[Literature-mining and bioinformatic analysis of androgen-independent prostate cancer-specific genes].

Abstract

OBJECTIVE:

To 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.

METHODS:

Eats 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.

RESULTS:

A 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.

CONCLUSION:

Such 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.

• Authors: Tie-Qiu Li, Chun-Qiong Feng, Ya-Guang Zou, Rong Shi, Shuang Liang, Xiang-Ming Mao
• Journal: Zhonghua nan ke xue = National journal of andrology (Zhonghua Nan Ke Xue) Vol. 15 Issue 12 Pg. 1102-7 (Dec 2009) ISSN: 1009-3591 [Print] China
• PMID: 20180422 (Publication Type: English Abstract, Journal Article)

Chemical References

• Androgen Antagonists
• Androgens

Topics

• 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)