Chinese Journal of Tissue Engineering Research ›› 2010, Vol. 14 ›› Issue (27): 5006-5009.doi: 10.3969/j.issn.1673-8225.2010.27.015

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Analyses of gene sets associated with embryonic stem cells in different histological grades of ovarian cancer

Ye Yun 1,2, Wang Gui-ping 1,3, Yang Xiao-qin1, Yin Xi1, Liang Shuang1, Zheng Wen-ling1, Ma Wen-li1   

  1. 1 Institute of Genetic Engineering, Southern Medical University, Guangzhou   510515, Guangdong Province, China; 2 Department of Biological and Chemical Engineering, Guangxi University of Technology, Liuzhou   545006, Guangxi Zhuang Autonomous Region, China; 3 Nursing School, Guangzhou Medical College, Guangzhou   510182, Guangdong Province, China
  • Online:2010-07-02 Published:2010-07-02
  • Contact: Ma Wen-li, Professor, Doctoral supervisor, Institute of Genetic Engineering, Southern Medical University, Guangzhou 510515, Guangdong Province, China wenli@fimmu.com
  • About author:Ye Yun☆, Studying for doctorate, Lecturer, Institute of Genetic Engineering, Southern Medical University, Guangzhou 510515, Guangdong Province, China; Department of Biological and Chemical Engineering, Guangxi University of Technology, Liuzhou 545006, Guangxi Zhuang Autonomous Region, China yeemvan@126.com
  • Supported by:

    the Biochip Key Laboratory of Guangdong Province, No. 2004B60144*

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Abstract:

BACKGROUND: Recent studies have found that tumor cells have the characteristics of stem cells. Therefore, the gene regulation network that controls the function of stem cells may play important roles in some tumors.
OBJECTIVE: To study the molecular trait of ovary cancer in different grades, and to obtain the expression of gene sets associated with embryonic stem cells in ovary cancer.
METHODS: Gene expression profile data of ovarian cancer (accession number GSE2109) were obtained from Gene Expression Omnibus (GEO) database of National Center for Biotechnology Information. Ovarian cancer samples served as study materials. Samples without clinical data were excluded and then separated into two groups (high differentiation and low differentiation) according to histological grade. Expression value of samples was obtained after raw profile data were normalized and quality control by dChip. Matrix of gene expression was obtained. The enrichment of gene sets, biological process and KEGG pathway were analyzed in ovarian cancer of various differentiations by GSEA software.
RESULTS AND CONCLUSION: A total of 13 gene sets associated with embryonic stem cells, 9 upregulated in poorly differentiated tumors, whereas 4 associated with PRC2 target downregulated. These suggested that gene sets associated with embryonic stem cells upregulated in poorly differentiated tumors. And pathways related cell cycle, cell division and DNA replication also enrich in poorly differentiated tumors. Gene expression profile revealed poorly differentiated ovary tumors possess the similar molecular trait with embryonic stem cells. These analyses may benefit further investigations of early diagnosis and treatment of ovarian cancer.

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