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Tumor Biology

, Volume 37, Issue 8, pp 11147–11162 | Cite as

Highly and moderately aggressive mouse ovarian cancer cell lines exhibit differential gene expression

  • Fengkun Du
  • Yan Li
  • Wensheng Zhang
  • Shubha P. Kale
  • Harris McFerrin
  • Ian Davenport
  • Guangdi Wang
  • Elena Skripnikova
  • Xiao-Lin Li
  • Nathan J. Bowen
  • Leticia B McDaniels
  • Yuan-Xiang Meng
  • Paula Polk
  • Yong-Yu Liu
  • Qian-Jin Zhang
Original Article

Abstract

Patients with advanced epithelial ovarian cancer often experience disease recurrence after standard therapies, a critical factor in determining their five-year survival rate. Recent reports indicated that long-term or short-term survival is associated with varied gene expression of cancer cells. Thus, identification of novel prognostic biomarkers should be considered. Since the mouse genome is similar to the human genome, we explored potential prognostic biomarkers using two groups of mouse ovarian cancer cell lines (group 1: IG-10, IG-10pw, and IG-10pw/agar; group 2: IG-10 clones 2, 3, and 11) which display highly and moderately aggressive phenotypes in vivo. Mice injected with these cell lines have different survival time and rates, capacities of tumor, and ascites formations, reflecting different prognostic potentials. Using an Affymetrix Mouse Genome 430 2.0 Array, a total of 181 genes were differentially expressed (P < 0.01) by at least twofold between two groups of the cell lines. Of the 181 genes, 109 and 72 genes were overexpressed in highly and moderately aggressive cell lines, respectively. Analysis of the 109 and 72 genes using Ingenuity Pathway Analysis (IPA) tool revealed two cancer-related gene networks. One was associated with the highly aggressive cell lines and affiliated with MYC gene, and another was associated with the moderately aggressive cell lines and affiliated with the androgen receptor (AR). Finally, the gene enrichment analysis indicated that the overexpressed 89 genes (out of 109 genes) in highly aggressive cell lines had a function annotation in the David database. The cancer-relevant significant gene ontology (GO) terms included Cell cycle, DNA metabolic process, and Programmed cell death. None of the genes from a set of the 72 genes overexpressed in the moderately aggressive cell lines had a function annotation in the David database. Our results suggested that the overexpressed MYC and 109 gene set represented highly aggressive ovarian cancer potential biomarkers while overexpressed AR and 72 gene set represented moderately aggressive ovarian cancer potential biomarkers. Based on our knowledge, the current study is first time to report the potential biomarkers relevant to different aggressive ovarian cancer. These potential biomarkers provide important information for investigating human ovarian cancer prognosis.

Keywords

Ovarian cancer Bioinformatics Prognostic biomarkers 

Notes

Acknowledgments

We would like to thank Dr. Erik K. Flemington (Tulane University) for providing software and helping us to analyze microarray data. We also thank Mr. Reginald Starks (Xavier University) for taking care of the animals used in this study. RCMI and LCRC Core Facilities and RCMI Bioinformatics Facility are gratefully acknowledged for providing support for this study.

Grant support

This study was supported by funding from NIH (RCMI, 2G12MD007595) and Louisiana Cancer Research Consortium (LCRC) to Dr. Qian-Jin Zhang.

Authorship

Fengkun Du conducted most of the microarray analysis. Yan Li and Xiao-Lin Li did cell culture and animal works. Shubha P. Kale, Harris McFerrin, Ian Davenport, Guangdi Wang, Yuan-Xiang Meng, and Yong-Yu Liu analyzed data and participated in the many discussions on the findings and follow-up experiments. Elena Skripnikova did RT-PCR analysis. Nathan J. Bowen did part of microarray analysis. Leticia B. McDaniels is undergraduate student who participated in and assisted with the experiments. Paula Polk did microarray.

Compliance with ethical standards

Conflicts of interest

None

Supplementary material

13277_2015_4518_MOESM1_ESM.txt (6.7 mb)
Table 1 (TXT 6,863 kb)
13277_2015_4518_MOESM2_ESM.ppt (4.9 mb)
Fig. 1 (PPT 5,063 kb)
13277_2015_4518_MOESM3_ESM.docx (48 kb)
ESM 1 (DOCX 48 kb)

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Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2016

Authors and Affiliations

  • Fengkun Du
    • 1
  • Yan Li
    • 1
    • 2
  • Wensheng Zhang
    • 1
  • Shubha P. Kale
    • 1
  • Harris McFerrin
    • 1
  • Ian Davenport
    • 1
  • Guangdi Wang
    • 3
  • Elena Skripnikova
    • 1
  • Xiao-Lin Li
    • 1
  • Nathan J. Bowen
    • 4
  • Leticia B McDaniels
    • 1
  • Yuan-Xiang Meng
    • 5
  • Paula Polk
    • 6
  • Yong-Yu Liu
    • 7
  • Qian-Jin Zhang
    • 1
  1. 1.Department of BiologyXavier University of LouisianaNew OrleansUSA
  2. 2.College of Chemistry & Environmental ScienceHebei UniversityBaodingChina
  3. 3.Department of ChemistryXavier University of LouisianaNew OrleansUSA
  4. 4.Department of Biology SciencesClark Atlanta UniversityAtlantaUSA
  5. 5.Department of Family MedicineMorehouse School of MedicineEast PointUSA
  6. 6.Research Core FacilityLSUHSC Health Sciences Center - ShreveportShreveportUSA
  7. 7.Department of Basic Pharmaceutical SciencesUniversity of Louisiana at MonroeMonroeUSA

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