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Identification of Vital Genes for NSCLC Integrating Mutual Information and Synergy

Author

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  • Xiaobo Yang

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    LMIB and NLSDE, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China
    Peng Cheng Laboratory, Shenzhen 518055, China)

  • Zhilong Mi

    (Zhongguancun Laboratory, Beijing 100094, China
    Peng Cheng Laboratory, Shenzhen 518055, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China)

  • Qingcai He

    (School of Mathematical Sciences, Beihang University, Beijing 100191, China
    LMIB and NLSDE, Beihang University, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100094, China
    Peng Cheng Laboratory, Shenzhen 518055, China)

  • Binghui Guo

    (Zhongguancun Laboratory, Beijing 100094, China
    Peng Cheng Laboratory, Shenzhen 518055, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China)

  • Zhiming Zheng

    (Zhongguancun Laboratory, Beijing 100094, China
    Peng Cheng Laboratory, Shenzhen 518055, China
    Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China)

Abstract

Lung cancer, amongst the fast growing malignant tumors, has become the leading cause of cancer death, which deserves attention. From a prevention and treatment perspective, advances in screening, diagnosis, and treatment have driven a reduction in non-small-cell lung cancer (NSCLC) incidence and improved patient outcomes. It is of benefit that the identification of key genetic markers contributes to the understanding of disease initiation and progression. In this work, information theoretical measures are proposed to determine the collaboration between genes and specific NSCLC samples. Top mutual information observes genes of high sample classification accuracy, such as STX11, S1PR1, TACC1, LRKK2, and SRPK1. In particular, diversity exists in different gender, histology, and smoking situations. Furthermore, leading synergy detects a high-accuracy combination of two ordinary individual genes, bringing a significant gain in accuracy. We note a strong synergistic effect of genes between COL1A2 and DCN, DCN and MMP2, and PDS5B and B3GNT8. Apart from that, RHOG is revealed to have quite a few functions in coordination with other genes. The results provide evidence for gene-targeted therapy as well as combined diagnosis in the context of NSCLC. Our approach can also be extended to find synergistic biomarkers associated with different diseases.

Suggested Citation

  • Xiaobo Yang & Zhilong Mi & Qingcai He & Binghui Guo & Zhiming Zheng, 2023. "Identification of Vital Genes for NSCLC Integrating Mutual Information and Synergy," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1460-:d:1100137
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    References listed on IDEAS

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    1. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    2. Roy S. Herbst & Daniel Morgensztern & Chris Boshoff, 2018. "The biology and management of non-small cell lung cancer," Nature, Nature, vol. 553(7689), pages 446-454, January.
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