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06 July 2019 : Laboratory Research  

A 10-Long Non-Coding RNA-Based Expression Signature as a Potential Biomarker for Prognosis of Acute Myeloid Leukemia

Ping Tang1AE, Menghan Xie1BE, Yan Wei1B, Xinsheng Xie1C, Dandan Chen1C, Zhongxing Jiang1AE*

DOI: 10.12659/MSM.917182

Med Sci Monit 2019; 25:4999-5004

Abstract

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous form of cancer, and it is one of the dominant causes of malignancy-related mortality in patients younger than 35 years old. Therefore, the treatment must be selected based on risk stratification. However, the methods to predict the clinical outcomes of AML are insufficient. Long non-coding RNAs (lncRNAs) are unable or barely able to code for proteins and have attracted remarkable interest because of their involvement in malignancies. Previous studies have proven that some lncRNAs contribute to the development and clinical outcome of AML. Our study constructed a risk stratification system for AML that will facilitate the prediction of clinical outcomes.

MATERIAL AND METHODS: We acquired the expression profiles of lncRNAs from the TCGA database to examine their role in the clinical outcomes of AML. We designed and validated a prognostic signature-based risk score system using a sample splitting approach and Cox regression analysis to elucidate the relationship between the clinical outcomes of AML and lncRNAs.

RESULTS: We selected 10 lncRNAs to predict the clinical outcome of AML and were able to successfully predict the survival of patients with AML using this 10-lncRNA expression signature.

CONCLUSIONS: We developed a 10-lncRNA expression signature to predict the clinical outcome of AML. This approach demonstrates remarkable prognostic and therapeutic potential for AML.

Keywords: Leukemia, Myeloid, Acute, Mortality, Prognosis, Biomarkers, Tumor, Gene Expression Regulation, Leukemic, Kaplan-Meier Estimate, RNA, Long Noncoding, Risk Factors, Treatment Outcome

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Editorial

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750