Dataset Information

A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma.

ABSTRACT: Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GEO) database. In the discovery series of 207 patients, we identified a set of six lncRNAs that was significantly associated with patients' overall survival (OS) using univariate Cox regression analysis. The six prognostic lncRNAs were combined to form an expression-based six-lncRNA signature which classified patients of the discovery series into the high-risk group and low-risk group with significantly different survival outcome (HR?=?2.31, 95% CI?=?1.8 to 2.965, p?

SUBMITTER: Sun J

PROVIDER: S-EPMC4904406 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma.

Sun Jie J Cheng Liang L Shi Hongbo H Zhang Zhaoyue Z Zhao Hengqiang H Wang Zhenzhen Z Zhou Meng M

Scientific reports 20160613

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology and have been implicated in the development and progression of cancers. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear and needs to be systematically investigated. In this study, we obtained and analyzed lncRNA expression profiles in three cohorts of 1043 DLBCL patients by repurposing the publicly available microarray datasets from the Gene Expression Omnibus (GE ...[more]

PMID: 27292966

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.

Dataset Information

A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma.

Publications

A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure