Protein graphs in cancer prediction

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. © 2010 Springer Science+Business Media B.V.