Citation

San Lucas FA, Redell J, Pramod D, Liu Y. BMC Syst. Biol. 2018; 12(Suppl 8): e131.

Affiliation

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, USA. Yin.Liu@uth.tmc.edu.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12918-018-0645-z

PMID

30577783

Abstract

BACKGROUND: Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.

RESULTS: In this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.

CONCLUSIONS: The systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

Language: en

Keywords

Biomarkers; Gene ontology annotation; Subnetwork modularity; Weighted protein interaction network; mTBI subtype classification