Literature-mining and bioinformatic analysis of androgen-independent prostate cancer-specific genes.
- Author:
Tie-Qiu LI
1
;
Chun-Qiong FENG
;
Ya-Guang ZOU
;
Rong SHI
;
Shuang LIANG
;
Xiang-Ming MAO
Author Information
- Publication Type:Journal Article
- MeSH: Androgen Antagonists; Androgens; metabolism; Computational Biology; Data Mining; Gene Expression; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes, Neoplasm; Humans; Male; Prostatic Neoplasms; genetics; metabolism
- From: National Journal of Andrology 2009;15(12):1102-1107
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVETo compare the differences of the gene expressions in androgen-independent and androgen-dependent prostate cancer (ADPC), gain a deeper insight into the molecular mechanism of androgen-independent prostate cancer (AIPC), and find effective means for its clinical diagnosis and treatment.
METHODSEats of genes highly-associated with prostate cancer were obtained by mining PubMed with the FACTA tool, and the specifically expressed genes in AIPC were analyzed with a set of bioinformatic tools including GATHER, PANTHER, STRING and ToppGene.
RESULTSA total of 128 genes specifically expressed in AIPC were identified, as compared with 23 that were specific to ADPC. Bioinformatic analysis showed the essential roles of AIPC-specific genes in such important biological processes as cell signal transduction, cell adhesion, apoptosis, oncogenesis, cell proliferation and cell differentiation.
CONCLUSIONSuch genes as MMPJ, EGFR, MMP2, ADM, MIF, IGFBP3, 112, MET, BAD, RHOA, SPP1, EP300, SMAD3, RAE1, PTK2, and TGFB2 may play important roles in transforming ADPC into AIPC.
