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2017 ; 2017
(ä): 3195348
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Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human
Monocytes Infected by Listeria monocytogenes
#MMPMID28951764
Fan W
; Zhou Y
; Li H
Comput Math Methods Med
2017[]; 2017
(ä): 3195348
PMID28951764
show ga
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.