title: Highly and moderately aggressive mouse ovarian cancer cell lines exhibit differential gene expression.
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.
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Fengkun
authors.initials: F
authors.lastname: Du
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.|College of Chemistry & Environmental Science, Hebei University, Baoding, Hebei Province 071002, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Wensheng
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Shubha P
authors.initials: SP
authors.lastname: Kale
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Harris
authors.initials: H
authors.lastname: McFerrin
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Ian
authors.initials: I
authors.lastname: Davenport
authors.affiliation: Department of Chemistry, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Guangdi
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Elena
authors.initials: E
authors.lastname: Skripnikova
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Xiao-Lin
authors.initials: XL
authors.lastname: Li
authors.affiliation: Department of Biology Sciences, Clark Atlanta University, 23 James P. Brawley Drive, SW, Atlanta, GA 30314, USA.
authors.forename: Nathan J
authors.initials: NJ
authors.lastname: Bowen
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Leticia B
authors.initials: LB
authors.lastname: McDaniels
authors.affiliation: Department of Family Medicine, Morehouse School of Medicine, 1513 E. Cleveland Ave. Building 100, East Point, GA 30344, USA.
authors.forename: Yuan-Xiang
authors.initials: YX
authors.lastname: Meng
authors.affiliation: Research Core Facility, LSUHSC Health Sciences Center - Shreveport, 1501 Kings Hwy, Shreveport, LA 71103, USA.
authors.forename: Paula
authors.initials: P
authors.lastname: Polk
authors.affiliation: Department of Basic Pharmaceutical Sciences, University of Louisiana at Monroe, 700 University Avenue, Monroe, LA 71209, USA.
authors.forename: Yong-Yu
authors.initials: YY
authors.lastname: Liu
authors.affiliation: Department of Biology, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.
authors.forename: Qian-Jin
authors.initials: QJ
authors.lastname: Zhang
mesh_terms: D000818:Animals; D014408:Biomarkers, Tumor / Q000235:genetics*; D000077216:Carcinoma, Ovarian Epithelial; D004195:Disease Models, Animal; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D051379:Mice; D008810:Mice, Inbred C57BL; D009375:Neoplasms, Glandular and Epithelial / Q000235:genetics* / Q000473:pathology*; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000473:pathology*; D060888:Real-Time Polymerase Chain Reaction; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor
keywords: Bioinformatics; Ovarian cancer; Prognostic biomarkers