title: Prediction of key genes in ovarian cancer treated with decitabine based on network strategy.
abstract: The objective of the present study was to predict key genes in ovarian cancer before and after treatment with decitabine utilizing a network approach and to reveal the molecular mechanism. Pathogenic networks of ovarian cancer before and after treatment were identified based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) detected by Significance Analysis of Microarrays (SAM) method. A weight was assigned to each gene in the pathogenic network and then candidate genes were evaluated. Topological properties (degree, betweenness, closeness and stress) of candidate genes were analyzed to investigate more confident pathogenic genes. Pathway enrichment analysis for candidate and seed genes were conducted. Validation of candidate gene expression in ovarian cancer was performed by reverse transcriptase-polymerase chain reaction (RT-PCR) assays. There were 73 nodes and 147 interactions in the pathogenic network before treatment, while 47 nodes and 66 interactions after treatment. A total of 32 candidate genes were identified in the before treatment group of ovarian cancer, of which 16 were rightly candidate genes after treatment and the others were silenced. We obtained 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) for decitabine treatment that were validated by RT-PCR. In conclusion, we successfully identified 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) and validated them, which provides insight into the molecular mechanisms of decitabine treatment and may be potential pathogenic biomarkers for the therapy of ovarian cancer.
authors.affiliation: Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, P.R. China.
authors.forename: Yu-Zhen
authors.initials: YZ
authors.lastname: Wang
authors.affiliation: Department of Nursing, Zhejiang Provincial People's Hospital, Xiacheng, Hangzhou, Zhejiang 310014, P.R. China.
authors.forename: Sheng-Chun
authors.initials: SC
authors.lastname: Qiu
mesh_terms: D000964:Antimetabolites, Antineoplastic / Q000627:therapeutic use*; D001374:Azacitidine / Q000031:analogs & derivatives* / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000235:genetics; D018797:Cell Cycle Proteins / Q000235:genetics; D019295:Computational Biology; D056744:Cyclin B1 / Q000235:genetics; D030541:Databases, Genetic; D000077209:Decitabine; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000187:drug effects / Q000235:genetics*; D006801:Humans; D053583:Interleukin-1beta / Q000235:genetics; D007376:Interleukin-2 / Q000235:genetics; D046228:Microarray Analysis; D009687:Nuclear Proteins / Q000235:genetics; D010051:Ovarian Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction
chemical_list: D000964:Antimetabolites, Antineoplastic; D014408:Biomarkers, Tumor; C534699:CCNB1 protein, human; C103706:CDC6 protein, human; D018797:Cell Cycle Proteins; D056744:Cyclin B1; C508585:IL1B protein, human; C508594:IL2 protein, human; D053583:Interleukin-1beta; D007376:Interleukin-2; D009687:Nuclear Proteins; D000077209:Decitabine; D019869:Phosphatidylinositol 3-Kinases; C560566:phosphoinositol-3 kinase regulatory subunit 2, human; D001374:Azacitidine