Identification of an individualized RNA binding protein-based prognostic signature for diffuse large B-cell lymphoma.
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ABSTRACT:
SUBMITTER: Xie Y
PROVIDER: S-EPMC8026940 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.
| S-EPMC8278125 | biostudies-literature
Project description: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.
| S-EPMC7902938 | biostudies-literature
Project description: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.
| S-EPMC7938316 | biostudies-literature
Project description:Diffuse large B-cell lymphoma (DLBCL) presents a great clinical challenge and has a poor prognosis, with immune-related genes playing a crucial role. We aimed to develop an immune-related prognostic signature for improving prognosis prediction in DLBCL. Samples from the GSE31312 dataset were randomly allocated to discovery and internal validation cohorts. Univariate Cox, random forest, LASSO regression and multivariate Cox analyses were utilized to develop a prognostic signature, which was verified in the internal validation cohort, entire validation cohort and external validation cohort (GSE10846). The tumor microenvironment was investigated using the CIBERSORT and ESTIMATE tools. Gene set enrichment analysis (GSEA) was further applied to analyze the entire GSE31312 cohort. We identified four immune-related genes (CD48, IL1RL, PSDM3, RXFP3) significantly associated with overall survival. Based on discovery and validation cohort analyses, this four-gene signature could classify patients into high- and low-risk groups, with significantly different prognoses. Activated memory CD4 T cells and activated dendritic cells were significantly decreased in the high-risk group, and these patients had lower immune scores. GSEA revealed enrichment of signaling pathways, such as T cell receptor, antigen receptor-mediated, antigen processing and presentation of peptide antigen via MHC class I, in the low-risk group. In conclusion, a robust signature based on four immune-related genes was successfully constructed for predicting prognosis in DLBCL patients.
| S-EPMC8544299 | biostudies-literature
Project description:BackgroundS100A8, a calcium-binding protein belonging to the S100 family, is involved in immune responses and multiple tumor pathogens. Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of B-cell lymphoma and remains incurable in 40% of patients. However, the role of S100A8 and its regulation of the immune response in DLBCL remain unclear.MethodsThe differential expression of S100A8 was identified via the GEO and TCGA databases. The prognostic role of S100A8 in DLBCL was calculated using the Kaplan-Meier curve. The function enrichment of differentially expressed genes (DEGs) was explored through GO, KEGG, GSEA, and PPI analysis. In our cohort, the expression of S100A8 was verified. Meanwhile, the biological function of S100A8 was applied after the inhibition of S100A8 in an in vitro experiment. The association between S100A8 and immune cell infiltration and treatment response in DLBCL was analyzed.ResultsS100A8 was significantly overexpressed and related to a poor prognosis in DLBCL patients. Function enrichment analysis revealed that DEGs were mainly enriched in the IL-17 signaling pathway. Our cohort also verified this point. In vitro experiments suggested that inhibition of S100A8 should promote cell apoptosis and suppress tumor growth. Single-cell RNA sequence analysis indicated that S100A8 might be associated with features of the tumor microenvironment (TME), and immune infiltration analyses discovered that S100A8 expression was involved in TME. In terms of drug screening, we predicted that many drugs were associated with preferable sensitivity.ConclusionElevated S100A8 expression is associated with a poor prognosis and immune infiltration in DLBCL. Inhibition of S100A8 could promote cell apoptosis and suppress tumor growth. Meanwhile, S100A8 has the potential to be a promising immunotherapeutic target for patients with DLBCL.
| S-EPMC10867108 | biostudies-literature
Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.MethodIn this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.ResultsWe constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.ConclusionIn summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.
| S-EPMC9048048 | biostudies-literature
Project description:BackgroundDiffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.MethodsGene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).ResultsConsensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.ConclusionsThe molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.
| S-EPMC9036805 | 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:Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts.
| S-EPMC4644669 | biostudies-literature
Project description:BackgroundThe International Prognostic Index (IPI) is widely used to discriminate the prognosis of patients with diffuse large B-cell lymphoma (DLBCL). However, there is a significant need to identify novel valuable biomarkers in the context of targeted therapy, such as immune checkpoint blockade (ICB).MethodsGene expression data and clinical DLBCL information were obtained from The Cancer Genome Atlas and Gene Expression Omnibus datasets. A total of 371 immune-related genes in DLBCL patients associated with different IPI risk groups were identified by weighted gene co-expression network analysis, and eight genes were selected to construct an IPI-based immune prognostic model (IPI-IPM). Subsequently, we analyzed the somatic mutation and transcription profiles of the IPI-IPM subgroups as well as the potential clinical response to immune checkpoint blockade (ICB) in IPI-IPM subgroups.ResultsThe IPI-IPM was constructed based on the expression of CMBL, TLCD3B, SYNDIG1, ESM1, EPHA3, HUNK, PTX3, and IL12A, where high-risk patients had worse overall survival than low-risk patients, consistent with the results in the independent validation cohorts. The comprehensive results showed that high IPI-IPM risk scores were correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, and upregulation of inhibitory immune checkpoints, including PD-L1, BTLA, and SIGLEC7, indicating a greater potential response to ICB therapy.ConclusionThe IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms of tumor progression and sheds light on the development of immunotherapy for DLBCL.
| S-EPMC8569825 | biostudies-literature