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

Authors Huang R, Liao XW, Li Q

Received 29 July 2017

Accepted for publication 4 October 2017

Published 2 November 2017 Volume 2017:10 Pages 5243—5254

DOI https://doi.org/10.2147/OTT.S147717

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 4

Editor who approved publication: Dr Ingrid Espinoza

Rui Huang,1,* Xiwen Liao,2,* Qiaochuan Li1

1Department of Hematology, 2Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China

*These authors contributed equally to this work

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 (β) 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.

Keywords: acute myeloid leukemia, prognosis, biomarker, GEO, TCGA

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