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A novel approach to identify subtype-specific network biomarkers of breast cancer survivability

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Abstract

Background

Increasing the survival rates for breast cancer has gained significant researcher interest. However, current studies reveal that a small subset of gene makers can predict survivability for people with different breast cancer subtypes. In these studies, the selected genes are not necessarily functionally related, and hence, they may not correctly indicate the molecular mechanism behind breast cancer survivability. Also, several studies have shown there is a very low overlap between the biomarkers subsets for the same cancer disease. To improve the robustness of the classification performance and stability of detected biomarkers, recent methods involve taking existing knowledge on relations between genes into account in the classifier by aggregating functionality-related genes to produce discriminative gene subnetworks called network biomarkers.

Results

In this paper, using a dataset of patients with different subtypes of breast cancer, we devised a novel network-based approach by integrating a protein–protein interaction (PPI) network with gene expression data to (1) identify the network biomarkers (metagene) of breast cancer survivability and (2) predict the survivability of breast cancer patients based on their subtypes of breast cancer. Our method involves using the concept of seed genes for the identification of network biomarkers, ADASYN to solve class-imbalance, and random forest to predict the survivability of patients. We obtained the best classification performance with distance three from seed gene protein where the Gmean, F1-measure, and accuracy were respectively 0.900, 0.800, and 90.34%. The maximum size of a network biomarker with distance 3 is 9. A maximum of 34 genes is needed to accurately predict the survivability of breast cancer patients.

Conclusion

This method can be used to identify the survivability of breast cancer patients using gene relationship networks. It has high prediction performance, including specificity and sensitivity for both cohorts of survivals and deceased.

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Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Windsor Essex County Cancer Centre Foundation (WECCCF) Seeds4Hope program.

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Authors and Affiliations

  1. Department of Computer Science, University of Manitoba, E2-445 EITC, Winnipeg, MB, R3T 2N2, Canada

    Sheikh Jubair

  2. School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, ON, N9B 3P4, Canada

    Abedalrhman Alkhateeb, Luis Rueda & Alioune Ngom

  3. Department of Mechanical, Automotive and Materials Engineering (MAME), University of Windsor, 401 Sunset Ave, Windsor, ON, N9B 3P4, Canada

    Ashraf Abou Tabl

Authors
  1. Sheikh Jubair
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  2. Abedalrhman Alkhateeb
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  3. Ashraf Abou Tabl
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  4. Luis Rueda
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  5. Alioune Ngom
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Contributions

S. Jubair has collected the data and implemented the method, A. Ngom and L. Rueda have supervised him during the implementation. A. Alkhateeb helped in extending the method and in validating the results. A. Abou Tabl helped in validating the results. All authors have contributed equally in brainstorming and writing the manuscript.

Corresponding author

Correspondence to Abedalrhman Alkhateeb.

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Conflict of interest

The authors declare that they have no competing interests.

Availability of data and material

The datasets analysed during the current study are available in the cBioPortal repository at http://www.cbioportal.org/study?id=brca_metabric. The resulting PPI network is included in the following link: Supplementary materials: http://www.luisrueda.myweb.cs.uwindsor.ca/datasets/Survivability-Network-Biomarkers-Data.zip. https://1drv.ms/u/s!AuOTsM1T2FzWhpFD_2mMNgkKZBoxng?e=f5cYzu.

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Jubair, S., Alkhateeb, A., Tabl, A.A. et al. A novel approach to identify subtype-specific network biomarkers of breast cancer survivability. Netw Model Anal Health Inform Bioinforma 9, 43 (2020). https://doi.org/10.1007/s13721-020-00249-4

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  • DOI: https://doi.org/10.1007/s13721-020-00249-4

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