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Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Cancer Gene Therapy volume 27, pages 56–69 (2020)Cite this article

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Abstract

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC+ and LSC− cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC+ and LSC− cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

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Funding

This study was supported by the National Natural Science Foundation of China (31701151), Natural Science Foundation of Shanghai (17ZR1412500), National Key R&D Program of China (2018YFC0910403), Shanghai Sailing Program (16YF1413800), the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245), the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences (201703), Science and Technology Commission of Shanghai Municipality (STCSM) (18dz2271000).

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Author notes

  1. These authors contributed equally: JiaRui Li, Lin Lu

Authors and Affiliations

  1. Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, P. R. China

    JiaRui Li, Yu-Hang Zhang, XiangYin Kong & Tao Huang

  2. School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China

    JiaRui Li & Yu-Dong Cai

  3. Department of Radiology, Columbia University Medical Center, New York, NY, 10032, USA

    Lin Lu

  4. Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, P. R. China

    YaoChen Xu

  5. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, P. R. China

    Min Liu & Lei Chen

  6. Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou, 510507, P. R. China

    KaiYan Feng

  7. Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, 200241, P. R. China

    Lei Chen

Authors
  1. JiaRui Li
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  2. Lin Lu
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  3. Yu-Hang Zhang
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  4. YaoChen Xu
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  5. Min Liu
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  6. KaiYan Feng
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  7. Lei Chen
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  8. XiangYin Kong
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  9. Tao Huang
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  10. Yu-Dong Cai
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Corresponding authors

Correspondence to XiangYin Kong, Tao Huang or Yu-Dong Cai.

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Li, J., Lu, L., Zhang, YH. et al. Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine. Cancer Gene Ther 27, 56–69 (2020). https://doi.org/10.1038/s41417-019-0105-y

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  • DOI: https://doi.org/10.1038/s41417-019-0105-y

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