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Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia.

ABSTRACT: Background:Molecular analysis is a promising source of clinically useful prognostic biomarkers. The aim of this investigation was to identify prognostic biomarkers for patients with acute myeloid leukemia (AML) by using the gene expression profile dataset from public database. Methods:The gene expression profile dataset and corresponding overall survival (OS) information of three cohorts of AML patients from GSE12417 and The Cancer Genome Atlas AML project (TCGA-LAML) were included in the present study. Prognostic gene screening was performed by using a survival package, whereas time-dependent receiver operating characteristic (ROC) curve analysis was performed using the survivalROC package. Results:In the three cohorts, 11 genes were identified that were significantly associated with AML OS. A linear prognostic model of the 11 genes was constructed and weighted by regression coefficient (?) from the multivariate Cox regression analyses of GSE12417 HG-U133A cohort to divide patients into high- and low-risk groups. GSE12417 HG-U133 plus 2.0 and TCGA-LAML were validation cohorts. Patients assigned to the high-risk group exhibited poor OS compared to patients in the low-risk group. The 11-gene signature is a prognostic marker of AML and demonstrates good performance for predicting 1-, 3-, and 5-year OS as evaluated by survivalROC in the three cohorts. Conclusion:Our study has identified an mRNA signature including 11 genes, which may serve as a potential prognostic marker of AML.

SUBMITTER: Huang R

PROVIDER: S-EPMC5679677 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia.

Huang Rui R Liao Xiwen X Li Qiaochuan Q

OncoTargets and therapy 20171102

<h4>Background</h4>Molecular analysis is a promising source of clinically useful prognostic biomarkers. The aim of this investigation was to identify prognostic biomarkers for patients with acute myeloid leukemia (AML) by using the gene expression profile dataset from public database.<h4>Methods</h4>The gene expression profile dataset and corresponding overall survival (OS) information of three cohorts of AML patients from GSE12417 and The Cancer Genome Atlas AML project (TCGA-LAML) were include ...[more]

PMID: 29138577

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Project description:Dysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated.LncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis.Four lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR?=?2.718, 95 % CI?=?1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (S?2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM.Our results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.

Project description:BackgroundMultiple studies have reported the prognostic impact of DNA methylation changes in acute myeloid leukemia (AML). However, these epigenetic markers have not been thoroughly validated and therefore are still not considered in clinical practice. Hence, we aimed to independently verify results of selected studies describing the relationship between DNA methylation of specific genes and their prognostic potential in predicting overall survival (OS) and event-free survival (EFS).ResultsFourteen studies (published 2011-2019) comprising of 27 genes were subjected to validation by a custom NGS-based sequencing panel in 178 newly diagnosed non-M3 AML patients treated by 3 + 7 induction regimen. The results were considered as successfully validated, if both the log-rank test and multivariate Cox regression analysis had a p-value ≤ 0.05. The predictive role of DNA methylation was confirmed for three studies comprising of four genes: CEBPA (OS: p = 0.02; EFS: p = 0.03), PBX3 (EFS: p = 0.01), LZTS2 (OS: p = 0.05; EFS: p = 0.0003), and NR6A1 (OS: p = 0.004; EFS: p = 0.0003). For all of these genes, higher methylation was an indicator of longer survival. Concurrent higher methylation of both LZTS2 and NR6A1 was highly significant for survival in cytogenetically normal (CN) AML group (OS: p < 0.0001; EFS: p < 0.0001) as well as for the whole AML cohort (OS: p = 0.01; EFS < 0.0001). In contrast, for two studies reporting the poor prognostic effect of higher GPX3 and DLX4 methylation, we found the exact opposite, again linking higher GPX3 (OS: p = 0.006; EFS: p < 0.0001) and DLX4 (OS: p = 0.03; EFS = 0.03) methylation to a favorable treatment outcome. Individual gene significance levels refer to the outcomes of multivariate Cox regression analysis.ConclusionsOut of twenty-seven genes subjected to DNA methylation validation, a prognostic role was observed for six genes. Therefore, independent validation studies are necessary to reveal truly prognostic DNA methylation changes and to enable the introduction of these promising epigenetic markers into clinical practice.

Dataset Information

Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia.

Publications

Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia.

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