Dataset Information

Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy.

ABSTRACT: Acute myeloid leukemia (AML) is a heterogeneous disease, and survival signatures are urgently needed to better monitor treatment. MiRNAs displayed vital regulatory roles on target genes, which was necessary involved in the complex disease. We therefore examined the expression levels of miRNAs and genes to identify robust signatures for survival benefit analyses. First, we reconstructed subpathway graphs by embedding miRNA components that were derived from low-throughput miRNA-gene interactions. Then, we randomly divided the data sets from The Cancer Genome Atlas (TCGA) into training and testing sets, and further formed 100 subsets based on the training set. Using each subset, we identified survival-related miRNAs and genes, and identified survival subpathways based on the reconstructed subpathway graphs. After statistical analyses of these survival subpathways, the most robust subpathways with the top three ranks were identified, and risk scores were calculated based on these robust subpathways for AML patient prognoses. Among these robust subpathways, three representative subpathways, path: 05200_10 from Pathways in cancer, path: 04110_20 from Cell cycle, and path: 04510_8 from Focal adhesion, were significantly associated with patient survival in the TCGA training and testing sets based on subpathway risk scores. In conclusion, we performed integrated analyses of miRNAs and genes to identify robust prognostic subpathways, and calculated subpathway risk scores to characterize AML patient survival.

SUBMITTER: Chang H

PROVIDER: S-EPMC5865743 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy.

Chang Huijuan H Gao Qiuying Q Ding Wei W Qing Xueqin X

PloS one 20180323 3

Acute myeloid leukemia (AML) is a heterogeneous disease, and survival signatures are urgently needed to better monitor treatment. MiRNAs displayed vital regulatory roles on target genes, which was necessary involved in the complex disease. We therefore examined the expression levels of miRNAs and genes to identify robust signatures for survival benefit analyses. First, we reconstructed subpathway graphs by embedding miRNA components that were derived from low-throughput miRNA-gene interactions. ...[more]

PMID: 29570744

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Project description:AimAcute myeloid leukemia (AML) is a heterogeneous disorder with complex genetic basis and adverse prognosis. Cytogenetics risk, somatic mutations and gene expression profiles are important prognostic factors for AML patients. However, accurate stratification of patient prognosis remains an unsolved problem in AML. This study was to to develop a novel gene profile to accurately classify AML patients into subgroups with different survival probabilities.MethodsSurvival-related genes were determined by Kaplan-Meier survival analysis and multivariate analysis using the expression and clinical data of 405 AML patients from Oregon Health & Science University (OHSU) dataset and validated in The Cancer Genome Atlas (TCGA) database. Feature selection was performed by using the Least Absolute Shrinkage and Selection Operator (LASSO) method. With the LASSO model, a prognostic 85-gene score was established and compared with 2 known gene-expression risk scores. The stratification of AML patients was performed by unsupervised hierarchical clustering of 85 gene expression levels to identify clusters of AML patients with different survival probabilities.ResultsThe LASSO model comprising 85 genes was considered as the optimal model based on relatively high area under curve value (0.83) and the minimum mean squared error. The 85-gene score was associated with increased mortality in AML patients. Hierarchical clustering analysis of the 85 genes revealed 3 subgroups of AML patients in the OHSU dataset. The cluster1 AML patients were associated with more female cases, higher percent of bone marrow blast cells, 85-gene score, cytogenetics risk, more frequent FLT3-ITD, DNMT3A, NP1 mutations, less frequent TP53, RUNX1 mutations, poorer overall survival than cluster2 tumors. The 85-gene score had higher AUC (0.75) than the 5-gene risk score and LSC17 score (0.74 and 0.65).ConclusionsThe 85-gene score is superior to the 2 established prognostic gene signatures in the prediction of prognosis of AML patients.

Project description:Long non-coding RNAs (lncRNAs) are transcripts characterized by >200 nucleotides, without validated protein production. Previous studies have demonstrated that certain lncRNAs have a critical role in the initiation and development of acute myeloid leukemia (AML). In the present study, the subtype‑specific lncRNAs in AML was identified. Following the exclusion of the subtype‑specific lncRNAs, the prognostic value of lncRNAs was investigated and a three‑lncRNA expression‑based risk score [long intergenic non‑protein coding RNA 926, family with sequence similarity 30 member A and LRRC75A antisense RNA 1 (LRRC75A‑AS1)] was developed for AML patient prognosis prediction by analyzing the RNA‑seq data of AML patients from Therapeutically Available Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) projects. In the training set obtained from TARGET, patients were divided into poor and favorable prognosis groups by the median risk score. The prognostic effectiveness of this lncRNA risk score was confirmed in the validation set obtained from TCGA by the same cut‑off. Furthermore, the lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis. As further verification of the independent prognostic power of the lncRNA risk score, stratified analysis was performed by a cytogenetics risk group and revealed a consistent result. The prognostic predictive ability of the risk score was compared with the cytogenetics risk group by time‑dependent receiver operating characteristic curves analysis. It was revealed that the combination of the lncRNA risk score and cytogenetics risk group provided a higher prognostic value than a single prognostic factor. The present study also performed co‑expression analysis to predict the potential regulatory mechanisms of these lncRNAs in a cis/trans/competing endogenous RNA manner. The results suggested that LRRC75A‑AS1 was highly associated with the target genes of transcription factors tumor protein 53 and ETS variant 6. Overall, these results highlighted the use of the three‑lncRNA expression‑based risk score as a potential molecular biomarker to predict the prognosis in AML patients.

Dataset Information

Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy.

Publications

Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy.

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