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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 3195348, 8 pages
https://doi.org/10.1155/2017/3195348
Research Article

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

1Medical Department, Xiangyang No. 1 People’s Hospital Affiliated to Hubei University of Medicine, Xiangyang, Hubei 441000, China
2Department of Obstetrics and Gynecology, Xiangyang No. 1 People’s Hospital Affiliated to Hubei University of Medicine, Xiangyang, Hubei 441000, China
3Department of Laboratory Medicine, Xiangyang No. 1 People’s Hospital Affiliated to Hubei University of Medicine, Xiangyang, Hubei 441000, China

Correspondence should be addressed to Hao Li; moc.621@144oahil

Received 10 April 2017; Revised 19 June 2017; Accepted 13 July 2017; Published 16 August 2017

Academic Editor: Marko Gosak

Copyright © 2017 Wufeng Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.