##plugins.themes.bootstrap3.article.main##

Akram Siavoshi Mahdieh Taghizadeh Elahe Dookhe Mehran Piran Mahsa Saliani Shahla Mohammad Ganji

Abstract

     Epithelial ovarian cancer (EOC), as a challenging disease among women with poor prognosis and unclear molecular pathogenesis, each year is responsible for 140000 deaths globally. Recent progress in the field revealed the importance of proteins as key players of different biological events. Considering the complicated protein interactions, taking a deeper look at protein-protein interactions (PPIs) could be considered as a superior strategy to unravel complex mechanisms encountered with regulatory cell signaling pathways of ovarian cancer. Hence, PPI network analysis was performed on differentially expressed genes (DEGs) of ovarian cancer to discover hub genes which have the potential to be introduced as biomarkers with clinical utility. A PPI network with 600 DEGs was constructed. Network topology analysis determined UBC, FN1, SPP1, ACTB, GAPDH, JUN, and RPL13A, with the highest Degree (K) and betweenness centrality (BC), as shortcuts of the network. KEGG pathway analysis showed that these genes are commonly enriched in ribosome and ECM-receptor interaction pathways. These pivotal hub genes, mainly UBC, FN1, RPL13A, SPP1, and JUN have been reported previously as potential prognostic biomarkers of different types of cancer. However, further experimental molecular studies and computational processes are required to confirm the function and association of the identified hub genes with epithelial ovarian cancer prognosis.

Article Details

Keywords

Epithelial Ovarian Cancer, Differentially Expressed Gene Analysis, PPI Network Analysis, Pathway Enrichment Analysis

References
Anders S. and Huber W. (2010) Differential expression analysis for sequence count data. Genome Biol, 11:R106.
Anders S., Pyl P. T. and Huber W. (2014) HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics, 31: 166-9.
Archana R., Simmons K. B., Robert C. and Bast J. R. (2013) The Emerging Role of HE4 in the Evaluation of Advanced Epithelial Ovarian and Endometrial Carcinomas. Oncology, 27: 548-556.
Arnedos M., Roulleaux Degage M., Perez-Garcia J. and Cortes J. (2019) Window of Opportunity trials for biomarker discovery in breast cancer. Curr. Opin. Oncol, 31: 486-492.
Ashburner M., Ball C. A., Blake J. A., Botstein D. and Butler H. (2000) Gene Ontology: tool for the unification of biology. Nat. Genet, 25: 25-9.
Bao Y., Wang L., Shi L., Yun F., Liu X., Chen Y., Chen C., Ren Y. and Jia Y. (2019) Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer. Cell Mol Biol Lett, 24(38): 2-20.
Board P. G, Coggan M., Baker R. T., Vuust J. and Webb G. C. (1992) Localization of the human UBC polyubiquitin gene to chromosome band 12q24.3. Genomics, 12: 639-42.
Bolger A. M., Lohse M. and Usadel B. (2014) Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, 30: 2114-20.
Castello L. M., Raineri D., Salmi L., Clemente N. and Vaschetto R. (2017) Osteopontin at the Crossroads of Inflammation and Tumor Progression. Mediators Inflamm, 22.
Cho A., Howell V. M. and Colvin E. K. (2015) The Extracellular Matrix in Epithelial Ovarian Cancer. A Piece of a Puzzle. Front. Oncol, 5: 245.
Dasgupta S., Wasson L. M., Rauniyar N., Prokai L. and Borejdo J. (2009) Novel gene C17orf37 in 17q12 amplicon promotes migration and invasion of prostate cancer cells. Oncogene, 28: 2860-2872.
De Cristofaro T., Di Palma T., Soriano A., Monticelli A. and Affinito O. (2016) Candidate genes and pathways downstream of PAX8 involved in ovarian high-grade serous carcinoma. J. Oncotarget, 7: 41929-41947.
Evans E. E., Henn A. D., Jonason A., Paris M. J. and Schiffhauer L. M. (2006) C35 (C17orf37) is a novel tumor biomarker abundantly expressed in breast cancer. Mol Cancer Ther, 11: 2919-30S.
Hao J. M, Chen J. Z., Sui H. M., Si-Ma X. Q. and Li GQ. (2010) A five-gene signature as a potential predictor of metastasis and survival in colorectal cancer. J. Pathol, 220: 475-89.
Huang Y. A., You Z. H., Chen X., Chan K. and Luo X. (2016) Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics, 17: 184.
Kanehisa M. and Goto S. (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res, 28: 27-30.
Kim D., Langmead B. and Salzberg S. L. (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods, 12: 357-60.
Kimura Y. and Tanaka K. (2016) Regulatory mechanisms involved in the control of ubiquitin homeostasis. J. Biochem, 147: 793-8.
Krzystyniak J., Ceppi L., Dizon D. S. and Birrer M. J. (2016) Epithelial ovarian cancer: the molecular genetics of epithelial. Ann. Oncol, 27: i4-i10.
Li M. X., Jin L. T., Wang T. J., Feng Y. J., Pan C. P. and Zhao D. M. (2018) Identification of potential core genes in triple negative breast cancer using bioinformatics analysis. Onco. Targets Ther, 11: 4105-4112.
Li Y., Xiao X., Ji X., Liu B. and Amos C. I. (2015) RNA-seq analysis of lung adenocarcinomas reveals different gene expression profiles between smoking and nonsmoking patients. Tumour. Biol, 36: 8993-9003.
Loghmani H., Noruzinia M., Abdul-Tehrani H., Taghizadeh M. and Karbassiane M. H. (2014) Association of estrogen receptors’ promoter methylation and clinicopathological characteristics in Iranian patients with breast cancer. Molecular and Biochemical diagnosis (MBD), 1: 21-33.
Love M. I., Huber W. and Anders S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15: 550.
Marcato P., Dean C. A., Araslanova R., Gillis M. and Joshi M. (2011) Aldehyde dehydrogenase activity of breast cancer stem cells is primarily due to isoform ALDH1A3 and its expression is predictive of metastasis. J. Stem. Cells, 29: 32-45.
Shevde L. A. and Samant R. S. (2014) Role of osteopontin in the pathophysiology of cancer. Matrix Biol, 37: 131–141.
Thor A. D., Young R. H. and Clement P. B. (1991) Pathology of the fallopian tube, broad ligament, peritoneum, and pelvic soft tissues. Hum. Pathol, 22: 856-867.
Torre L. A., Trabert B., DeSantis C. E., Miller K. D. and Samimi G. (2018) Ovarian cancer statistics. CA. Cancer J. Clin, 68: 284-296.
Wang S., Zhou L., Han L. and Yuan Y. (2014) Expression and purification of non-tagged recombinant mouse SPP1 in E. coli and its biological significance. Bioengineered, 5: 405-408.
Warburg O. (1956) On respiratory impairment in cancer cells. Science, 124: 269-70.
Wu B., Xie J., Du Z., Wu J. and Zhang P. (2014) PPI network analysis of mRNA expression profile of ezrin knockdown in esophageal squamous cell carcinoma. Biomed Res Int, 3: 651954.
Zeng B., Min Z., Wu H. and Zhengai X. (2018) SPP1 promotes ovarian cancer progression via Integrin β1/FAK/AKT signaling pathway. Onco Targets Ther, 11: 1333-1343.
Zhang X, Wang Y. (2019) Identification of hub genes and key pathways associated with the progression of gynecological cancer. Oncol Lett, 18: 6516-6524.
Zhuo C., Li X., Zhuang H., Tian S. and Cui H. (2016) Elevated THBS2, COL1A2, and SPP1 expression levels as predictors of gastric cancer prognosis. Cell Physiol Biochem, 40: 1316-1324.
How to Cite
SiavoshiA., TaghizadehM., DookheE., PiranM., SalianiM., & Mohammad GanjiS. (2020). Network Analysis of Differential Gene Expression to Identify Hub Genes in Ovarian Cancer. Journal of Cell and Molecular Research, 12(1), 1-9. https://doi.org/10.22067/jcmr.v12i1.85654
Section
Research Articles