Investigation of the hub genes and related mechanism in ovarian cancer via bioinformatics analysis

详细信息    查看全文

• 作者：Ling-jie Fu (1)
  Bing Wang (2)
  
• 关键词：Ovarian cancer ; Protein ; protein interaction network ; Network module ; Molecular mechanism
• 刊名：Journal of Ovarian Research
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：6
• 期：1
• 全文大小：642 KB
• 参考文献：1. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ: Cancer statistics, 2007. / CA Cancer J Clin 2007, 57:43鈥?6. CrossRef
  2. Kurman RJ, Shih I-M: The Origin and pathogenesis of epithelial ovarian cancer-a proposed unifying theory. / Am J Surg Pathol 2010, 34:433. CrossRef
  3. Feeley K, Wells M: Precursor lesions of ovarian epithelial malignancy. / Histopathology 2001, 38:87鈥?5. CrossRef
  4. Bell DA: Origins and molecular pathology of ovarian cancer. / Mod Pathol 2005, 18:S19-S32. CrossRef
  5. Goff BA, Mandel L, Muntz HG, Melancon CH: Ovarian carcinoma diagnosis. / Cancer 2000, 89:2068鈥?075. CrossRef
  6. Lengauer C, Kinzler KW, Vogelstein B: Genetic instabilities in human cancers. / Nature 1998, 396:643鈥?49. CrossRef
  7. Willner J, Wurz K, Allison KH, Galic V, Garcia RL, Goff BA, Swisher EM: Alternate molecular genetic pathways in ovarian carcinomas of common histological types. / Hum Pathol 2007, 38:607鈥?13. CrossRef
  8. Merajver SD, Pham TM, Caduff RF, Chen M, Poy EL, Cooney KA, Weber BL, Collins FS, Johnston C, Frank TS: Somatic mutations in the BRCA1 gene in sporadic ovarian tumours. / Nat Genet 1995, 9:439鈥?43. CrossRef
  9. Nakayama K, Nakayama N, Kurman RJ, Cope L, Pohl G, Samuels Y, Velculescu VE, Wang T-L, Shih I-M: Sequence mutations and amplification of PIK3CA and AKT2 genes in purified ovarian serous neoplasms. / Cancer Biol Ther 2006, 5:779鈥?85. CrossRef
  10. Worsley SD, Ponder BA, Davies BR: Overexpression of cyclin D1 in epithelial ovarian cancers. / Gynecol Oncol 1997, 64:189鈥?95. CrossRef
  11. Sui L, Tokuda M, Ohno M, Hatase O, Hando T: The concurrent expression of p27< sup> kip1</sup> and cyclin D1 in epithelial ovarian tumors. / Gynecol Oncol 1999, 73:202鈥?09. CrossRef
  12. Gilks CB: Subclassification of ovarian surface epithelial tumors based on correlation of histologic and molecular pathologic data. / International Journal of Gynecologic Pathology 2004, 23:200鈥?05. CrossRef
  13. Bowen NJ, Walker LD, Matyunina LV, Logani S, Totten KA, Benigno BB, McDonald JF: Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. / BMC Med Genomics 2009, 2:71. CrossRef
  14. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. / Biostatistics 2003, 4:249鈥?64. CrossRef
  15. Team R: R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, 2007. / Book R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2007 2012. ISBN 3鈥?00051鈥?7鈥?
  16. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK: A comparison of background correction methods for two-colour microarrays. / Bioinformatics 2007, 23:2700鈥?707. CrossRef
  17. Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. / Nucleic Acids Res 2000, 28:27鈥?0. CrossRef
  18. Alvord G, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA: The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. / Genome Biol 2007, 8:R183. CrossRef
  19. Srihari S, Leong HW: Temporal dynamics of protein complexes in PPI networks: a case study using yeast cell cycle dynamics. / BMC bioinformatics 2012,13(Suppl 17):S16.
  20. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B: STRING: a database of predicted functional associations between proteins. / Nucleic Acids Res 2003, 31:258鈥?61. CrossRef
  21. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. / Genome Res 2003, 13:2498鈥?504. CrossRef
  22. Junker BH, Kosch眉tzki D, Schreiber F: Exploration of biological network centralities with CentiBiN. / BMC bioinformatics 2006, 7:219. CrossRef
  23. Maraziotis IA, Dimitrakopoulou K, Bezerianos A: An in silico method for detecting overlapping functional modules from composite biological networks. / BMC Syst Biol 2008, 2:93. CrossRef
  24. DeRycke MS, Pambuccian SE, Gilks CB, Kalloger SE, Ghidouche A, Lopez M, Bliss RL, Geller MA, Argenta PA, Harrington KM: Nectin 4 overexpression in ovarian cancer tissues and serum potential role as a serum biomarker. / Am J Clin Pathol 2010, 134:835鈥?45. CrossRef
  25. Balch C, Huang TH, Brown R, Nephew KP: The epigenetics of ovarian cancer drug resistance and resensitization. / Am J Obstet Gynecol 2004, 191:1552鈥?572. CrossRef
  26. Reles A, Wen WH, Schmider A, Gee C, Runnebaum IB, Kilian U, Jones LA, El-Naggar A, Minguillon C, Sch枚nborn I: Correlation of p53 mutations with resistance to platinum-based chemotherapy and shortened survival in ovarian cancer. / Clin Cancer Res 2001, 7:2984鈥?997.
  27. Jabbour AM, Gordon L, Daunt CP, Green BD, Kok CH, D'Andrea R, Ekert PG: p53-Dependent transcriptional responses to interleukin-3 signaling. / PloS One 2012, 7:e31428. CrossRef
  28. Nakayama N, Nakayama K, Shamima Y, Ishikawa M, Katagiri A, Iida K, Miyazaki K: Gene amplification CCNE1 is related to poor survival and potential therapeutic target in ovarian cancer. / Cancer 2010, 116:2621鈥?634. CrossRef
  29. SLAV D, Bar I, Sandbank J: Usefulness of CDK5RAP3, CCNB2, and RAGE genes for the diagnosis of lung adenocarcinoma. / Int J Biol Markers 2007, 22:108鈥?13.
  30. Rodriguez-Antona C, Ingelman-Sundberg M: Cytochrome P450 pharmacogenetics and cancer. / Oncogene 2006, 25:1679鈥?691. CrossRef
  31. Jia J, Li B, Jin Y, Wang D: Expression, purification, and characterization of human tyrosyl-tRNA synthetase. / Protein Expr Purif 2003, 27:104鈥?08. CrossRef
  32. Morotti M, Becker CM, Menada MV, Ferrero S: Targeting tyrosine-kinases in ovarian cancer. / Expert Opin Investig Drugs 2013,22(10):1265鈥?9. CrossRef
  33. Bains RK, Kovacevic M, Plaster CA, Tarekegn A, Bekele E, Bradman NN, Thomas MG: Molecular diversity and population structure at the cytochrome P450 3A5 gene in Africa. / BMC genetics 2013, 14:34. CrossRef
  34. Downie D, McFadyen MC, Rooney PH, Cruickshank ME, Parkin DE, Miller ID, Telfer C, Melvin WT, Murray GI: Profiling cytochrome P450 expression in ovarian cancer: identification of prognostic markers. / Clin Cancer Res 2005, 11:7369鈥?375. CrossRef
  35. Ferrara N, Gerber H-P, LeCouter J: The biology of VEGF and its receptors. / Nat Med 2003, 9:669鈥?76. CrossRef
  36. Awata T, Inoue K, Kurihara S, Ohkubo T, Watanabe M, Inukai K, Inoue I, Katayama S: A common polymorphism in the 5鈥?untranslated region of the VEGF gene is associated with diabetic retinopathy in type 2 diabetes. / Diabetes 2002, 51:1635鈥?639. CrossRef
  37. Herr D, Sallmann A, Bekes I, Konrad R, Holzheu I, Kreienberg R, Wulff C: VEGF induces ascites in ovarian cancer patients via increasing peritoneal permeability by downregulation of Claudin 5. / Gynecol Oncol 2012, 127:210鈥?16. CrossRef
  
• 作者单位：Ling-jie Fu (1)
  Bing Wang (2)
  
  1. Department of gynaecology and obstetrics, Shengjing hospital of China Medical University, No.36 Sanhao Street, Shenyang City, 110004, China
  2. Ultrasound Department, Shengjing hospital of China Medical University, No.36 Sanhao Street, Shenyang City, 110004, China
  
• ISSN：1757-2215

文摘

Background Ovarian cancer is a cancerous growth arising from the ovary. Objective This study was aimed to explore the molecular mechanism of the development and progression of the ovarian cancer. Methods We first identified the differentially expressed genes (DEGs) between the ovarian cancer samples and the healthy controls by analyzing the GSE14407 affymetrix microarray data, and then the functional enrichments of the DEGs were investigated. Furthermore, we constructed the protein-protein interaction network of the DEGs using the STRING online tools to find the genes which might play important roles in the progression of ovarian cancer. In addition, we performed the enrichment analysis to the PPI network. Results Our study screened 659 DEGs, including 77 up- and 582 down-regulated genes. These DEGs were enriched in pathways such as Cell cycle, p53 signaling pathway, Pathways in cancer and Drug metabolism. CCNE1, CCNB2 and CYP3A5 were the significant genes identified from these pathways. Protein-protein interaction (PPI) network was constructed and network Module A was found closely associated with ovarian cancer. Hub nodes such as VEGFA, CALM1, BIRC5 and POLD1 were found in the PPI network. Module A was related to biological processes such as mitotic cell cycle, cell cycle, nuclear division, and pathways namely Cell cycle, Oocyte meiosis and p53 signaling pathway. Conclusions It indicated that ovarian cancer was closely associated to the dysregulation of p53 signaling pathway, drug metabolism, tyrosine metabolism and cell cycle. Besides, we also predicted genes such as CCNE1, CCNB2, CYP3A5 and VEGFA might be target genes for diagnosing the ovarian cancer.