title: Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: The 2nd Department of Gynecology, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang 830000, China.
authors.forename: Yun-Xia
authors.initials: YX
authors.lastname: Zhang
authors.affiliation: Department of Gynecology, Ninth Hospital of Xi'an, Xi'an, Shaanxi 715100, China.
authors.forename: Yan-Li
authors.identifier: 0000-0002-9260-4076
authors.initials: YL
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D002374:Catalase / Q000235:genetics; D002453:Cell Cycle; D018797:Cell Cycle Proteins / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020781:Matrix Metalloproteinase 1 / Q000235:genetics; D008957:Models, Genetic; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D015398:Signal Transduction; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D001618:beta-Lactamases / Q000235:genetics
chemical_list: C113571:CDC45 protein, human; D018797:Cell Cycle Proteins; D002374:Catalase; C522360:MMP1 protein, human; D020781:Matrix Metalloproteinase 1; C417264:VIM-1 metallo-beta-lactamase; D001618:beta-Lactamases