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Comput Math Methods Med. 2016;2016:3186051. doi: 10.1155/2016/3186051. Epub 2016 Feb 29.

Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.

Author information

1
The 2nd Department of Gynecology, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang 830000, China.
2
Department of Gynecology, Ninth Hospital of Xi'an, Xi'an, Shaanxi 715100, China.

Abstract

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.

PMID:
27034707
PMCID:
PMC4789371
DOI:
10.1155/2016/3186051
[Indexed for MEDLINE]
Free PMC Article

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