A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts

Lin, Sheng-Yan; Miao, Ya-Ru; Hu, Fei-Fei; Hu, Hui; Zhang, Qiong; Li, Qiubai; Chen, Zhichao; Guo, An-Yuan

doi:10.7150/jca.35382

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J Cancer 2020; 11(1):251-259. doi:10.7150/jca.35382 This issue Cite

Research Paper

A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts

Sheng-Yan Lin^1*, Ya-Ru Miao^1*, Fei-Fei Hu¹, Hui Hu¹, Qiong Zhang¹, Qiubai Li², Zhichao Chen^2✉, An-Yuan Guo^1✉

1. Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
2. Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
* Equal contribution authors

Citation:

Lin SY, Miao YR, Hu FF, Hu H, Zhang Q, Li Q, Chen Z, Guo AY. A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts. J Cancer 2020; 11(1):251-259. doi:10.7150/jca.35382. https://www.jcancer.org/v11p0251.htm

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Abstract

Background: 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.

Purpose: This study aims to identify prognostic factors to stratify CN-AML patients to choose better treatments and improve their outcomes.

Methods: 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”.

Results: Based on independent prognostic membrane genes, we constructed a regression model for CN-AML prognosis prediction: score = (0.0492 * CD52) - (0.0018 * CD96) + (0.0131 * EMP1) + (0.2058 * TSPAN2) + (0.0234 * STAB1) - (0.3658 * MBTPS1), 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.

Conclusions: We identified the MPG6 score as a stable indicator with great potential for clinical application in risk stratification and outcome prediction in CN-AML.

Keywords: Cytogenetically normal acute myeloid leukemia, membrane protein genes, MPG6 (6-Membrane Protein Gene) score, risk stratification, outcome prediction.

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APA

Lin, S.Y., Miao, Y.R., Hu, F.F., Hu, H., Zhang, Q., Li, Q., Chen, Z., Guo, A.Y. (2020). A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts. Journal of Cancer, 11(1), 251-259. https://doi.org/10.7150/jca.35382.

ACS

Lin, S.Y.; Miao, Y.R.; Hu, F.F.; Hu, H.; Zhang, Q.; Li, Q.; Chen, Z.; Guo, A.Y. A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts. J. Cancer 2020, 11 (1), 251-259. DOI: 10.7150/jca.35382.

NLM

CSE

Lin SY, Miao YR, Hu FF, Hu H, Zhang Q, Li Q, Chen Z, Guo AY. 2020. A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts. J Cancer. 11(1):251-259.

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