Dataset Information

Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, with 30% to 40% of patients failing to be cured with available primary therapy. microRNAs (miRNAs) are RNA molecules that attenuate expression of their mRNA targets. To characterize the DLBCL miRNome, we sequenced miRNAs from 92 DLBCL and 15 benign centroblast fresh frozen samples and from 140 DLBCL formalin-fixed, paraffin-embedded tissue samples for validation.We identify known and candidate novel miRNAs, 25 of which are associated with survival independently of cell-of-origin and International Prognostic Index scores, which are established indicators of outcome. Of these 25 miRNAs, six miRNAs are significantly associated with survival in our validation cohort. Abundant expression of miR-28-5p, miR-214-5p, miR-339-3p, and miR-5586-5p is associated with superior outcome, while abundant expression of miR-324-5p and NOVELM00203M is associated with inferior outcome. Comparison of DLBCL miRNA-seq expression profiles with those from other cancer types identifies miRNAs that were more abundant in B-cell contexts. Unsupervised clustering of miRNAs identifies two clusters of patients that have distinct differences in their outcomes. Our integrative miRNA and mRNA expression analyses reveal that miRNAs increased in abundance in DLBCL appear to regulate the expression of genes involved in metabolism, cell cycle, and protein modification. Additionally, these miRNAs, including one candidate novel miRNA, miR-10393-3p, appear to target chromatin modification genes that are frequent targets of somatic mutation in non-Hodgkin lymphomas.Our comprehensive sequence analysis of the DLBCL miRNome identifies candidate novel miRNAs and miRNAs associated with survival, reinforces results from previous mutational analyses, and reveals regulatory networks of significance for lymphomagenesis.

SUBMITTER: Lim EL

PROVIDER: S-EPMC4308918 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

Lim Emilia L EL Trinh Diane L DL Scott David W DW Chu Andy A Krzywinski Martin M Zhao Yongjun Y Robertson A Gordon AG Mungall Andrew J AJ Schein Jacqueline J Boyle Merrill M Mottok Anja A Ennishi Daisuke D Johnson Nathalie A NA Steidl Christian C Connors Joseph M JM Morin Ryan D RD Gascoyne Randy D RD Marra Marco A MA

Genome biology 20150129

<h4>Background</h4>Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, with 30% to 40% of patients failing to be cured with available primary therapy. microRNAs (miRNAs) are RNA molecules that attenuate expression of their mRNA targets. To characterize the DLBCL miRNome, we sequenced miRNAs from 92 DLBCL and 15 benign centroblast fresh frozen samples and from 140 DLBCL formalin-fixed, paraffin-embedded tissue samples for validation.<h4>Results</h4>We identify known and candidate nove ...[more]

PMID: 25723320

Similar Datasets

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.

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.

Project description:Objectives/hypothesisThis study utilizes a large population national database to comprehensively analyze prognosticators and overall survival (OS) outcomes of varying treatment modalities in a large cohort of sinonasal diffuse large B-cell lymphoma (SN-DLBCL) patients.Study designRetrospective database study.MethodsThe National Cancer Database was queried for all SN-DLBCL cases diagnosed from 2004 to 2015. Kaplan-Meier log-rank test determined differences in OS based on clinical covariates. Cox proportional-hazards analysis was used to determine clinical and sociodemographic covariates predictive of mortality.ResultsA total of 2,073 SN-DLBCL patients were included, consisting of 48% female with a mean age of 66.0 ± 16.2 years. Overall, 82% of patients were Caucasian, 74% had early-stage disease, and 49% had primary tumors in the paranasal sinuses. Early-stage patients were more likely to receive multi-agent chemoradiotherapy compared to multi-agent chemotherapy alone (P < .001). Multivariable Cox proportional-hazards analysis revealed chemoradiotherapy to confer significantly greater OS improvements than chemotherapy alone (hazard ratio [HR]: 0.61; P < .001). However, subset analysis of late-stage patients demonstrated no significant differences in OS between these treatment modalities (P = .245). On multivariable analysis of chemotherapy patients treated post-2012, immunotherapy (HR = 0.51; P = .024) demonstrated significant OS benefits. However, subset analysis showed no significant advantage in OS with administering immunotherapy for late-stage patients (P = .326). Lastly, for all patients treated post-2012, those receiving immunotherapy had significantly improved OS compared to those not receiving immunotherapy (P < .001).ConclusionsTreatment protocol selection differs between early- and late-stage SN-DLBCL patients. Early-stage patients receiving chemotherapy may benefit from immunotherapy as part of their treatment paradigm.Level of evidence3 Laryngoscope, 131:E2727-E2735, 2021.

Dataset Information

Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

Publications

Comprehensive miRNA sequence analysis reveals survival differences in diffuse large B-cell lymphoma patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets