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Original Research

Open Access

Genes associated with platinum-resistance in platinum-resistant ovarian cell line

  • Yang Yang1
  • Lu Chen1
  • Liwei Xu1
  • Zhenlin Fan2
  • Yunsong Lu3
  • Tingting Ji4
  • Hui Wang5,*,

1Department of Tumor & Blood Disease, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun

2Department of Rehabilitation Medicine, Jilin Province People’s Hospital, Changchun

3Departentment of Spine, Changchun Orthopaedics Hospital, Changchun

4Graduate School of Changchun University of Traditional Chinese Medicine, Changchun

5Department of Gynecology, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun (China)

DOI: 10.12892/ejgo3915.2018 Vol.39,Issue 2,April 2018 pp.257-264

Published: 10 April 2018

*Corresponding Author(s): Hui Wang E-mail: wangh.ui@163.com

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Abstract

Background: In this study the authors aimed to identify genes associated with platinum-resistance in platinum-resistant ovarian cell line. Materials and Methods: The transcriptome profile dataset ERA033498 was downloaded. Clean sequencing reads were mapped to human hg19 genome using TopHat. After filtering, reads were then annotated using ANNOVAR tool. Gene expression level was estimated by the value of Fragments Per Kilobase of transcript per Million mapped reads (FPKM) using Cufflinks. R/Limma package was used to identify differentially expressed genes (DEGs) between the platinum-sensitive and -resistant cell lines in each patient, with the threshold of |log2(fold change)|> 2 and q-value < 0.05. Then, functional annotation and pathway enrichment analysis of DEGs was performed. A protein-protein interaction (PPI) network was also built using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), followed by module analysis, and further functional annotation of the sub-network genes. Results: Quality sequencing reads was collected from each sample, with the lowest alignment rate of 95.38%. Totally, 304 DEGs were commonly identified from the two patients, including 247 up-regulated. Four IFIT family members were observed to be up-regulated. STAT1, MX1, DDX58, XAF1, and IFIH1 were the top five hubs with the highest connection degrees in the PPI network. STAT1 may affect platinum-resistance via influenza A, herpes simplex infection, hepatitis C and measles pathways; IFIH1 may function via RIG-I-like receptor signaling pathway, influenza A, herpes simplex infection, and measles pathways; IFIT1 via the herpes simplex infection and hepatitis C pathways. Conclusions: STAT1, IFIH1, and the four IFIT family members may be associated with platinum-resistance in ovarian cancer cells.

Keywords

Ovarian cancer; Platinum-resistance; Functional annotation and pathway enrichment analysis; Protein-protein interaction network; Module analysis.

Cite and Share

Yang Yang, Lu Chen, Liwei Xu, Zhenlin Fan, Yunsong Lu, Tingting Ji, Hui Wang. Genes associated with platinum-resistance in platinum-resistant ovarian cell line. European Journal of Gynaecological Oncology. 2018; 39(2): 257-264. doi: 10.12892/ejgo3915.2018

References

[1] Siegel R., Naishadham D., Jemal A.: “Cancer statistics, 2013”. CA Cancer J. Clin., 2013, 63, 11.

[2] Krämer O.H., Knauer S.K., Greiner G., Jandt E., Reichardt S., Gührs K-H. et al.: “A phosphorylation-acetylation switch regulates STAT1 signaling”. Genes Dev., 2009, 23, 223.

[3] Du Bois A., Quinn M., Thigpen T., Vermorken J., Avall-Lundqvist E., Bookman M., et al.: “2004 consensus statements on the management of ovarian cancer: final document of the 3rd International Gynecologic Cancer Intergroup Ovarian Cancer Consensus Conference (GCIG OCCC 2004)”. Ann. Oncol., 2005, 16, viii7.

[4] Siddik Z.H.: “Cisplatin: mode of cytotoxic action and molecular basis of resistance”. Oncogene, 2003, 22, 7265.

[5] Rabik C.A., Dolan M.E.: “Molecular mechanisms of resistance and toxicity associated with platinating agents”. Cancer Treat. Rev., 2007, 33, 9.

[6] Holzer A.K., Manorek G.H., Howell S.B.: “Contribution of the major copper influx transporter CTR1 to the cellular accumulation of cisplatin, carboplatin, and oxaliplatin”. Mol. Pharmacol., 2006, 70, 1390.

[7] Samimi G., Katano K., Holzer A.K., Safaei R., Howell S.B.: “Modulation of the cellular pharmacology of cisplatin and its analogs by the copper exporters ATP7A and ATP7B”. Mol. Pharmacol., 2004, 66, 25.

[8] Nakayama K., Kanzaki A., Ogawa K., Miyazaki K., Neamati N., Takebayashi Y.: “Copper-transporting P-type adenosine triphosphatase (ATP7B) as a cisplatin based chemoresistance marker in ovarian carcinoma: Comparative analysis with expression of MDR1, MRP1, MRP2, LRP and BCRP”. Int. J. Cancer, 2002, 101, 488.

[9] Konstantinopoulos P.A., Spentzos D., Cannistra S.A.: “Gene-expression profiling in epithelial ovarian cancer”. Nat. Clin. Pract. Oncol., 2008, 5, 577.

[10] Li M., Balch C., Montgomery J.S., Jeong M., Chung J.H., Yan P., et al.: “Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer”. BMC Med. Genomics, 2009, 2, 34.

[11] Berchuck A., Iversen E.S., Lancaster J.M., Pittman J., Luo J., Lee P., et al.: “Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers”. Clin. Cancer Res., 2005, 11, 3686.

[12] Hartmann L.C., Lu K.H., Linette G.P., Cliby W.A., Kalli K.R., Gershenson D., et al.: “Gene expression profiles predict early relapse in ovarian cancer after platinum-paclitaxel chemotherapy”. Clin. Cancer Res., 2005, 11, 2149.

[13] Trapnell C., Pachter L., Salzberg S.L.: “TopHat: discovering splice junctions with RNA-Seq”. Bioinformatics, 2009, 25, 1105.

[14] Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G., van Baren M.J., et al.: “Transcript assembly and quantification by RNASeq reveals unannotated transcripts and isoform switching during cell differentiation”. Nat. Biotechnol., 2010, 28, 511.

[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] Da Wei Huang B.T.S., Lempicki R.A.: “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources”. Nat. Protoc., 2008, 4, 44.

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

[18] Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., et al.: “Cytoscape: a software environment for integrated models of biomolecular interaction networks”. Genome Res., 2003, 13, 2498.

[19] Diamond M.S., Farzan M.: “The broad-spectrum antiviral functions of IFIT and IFITM proteins”. Nat Rev Immunol 2012;13:46-57.

[20] Stronach E.A., Alfraidi A., Rama N., Datler C., Studd J.B., Agarwal R., et al.: “HDAC4-regulated STAT1 activation mediates platinum resistance in ovarian cancer”. Cancer Res., 2011, 71, 4412.

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