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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:BackgroundDysregulated 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.MethodsLncRNA 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.ResultsFour 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 (Sβ2M), 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.ConclusionsOur 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:BackgroundAcute myeloid leukemia (AML) occurs frequently in the elderly, of whom the prognosis is dismal. Sarcopenia is a progressive and generalized skeletal muscle disorder associated with an increased possibility of adverse outcomes. This study aims to explore the prognostic value of sarcopenia in AML patients and develop a novel prognostic model.MethodsA total of 227 AML patients were enrolled. Body composition was assessed by bioelectrical impedance analysis before treatment. Sarcopenia was diagnosed by low muscle quantity. Cox proportional hazard regression model were applied to verify prognostic variables for overall survival (OS) and disease-free survival (DFS). A novel prognostic model of nomogram was developed and validated by 'R'.ResultsForty-one (18.1%) patients were defined as sarcopenia. The median age of the sarcopenic group was significantly greater than the non-sarcopenic group (median 70 vs. 64 years, P = 0.001). Sarcopenic patients showed significantly less height (P = 0.002), weight (P <0.001), Body Mass Index (P <0.001), Fat Mass (P = 0.017), Fat-free Mass (P <0.001), Appendicular Skeletal Muscle Mass (P <0.001), Skeletal Muscle Index (P <0.001), Fat-free Mass Index (P <0.001), and hemoglobin level (P = 0.025) than the non-sarcopenic ones. Patients in the sarcopenic group also showed a statistically shorter OS and DFS (median OS: 13.7 vs. 55.6 months, P = 0.003; median DFS: 12.5 months vs. not reached, P = 0.026). ELN high risk [Hazard Ratio (HR): 1.904, 95% Confidence Interval (CI): 1.018-3.562, P = 0.044), sarcopenia (HR: 1.887, 95% CI: 1.071-3.324, P = 0.028), and reduced-intensity regimens (HR: 3.765, 95% CI: 1.092-12.980, P = 0.036) were independent predictors for OS in multivariate analysis. A nomogram for predicting OS was constructed using the above three factors. The c index, calibration plots and decision curve analyses (DCA) showed better discrimination, calibration, and net benefits of the nomogram than the ELN model.ConclusionSarcopenia was common and had an inferior prognosis in AML and needs more attention in 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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