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Protein Graphs in Cancer Prediction

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An Omics Perspective on Cancer Research

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Abstract

The consequences of breast, colon and prostate cancer create the necessity of new, simpler and faster theoretical models that may allow earlier cancer detection. The present work has built several Quantitative Protein (or Proteome) – Disease Relationships (QPDRs). QPDRs, similar to Quantitative Structure Activity Relationship (QSAR) models, are based on topological indices (TIs) and/or connectivity indices (CIs) of graphs. In particular, we used Star graphs and Lattice networks of protein sequence or MS outcomes of blood proteome in order to predict the proteins related to breast and colon cancer and to improve the diagnostic potential of the PSA biomarker for prostate cancer. The advantages of this method are the simplicity, fast calculations and few resources needed (free software programmes, such as MARCH-INSIDE and S2SNet). Thus, this ideal theoretical scheme can be easily extended to other types of diseases or even other fields, such as Genomics or Systems Biology.

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Acknowledgements

We thank for the grant (2007/127 and 2007/144) from the General Directorate of Scientific and Technologic Promotion of the Galician University System of the Xunta de Galicia, for the grant (PIO52048 and RD07/0067/0005) funded by the Carlos III Health Institute, and for the grant (File 2006/60, 2007/127 and 2007/144) from the General Directorate of Scientific and Technologic Promotion of the Galician University System of the Xunta de Galicia (Spain). González-Díaz and Munteanu acknowledge for the contract/grant sponsorships of Isidro Parga Pondal Programme, Xunta de Galicia (Spain). Ferino gratefully acknowledges a PhD scholarship from the University of Cagliari, actually developing a Bioinformatics research under Supervision of Professor Uriarte and Dr. González-Díaz at the University of Santiago de Compostela (Spain).

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Authors and Affiliations

  1. Department of Microbiology & Parasitology, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Humberto González-Díaz

  2. Dipartimento Farmaco Chimico Tecnologico, Universitá degli Studi di Cagliari, 09124, Cagliari, Italia

    Giulio Ferino

  3. Department of Information and Communication Technologies, University of A Coruña, 15071, A Coruña, Spain

    Alejandro Pazos & Cristian R. Munteanu

  4. Department of Organic Chemistry, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Francisco J. Prado-Prado, Santiago Vilar & Eugenio Uriarte

Authors
  1. Humberto González-Díaz
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  2. Giulio Ferino
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  3. Francisco J. Prado-Prado
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  4. Santiago Vilar
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  5. Eugenio Uriarte
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  6. Alejandro Pazos
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  7. Cristian R. Munteanu
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Correspondence to Humberto González-Díaz .

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  1. Dept. Clinical Oncology, Queen Elizabeth Hospital, Gascoigne Road 30, Kowloon, Hong Kong/PR China

    William C.S. Cho

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González-Díaz, H. et al. (2010). Protein Graphs in Cancer Prediction. In: Cho, W. (eds) An Omics Perspective on Cancer Research. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2675-0_7

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