lymphoma (DLBCL), but the lack of practical methods for genome-scale analysis has limited translation to clinical practice. We built and validated a simple model using one gene expressed by tumor cells and another expressed by host immune cells, assessing added prognostic value to the clinical International Prognostic Index (IPI). LIM domain only 2 (LMO2) was validated as an independent predictor of survival and the "germinal center B cell-like" subtype. Expression of tumor necrosis factor receptor superfamily member 9 (TNFRSF9) from the DLBCL microenvironment was the best gene in bivariate combination with LMO2. Study of TNFRSF9 tissue expression in 95 patients with DLBCL showed expression limited to infiltrating T cells. A model integrating these 2 genes was independent of "cell-of-origin" classification, "stromal signatures," IPI, and added to the predictive power of the IPI. A composite score integrating these genes with IPI performed well in 3 independent cohorts of 545 DLBCL patients, as well as in a simple assay of routine formalin-fixed specimens from a new validation cohort of 147 patients with DLBCL. We conclude that the measurement of a single gene expressed by tumor cells (LMO2) and a single gene expressed by the immune microenvironment (TNFRSF9) powerfully predicts overall survival in patients with DLBCL.
| S-EPMC3152499 | biostudies-literature
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.
| S-EPMC7579992 | biostudies-literature
Project description:Background/aimsDiffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.MethodsUtilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.ResultsDLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8+ T cell, CD4+ memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.ConclusionsThis study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.
| S-EPMC10866451 | biostudies-literature
Project description:BackgroundWith the improvement of clinical treatment outcomes in diffuse large B cell lymphoma (DLBCL), the high rate of relapse in DLBCL patients is still an established barrier, as the therapeutic strategy selection based on potential targets remains unsatisfactory. Therefore, there is an urgent need in further exploration of prognostic biomarkers so as to improve the prognosis of DLBCL.MethodsThe univariable and multivariable Cox regression models were employed to screen out gene signatures for DLBCL overall survival (OS) prediction. The differential expression analysis was used to identify representative genes in high-risk and low-risk groups, respectively, where student t test and fold change were implemented. The functional difference between the high-risk and low-risk groups was identified by the gene set enrichment analysis.ResultsWe conducted a systematic data analysis to screen the candidate genes significantly associated with OS of DLBCL in three NCBI Gene Expression Omnibus (GEO) datasets. To construct a prognostic model, five genes (CEBPA, CYP27A1, LST1, MREG, and TARP) were then screened and tested using the multivariable Cox model and the stepwise regression method. Kaplan-Meier curve confirmed the good predictive performance of this five-gene Cox model. Thereafter, the prognostic model and the expression levels of the five genes were validated by means of an independent dataset. High expression levels of these five genes were significantly associated with favorable prognosis in DLBCL, both in training and validation datasets. Additionally, further analysis revealed the independent value and superiority of this prognostic model in risk prediction. Functional enrichment analysis revealed some vital pathways responsible for unfavorable outcome and potential therapeutic targets in DLBCL.ConclusionWe developed a five-gene Cox model for the clinical outcome prediction of DLBCL patients. Meanwhile, potential drug selection using this model can help clinicians to improve the clinical practice for the benefit of patients.
| S-EPMC8220154 | biostudies-literature
Project description:OBJECTIVE:This study aimed to identify prognostic genes for diffuse large B-cell lymphoma (DLBCL), using bioinformatic methods. METHODS:Five gene expression data sets were downloaded from the Gene Expression Omnibus database. Significance analysis of microarrays algorithm was used to identify differentially expressed genes (DEGs) from two data sets. Functional enrichment analysis was performed for the DEGs with the Database for Annotation, Visualization and Integration Discovery (DAVID). Survival analysis was performed with the Kaplan-Meier method using function survfit from package survival of R for the other three data sets. Cox univariate regression analysis was used to further screen out prognostic genes. RESULTS:Thirty-one common DEGs were identified in the two data sets, mainly enriched in the regulation of lymphocyte activation, immune response, and interleukin-mediated signaling pathway. Combined with 47 DLBCL-related genes acquired by literature retrieval, a total of 78 potential prognostic genes were obtained. Cases from the other three data sets were used in hierarchical clustering, and the 78 genes could cluster them into several subtypes with significant differences in survival curves. Cox univariate regression analysis revealed 45, 33, and eleven prognostic genes in the three data sets, respectively. Five common prognostic genes were revealed, including LCP2, TNFRSF9, FUT8, IRF4, and TLE1, among which LCP2, FUT8, and TLE1 were novel prognostic genes. CONCLUSION:Five prognostic genes of DLBCL were identified in this study. They could not only be used for molecular subtyping of DLBCL but also be potential targets for treatment.
| S-EPMC4655963 | biostudies-literature
Project description:Gene expression profiling of DLBCL patient samples was performed to investigate, whether molecular gene expression signatures retain their prognostic significance in patients treated with chemotherapy plus Rituximab. The lymphnode, germinal center signature and a new angiogenesis signature were combined to a final multivariate model which defined quartile groups among Rituximab-CHOP-treated patients with distinct 3-year overall survival rates. Keywords: clinical history design
2008-11-28 | GSE10846 | GEO
Project description:JMJD3 (Jumonji domain containing-3), a histone H3 Lys27 (H3K27) demethylase, has been reported to be involved in the antigen-driven differentiation of germinal center B-cells. However, insight into the mechanism of JMJD3 in DLBCL (Diffuse large B-cell lymphoma) progression remains poorly understood. In this study, we investigated the subtype-specific JMJD3-dependent survival effects in DLBCL. Our data showed that in the ABC subtype, silencing-down of JMJD3 inhibited interferon regulatory factor 4 (IRF4) expression in a demethylase activity-dependent fashion. IRF4 reciprocally stimulated expression of JMJD3, forming a positive feedback loop that promoted survival in these cells. Accordingly, IRF4 expression was sufficient to rescue the pro-apoptotic effect of JMJD3 suppression in the ABC, but not in the GCB subtype. In contrast, ectopic overexpression of BCL-2 completely offset JMJD3-mediated survival in the GCB DLBCL cells. In vivo, treatment with siRNA to JMJD3 reduced tumor volume concordant with increased apoptosis in either subtype. This suggests it is a common target, though the distinctive signaling axes regulating DCBCL survival offer different strategic options for treating DLBCL subtypes.
| S-EPMC5045403 | biostudies-literature
Project description:Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention. golub-00095 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Tissue Sites: Lymphoid tissue Material Types: synthetic_DNA, synthetic_RNA, organism_part Cell Types: B-Lymphocyte Disease States: Diffuse large B-cell Lymphoma, Follicular Lymphoma
2015-05-15 | E-GEOD-68895 | biostudies-arrayexpress
Project description:Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.
2015-05-15 | GSE68895 | GEO
Project description:CD19 and CD20 are B cell-specific antigens whose expression is heterogeneous when analyzed by flow cytometry (FCM). We determined the association between CD20 expression and clinical outcome in patients with diffuse large B-cell lymphoma (DLBCL). The mean fluorescence intensity of CD20 and CD19 was determined by FCM, and the cytoplasmic expression of CD20 was determined by immunohistochemistry (IHC) on 272 diagnostic DLBCL samples. Exon 5 of the MS4A1 gene coding for the extracellular component of the CD20 antigen was sequenced in 15 samples. A total of 43 of 272 (16%) samples had reduced CD20 expression by FCM; of these, 35 (13%) had bright CD19 expression. The latter had a markedly inferior survival when treated with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or rituximab-CHOP (R-CHOP; median survival of 1.2 and 3.0 years vs not reached for the others, P < .001 and P = .001), independent of the International Prognostic Index. A total of 41 of 43 samples with reduced CD20 expression by FCM had strong staining for CD20 by IHC. There were no mutations in exon 5 of the MS4A1 gene to explain the discrepancy between FCM and IHC. CD20 and CD19 expression by FCM should be determined on all biopsies of patients with DLBCL because reduced CD20 expression cannot be reliably detected by IHC.
| S-EPMC2943836 | biostudies-other