Meta- and gene set analysis of stomach cancer gene expression data

被引:0
|
作者
Kim, Seon-Young
Kim, Jeong-Hwan
Lee, Heun-Sik
Noh, Seung-Moo
Song, Kyu-San
Cho, June-Sik
Jeong, Hyun-Yong
Kim, Woo Ho
Yeom, Young-Il
Kim, Nam-Soon
Kim, Sangsoo
Yoo, Hyang-Sook
Kim, Yong Sung [1 ]
机构
[1] Korea Res Inst Biosci & Biotechnol, Genome Res Ctr, Human Genom Lab, Taejon 305806, South Korea
[2] Chungnam Natl Univ, Coll Med, Dept Pathol, Taejon 301747, South Korea
[3] Chungnam Natl Univ, Coll Med, Dept Gen Surg, Taejon 301747, South Korea
[4] Chungnam Natl Univ, Coll Med, Dept Diagnost Radiol, Taejon 301747, South Korea
[5] Chungnam Natl Univ, Coll Med, Dept Internal Med, Taejon 301747, South Korea
[6] Seoul Natl Univ, Coll Med, Dept Pathol, Seoul 110799, South Korea
[7] Korea Res Inst Biosci & Biotechnol, Natl Genome Informat Ctr, Taejon 305806, South Korea
[8] Soongsil Univ, Dept Bioinformat, Seoul 156743, South Korea
[9] Korea Res Inst Biosci & Biotechnol, Century Frontier R&D Program 21st, Ctr Funct Anal Human Genome, Taejon 305806, South Korea
关键词
database; gastric cancer; gene set; metaanalysis; microarray;
D O I
暂无
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
We generated gene expression data from the tissues of 50 gastric cancer patients, and applied meta-analysis and gene set analysis to this data and three other stomach cancer gene expression data sets to define the gene expression changes in gastric tumors. By meta-analysis we identified genes consistently changed in gastric carcinomas, while gene set analysis revealed consistently changed biological themes. Genes and gene sets involved in digestion, fatty acid metabolism, and ion transport were consistently down-regulated in gastric carcinomas, while those involved in cellular proliferation, cell cycle, and DNA replication were consistently up-regulated. We also found significant differences between the genes and gene sets expressed in diffuse and intestinal type gastric carcinoma. By gene set analysis of cytogenetic bands, we identified many chromosomal regions with possible gross chromosomal changes (amplifications or deletions). Similar analysis of transcription factor binding sites (TFBSs), revealed transcription factors that may have caused the observed gene expression changes in gastric carcinomas, and we confirmed the overexpression of one of these, E2F1, in many gastric carcinomas by tissue array and immunohistochemistry. We have incorporated the results of our meta and gene set analyses into a web accessible database.
引用
收藏
页码:200 / 209
页数:10
相关论文
共 50 条
  • [1] Gene set analysis for longitudinal gene expression data
    Zhang, Ke
    Wang, Haiyan
    Bathke, Arne C.
    Harrar, Solomon W.
    Piepho, Hans-Peter
    Deng, Youping
    BMC BIOINFORMATICS, 2011, 12
  • [2] Gene set analysis for longitudinal gene expression data
    Ke Zhang
    Haiyan Wang
    Arne C Bathke
    Solomon W Harrar
    Hans-Peter Piepho
    Youping Deng
    BMC Bioinformatics, 12
  • [3] Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE)
    Meng, Hailong
    Yaari, Gur
    Bolen, Christopher R.
    Avey, Stefan
    Kleinstein, Steven H.
    PLOS COMPUTATIONAL BIOLOGY, 2019, 15 (04) : 1 - 10
  • [4] In silico analysis of stomach lineage specific gene set expression pattern in gastric cancer
    Pandi, Narayanan Sathiya
    Suganya, Sivagurunathan
    Rajendran, Suriliyandi
    BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, 2013, 439 (04) : 539 - 546
  • [5] Gene Set Correlation Analysis and Visualization Using Gene Expression Data
    Tsai, Chen-An
    Chen, James J.
    CURRENT BIOINFORMATICS, 2021, 16 (03) : 406 - 421
  • [6] Analysis of Gene Expression Cancer Data Set: Classification of TCGA Pan-cancer HiSeq Data
    Nitta, Yusaku
    Borders, Mitchell
    Ludwig, Simone A.
    2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 4745 - 4752
  • [7] Time-Course Gene Set Analysis for Longitudinal Gene Expression Data
    Hejblum, Boris P.
    Skinner, Jason
    Thiebaut, Rodolphe
    PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (06)
  • [8] The effect of LTA gene polymorphisms on cancer risk: an updated systematic review and meta- analysis
    Li, Jingdong
    Wang, Yaxuan
    Chang, Xueliang
    Han, Zhenwei
    BIOSCIENCE REPORTS, 2020, 40
  • [9] Conducting gene set tests in meta-analyses of transcriptome expression data
    Kosch, Robin
    June, Klaus
    RESEARCH SYNTHESIS METHODS, 2019, 10 (01) : 99 - 112
  • [10] FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data
    Manijak M.P.
    Nielsen H.B.
    BMC Research Notes, 4 (1)