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Symmetry in cancer networks identified: Proposal for multicancer biomarkers

Published online by Cambridge University Press:  26 December 2019

Pramod Shinde Open the ORCID record for Pramod Shinde [Opens in a new window] ,
Loïc Marrec ,
Aparna Rai ,
Alok Yadav ,
Rajesh Kumar ,
Mikhail Ivanchenko ,
Alexey Zaikin  and
Sarika Jalan Open the ORCID record for Sarika Jalan [Opens in a new window]
Pramod Shinde
Affiliation:
Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (emails: [email protected], [email protected]),
Loïc Marrec
Affiliation:
CNRS, Laboratoire Jean Perrin (UMR 8237), Sorbonne Université, Paris F-75005, France (email: [email protected]),
Aparna Rai
Affiliation:
Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (emails: [email protected], [email protected]),
Alok Yadav
Affiliation:
Complex Systems Lab, Discipline of Physics, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (email: [email protected]),
Rajesh Kumar
Affiliation:
Discipline of Physics, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (email: [email protected]),
Mikhail Ivanchenko
Affiliation:
Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia (email: [email protected]),
Alexey Zaikin
Affiliation:
Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia (email: [email protected]), Department of Mathematics and Institute for Womens Health, University College London, London, WC1E 6BT, UK and Department of Pediatrics, Faculty of Pediatrics, Sechenov University, Moscow, Russia (email: [email protected])
Sarika Jalan*
Affiliation:
Discipline of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (emails: [email protected], [email protected]), Complex Systems Lab, Discipline of Physics, Indian Institute of Technology Indore, Khandwa road, Simrol, Indore 453552, India (email: [email protected]), Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia (email: [email protected]),
*
*Corresponding author. Email: [email protected]
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Abstract

One of the most challenging problems in biomedicine and genomics is the identification of disease biomarkers. In this study, proteomics data from seven major cancers were used to construct two weighted protein–protein interaction networks, i.e., one for the normal and another for the cancer conditions. We developed rigorous, yet mathematically simple, methodology based on the degeneracy at –1 eigenvalues to identify structural symmetry or motif structures in network. Utilizing eigenvectors corresponding to degenerate eigenvalues in the weighted adjacency matrix, we identified structural symmetry in underlying weighted protein–protein interaction networks constructed using seven cancer data. Functional assessment of proteins forming these structural symmetry exhibited the property of cancer hallmarks. Survival analysis refined further this protein list proposing BMI, MAPK11, DDIT4, CDKN2A, and FYN as putative multicancer biomarkers. The combined framework of networks and spectral graph theory developed here can be applied to identify symmetrical patterns in other disease networks to predict proteins as potential disease biomarkers.

Keywords

cancer networkseigenvalue analysisgraph symmetrybiomarkers
Type
Original Article
Information
Network Science , Volume 7 , Issue 4 , December 2019 , pp. 541 - 555
Copyright
© Cambridge University Press 2019 

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