Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Purpose.
The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC.
Methods.
A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls.
Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE.
Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution.
Eventually, pathway enrichment analysis for candidate genes was performed.
Results.
In this work, a total of 330 DEGs were identified between CC and normal controls.
From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10.
Among these candidate genes, VIM had the highest weight value.
Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism.
Conclusion.
Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC.
We believe that our results can provide theoretical guidelines for future clinical application.
American Psychological Association (APA)
Zhang, Yun-Xia& Zhao, Yan-Li. 2016. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100104
Modern Language Association (MLA)
Zhang, Yun-Xia& Zhao, Yan-Li. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100104
American Medical Association (AMA)
Zhang, Yun-Xia& Zhao, Yan-Li. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100104
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100104