Identification of potential targets for ovarian cancer treatment by systematic bioinformatics analysis

Purpose of investigation: To provide a systematic overview to understand the mechanism of ovarian cancer. Materials and Methods: Data of GSE14407 downloaded from Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analysis were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the authors constructed the protein-protein interaction (PPI) network and co-expression networks by Cytoscape. Results: A total 1,442 genes were identified to be differentially expressed. Regulatory effects of DEGs mainly focused on cell cycle, transcription regulation, and cellular protein metabolic process. Significant pathways were determined to be p53 signaling pathway, amino sugar, and nucleotide sugar metabolism. The most significant transcription factor was aryl hydrocarbon receptor nucleartranslocator (ARNT). Abnormal spindle-like microcephaly-associated protein (ASPM), Aurora kinase (AURKA), Cyclin-A2 (CCNA2), G2/mitotic-specific cyclin-B1, (CCNB1), and Cyclin-dependent kinase 1 (CDK1) were significant nodes in PPI network. Conclusion: The significant genes and pathways show potential targets for the treatment of ovarian cancer.

Ovarian cancer; Protein-protein interaction; Co-expression network; Gene ontology analysis; Pathway enrichment analysis.

[1] Balmain A., Gray J., Ponder B.: “The genetics and genomics of cancer.” Nat. Genet., 2003, 33, 238.

[2] Zhao M., Sun J., Zhao Z.: “Distinct and competitive regulatory patterns of tumor suppressor genes and oncogenes in ovarian cancer”. PloS One, 2012, 7, e44175.

[3] Shayesteh L., Lu Y., Kuo W-L., Baldocchi R., Godfrey T., Collins C., et al.: “PIK3CA is implicated as an oncogene in ovarian cancer”. Nat. Genet., 1999, 21, 99.

[4] Zhao M., Sun J., Zhao Z.: “Synergetic regulatory networks mediated by oncogene-driven microRNAs and transcription factors in serous ovarian cancer”. Mol. Biosyst., 2013, 9, 3187.

[5] Siegel R., Naishadham D., Jemal A.: “Cancer statistics, 2012”. CA Cancer J. Clin., 2012, 62, 10.

[6] Ono K., Tanaka T., Tsunoda T., Kitahara O., Kihara C., Okamoto A., et al.: “Identification by cDNA microarray of genes involved in ovarian carcinogenesis”. Cancer Res., 2000, 60, 5007.

[7] Crijns A.P., Fehrmann R.S., de Jong S., Gerbens F., Meersma G.J., Klip H.G., et al.: “Survival-related profile, pathways, and transcription factors in ovarian cancer. PLoS Med., 2009, 6, e24. doi: 10.1371/ journal.pmed.1000024.

[8] Van Jaarsveld M., Helleman J., Berns E.M., Wiemer E.A.: “MicroRNAs in ovarian cancer biology and therapy resistance”. Int. J. Biochem. Cell Biol., 2010, 42, 1282.

[9] Hermeking H.: “MicroRNAs in the p53 network: micromanagement of tumour suppression”. Nat. Rev. Cancer, 2012, 12, 613. doi: 10.1038/nrc3318. Epub 2012 Aug 17.

[10] Darnell J.E.: “Transcription factors as targets for cancer therapy”. Nat. Rev. Cancer, 2002, 2, 740.

[11] Chuaqui R.F., Cole K.A., Emmert-Buck M.R., Merino M.J.: “Histopathology and molecular biology of ovarian epithelial tumors”. Ann. Diagn. Pathol., 1998, 2, 195.

[12] Aunoble B., Sanches R., Didier E., Bignon Y.: “Major oncogenes and tumor suppressor genes involved in epithelial ovarian cancer (review)”. Int. J. Oncol., 2000, 16, 567.

[13] Bowen N.J., Walker L.D., Matyunina L.V., Logani S., Totten K.A., Benigno B.B., et al.: “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.

[14] Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U., et al.: “Exploration, normalization, and summaries of high density oligonucleotide array probe level data”. Biostatistics, 2003, 4, 249.

[15] Smyth G.K.: “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat. Appl. Genet. Mol. Biol., 2004, 3, Article3. Epub 2004 Feb 12.

[16] Harris M., Clark J., Ireland A., Lomax J., Ashburner M., Foulger R., et al.: “The Gene Ontology (GO) database and informatics resource”. Nucleic Acids Res., 2004, 32, D258.

[17] Al-Shahrour F, Díaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 2004, 20, 578.

[18] Kanehisa M., Goto S.: “KEGG: kyoto encyclopedia of genes and genomes”. Nucleic Acids Res., 2000, 28, 27.

[19] Huang da W., Sherman B.T., Lempicki R.A.: “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources”. Nat. Protoc., 2009, 4, 44.

[20] Letunic I., Copley R.R., Pils B., Pinkert S., Schultz J., Bork P.: “SMART 5: domains in the context of genomes and networks”. Nucleic Acids Res., 2006, 34, D257.

[21] Kohl M., Wiese S., Warscheid B.: “Cytoscape: software for visualization and analysis of biological networks”. Methods Mol. Biol., 2011, 696, 291. doi: 10.1007/978-1-60761-987-1_18.

[22] He J., Li C.,Ye B., Zhong W.: “Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks”. BMC Bioinformatics, 2012, 13, 19

[23] Fujita P.A., Rhead B., Zweig A.S., Hinrichs A.S., Karolchik D., Cline M.S., et al.: “The UCSC genome browser database: update 2011”. Nucleic Acids Res., 2011, 39, D876.

[24] Ellrott K., Yang C., Sladek F.M., Jiang T.: “Identifying transcription factor binding sites through Markov chain optimization”. Bioinformatics, 2002, 18, S100.

[25] Marks J., Davidoff A., Kerns B., Humphrey P., Pence J., Dodge R., et al.: “Overexpression and mutation of p53 in epithelial ovarian cancer”. Cancer Res., 1991, 51, 2979.

[26] Eliopoulos A.G., Kerr D.J., Herod J., Hodgkins L., Krajewski S., Reed J.C., et al.: “The control of apoptosis and drug resistance in ovarian cancer: influence of p53 and Bcl-2”. Oncogene, 1995, 11, 1217.

[27] Yang X., Fraser M., Moll U.M., Basak A., Tsang B.K.: “Akt-mediated cisplatin resistance in ovarian cancer: modulation of p53 action on caspase-dependent mitochondrial death pathway”. Cancer Res., 2006, 66, 3126.

[28] Hankinson O.: “The aryl hydrocarbon receptor complex”. Annu. Rev. Pharmacol. Toxicol., 1995, 35, 307.

[29] Kang H.J., Kim H.J., Kim S.K., Barouki R., Cho C-H., Khanna K.K., et al.: “BRCA1 modulates xenobiotic stress-inducible gene expression by interacting with ARNT in human breast cancer cells”. J. Biol. Chem., 2006, 281, 14654.

[30] Lin P., Hu S-W., Chang T-H.: “Correlation between gene expression of aryl hydrocarbon receptor (AhR), hydrocarbon receptor nuclear translocator (Arnt), cytochromes P4501A1 (CYP1A1) and 1B1 (CYP1B1), and inducibility of CYP1A1 and CYP1B1 in human lymphocytes”. Toxicol. Sci., 2003, 71, 20.

[31] Aktas D., Guney I., Alikasifoglu M., Yüce K., Tuncbilek E., Ayhan A.: “CYP1A1 gene polymorphism and risk of epithelial ovarian neoplasm”. Gynecol. Oncol., 2002, 86, 124.

[32] Kouprina N., Pavlicek A., Collins N.K., Nakano M., Noskov V.N., Ohzeki J-I., et al.: “The microcephaly ASPM gene is expressed in proliferating tissues and encodes for a mitotic spindle protein”. Hum. Mol. Genet., 2005, 14, 2155.

[33] Brüning-Richardson A., Bond J., Alsiary R., Richardson J., Cairns D, McCormack L., et al.: “ASPM and microcephalin expression in epithelial ovarian cancer correlates with tumour grade and survival”. Br. J. Cancer, 2011, 104, 1602.

[34] Horvath S., Zhang B., Carlson M., Lu K., Zhu S., Felciano R., et al.: “Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target”. Proc. Natl. Acad. Sci. USA, 2006, 103, 17402.

[35] Hannak E., Kirkham M., Hyman A.A., Oegema K.: “Aurora-A kinase is required for centrosome maturation in Caenorhabditis elegans”. J. Cell. Biol., 2001, 155, 1109.

[36] Chung C., Man C., Jin Y., Jin C., Guan X., Wang Q., et al.: “Amplification and overexpression of aurora kinase A (AURKA) in immortalized human ovarian epithelial (HOSE) cells”. Mol. Carcinogen, 2005, 43, 165.