Literature DB >> 20180422

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

Tie-Qiu Li1, Chun-Qiong Feng, Ya-Guang Zou, Rong Shi, Shuang Liang, Xiang-Ming Mao.   

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.

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Year:  2009        PMID: 20180422

Source DB:  PubMed          Journal:  Zhonghua Nan Ke Xue        ISSN: 1009-3591


  3 in total

1.  Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data.

Authors:  Chen Chen; Li-Guo Zhang; Jian Liu; Hui Han; Ning Chen; An-Liang Yao; Shao-San Kang; Wei-Xing Gao; Hong Shen; Long-Jun Zhang; Ya-Peng Li; Feng-Hong Cao; Zhi-Guo Li
Journal:  Onco Targets Ther       Date:  2016-03-16       Impact factor: 4.147

2.  Differential DNA Methylation in Prostate Tumors from Puerto Rican Men.

Authors:  Gilberto Ruiz-Deya; Jaime Matta; Jarline Encarnación-Medina; Carmen Ortiz-Sanchéz; Julie Dutil; Ryan Putney; Anders Berglund; Jasreman Dhillon; Youngchul Kim; Jong Y Park
Journal:  Int J Mol Sci       Date:  2021-01-13       Impact factor: 5.923

3.  Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression.

Authors:  Yin Li; Wanwipa Vongsangnak; Luonan Chen; Bairong Shen
Journal:  BMC Med Genomics       Date:  2014-05-08       Impact factor: 3.063

  3 in total

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