Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data

Yan Yan, Jiangyong Wei, Xiaohua Hu, Tianhai Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The advances in proteomic technologies have offered an unprecedented opportunity and valuable resources to reveal molecular targets for treatment. Although a number of approaches have been designed to develop mathematical models using the time series proteomic profiles, the recently published single-patient proteomic data raised substantial challenges for analysing these non-time series datasets. To address this issue, this work proposes the first attempt for designing mathematical models using the non-time series proteomic data. Using the triple-negative breast cancer (TNBC) as the test system, we first use the single-cell analysis algorithm to derive the pseudo-time trajectory of the protein activities. Our integrated approach includes both a top-down approach (namely the Gaussian graphical model) and a bottom-up approach (i.e. differential equation model) to reverse-engineer the regulatory network. Based on the information from GO-enrichment analysis and KEGG database, we select 16 proteins that are key components in the mitogen-activated protein (MAP) kinase pathways. We construct the structure of a network with 16 proteins and a dynamic model for a network of 12 proteins. The derived protein-protein relationships are partially supported by the established protein activation relationships, and our model also predicts potential protein relationships that may be confirmed by further experimental studies. In summary, our results suggest that the proposed integrated framework is an effective approach to reconstruct regulatory networks using non-time course proteomic data.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsHuiru (Jane) Zheng, Zoraida Callejas, David Griol, Haiying Wang, Xiaohua Hu, Harald Schmidt, Jan Baumbach, Julie Dickerson, Le Zhang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2174-2181
Number of pages8
ISBN (Electronic)9781538654880, 9781538654873, 9781538654897
DOIs
Publication statusPublished - Dec 2018
EventIEEE International Conference on Bioinformatics and Biomedicine, 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8609864

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, 2018
Abbreviated titleIEEE BIBM 2018
CountrySpain
CityMadrid
Period3/12/186/12/18
Internet address

Keywords

  • network inference
  • protein-protein network
  • proteomics data
  • triple-negative breast cancer

Cite this

Yan, Y., Wei, J., Hu, X., & Tian, T. (2018). Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data. In H. J. Zheng, Z. Callejas, D. Griol, H. Wang, X. Hu, H. Schmidt, J. Baumbach, J. Dickerson, ... L. Zhang (Eds.), Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2174-2181). [8621548] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM.2018.8621548
Yan, Yan ; Wei, Jiangyong ; Hu, Xiaohua ; Tian, Tianhai. / Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data. Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). editor / Huiru (Jane) Zheng ; Zoraida Callejas ; David Griol ; Haiying Wang ; Xiaohua Hu ; Harald Schmidt ; Jan Baumbach ; Julie Dickerson ; Le Zhang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 2174-2181 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).
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abstract = "The advances in proteomic technologies have offered an unprecedented opportunity and valuable resources to reveal molecular targets for treatment. Although a number of approaches have been designed to develop mathematical models using the time series proteomic profiles, the recently published single-patient proteomic data raised substantial challenges for analysing these non-time series datasets. To address this issue, this work proposes the first attempt for designing mathematical models using the non-time series proteomic data. Using the triple-negative breast cancer (TNBC) as the test system, we first use the single-cell analysis algorithm to derive the pseudo-time trajectory of the protein activities. Our integrated approach includes both a top-down approach (namely the Gaussian graphical model) and a bottom-up approach (i.e. differential equation model) to reverse-engineer the regulatory network. Based on the information from GO-enrichment analysis and KEGG database, we select 16 proteins that are key components in the mitogen-activated protein (MAP) kinase pathways. We construct the structure of a network with 16 proteins and a dynamic model for a network of 12 proteins. The derived protein-protein relationships are partially supported by the established protein activation relationships, and our model also predicts potential protein relationships that may be confirmed by further experimental studies. In summary, our results suggest that the proposed integrated framework is an effective approach to reconstruct regulatory networks using non-time course proteomic data.",
keywords = "network inference, protein-protein network, proteomics data, triple-negative breast cancer",
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Yan, Y, Wei, J, Hu, X & Tian, T 2018, Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data. in HJ Zheng, Z Callejas, D Griol, H Wang, X Hu, H Schmidt, J Baumbach, J Dickerson & L Zhang (eds), Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)., 8621548, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 2174-2181, IEEE International Conference on Bioinformatics and Biomedicine, 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621548

Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data. / Yan, Yan; Wei, Jiangyong; Hu, Xiaohua; Tian, Tianhai.

Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ed. / Huiru (Jane) Zheng; Zoraida Callejas; David Griol; Haiying Wang; Xiaohua Hu; Harald Schmidt; Jan Baumbach; Julie Dickerson; Le Zhang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 2174-2181 8621548 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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KW - proteomics data

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Yan Y, Wei J, Hu X, Tian T. Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data. In Zheng HJ, Callejas Z, Griol D, Wang H, Hu X, Schmidt H, Baumbach J, Dickerson J, Zhang L, editors, Proceedings: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 2174-2181. 8621548. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621548