A 6-Membrane Protein Gene Score for Prognostic Prediction of Cytogenetically Normal Acute Myeloid Leukemia in Multiple Cohorts

Overview

Journal J Cancer

Specialty Oncology

Date 2020 Jan 2

PMID 31892991

Citations 10

Authors

Sheng-Yan Lin

Ya-Ru Miao

Fei-Fei Hu

Hui Hu

Qiong Zhang

Qiubai Li

Zhichao Chen

An-Yuan Guo

Affiliations

Soon will be listed here.

Abstract

Cytogenetically normal acute myeloid leukemia (CN-AML) is a large proportion of AMLs with diverse prognostic outcomes. Identifying membrane protein genes as prognostic factors to stratify CN-AML patients will be critical to improve their outcomes. This study aims to identify prognostic factors to stratify CN-AML patients to choose better treatments and improve their outcomes. CN-AML data were from TCGA cohort (n = 79) and four GEO datasets. We identified independent prognostic genes by Cox regression and Kaplan-Meier methods, and constructed linear regression model using LASSO algorithm. The prediction error curve was calculated using R package "pec". Based on independent prognostic membrane genes, we constructed a regression model for CN-AML prognosis prediction: score = (0.0492 * ) - (0.0018 * ) + (0.0131 * ) + (0.2058 * ) + (0.0234 * ) - (0.3658 * ), which was named as MPG6 (6-Membrane Protein Gene) score. Tested in multiple CN-AML datasets, consistent results showed that CN-AML patients with high MPG6 score had poor survival, higher WBC count and shorter EFS. Comparing with other reported scoring models, the benchmark result of MPG6 achieved better association with survival in multiple cohorts. Moreover, by combining with other clinical indicators in CN-AML, MPG6 could improve the performance of survival prediction and serve as a robust prognostic factor. We identified the MPG6 score as a stable indicator with great potential for clinical application in risk stratification and outcome prediction in CN-AML.

Citing Articles

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Acute myeloid leukemia stem cell signature gene EMP1 is not an eligible therapeutic target.

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Serum circRNA_100199 is a Prognostic Biomarker in Acute Myeloid Leukemia.

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Guo L, Kou R, Song Y, Li G, Jia X, Li Z J Clin Lab Anal. 2022; 36(4):e24337.

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