Characterize the difference between TMPRSS2-ERG and non-TMPRSS2-ERG fusion patients by clinical and biological characteristics in prostate cancer

被引：2

作者：

Wang, Shiyuan ^{[1
]}

Zhang, Qi ^{[1
]}

Xu, Dandan ^{[2
]}

Pan, Yi ^{[1
]}

Lv, Yingli ^{[1
]}

Chen, Xiaowen ^{[1
]}

Zuo, Yongchun ^{[3
]}

Yang, Lei ^{[1
]}

机构：

[1] Harbin Med Univ, Coll Bioinformat Sci & Technol, Harbin 150081, Heilongjiang, Peoples R China

[2] Harbin Univ, Fac Sci, Dept Biol, Harbin 150086, Heilongjiang, Peoples R China

[3] Inner Mongolia Univ, Coll Life Sci, State Key Lab Reprod Regulat & Breeding Grassland, Hohhot 010070, Peoples R China

来源：

GENE | 2018年 / 679卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Prostate cancer; TMPRSS2-ERG fusion; Survival analysis; Cox regression; Network; PREDICT SUBCELLULAR-LOCALIZATION; ETS TRANSCRIPTION FACTORS; RECOMBINATION SPOTS; GENE; EXPRESSION; SITES; PROTEINS; INFORMATION; RECURRENCE; SEQUENCE;

D O I：

10.1016/j.gene.2018.09.006

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

The TMPRSS2-ERG gene fusion were frequently found in prostate cancer, and thought to play some fundamental mechanisms for the development of prostate cancer. However, until now, the clinical and prognostic significance of TMPRSS2-ERG gene fusion was not fully understood. In this study, based on the 281 prostate cancers that constructed from a historical watchful waiting cohort, the statistically significant associations between TMPRSS2-ERG gene fusion and clinicopathologic characteristics were identified. In addition, the Elastic Net algorithm was used to predict the patients with TMPRSS2-ERG fusion status, and good predictive results were obtained, indicating that this algorithm was suitable to this prediction problem. The differential gene network was constructed from the network, and the KEGG enrichment analysis demonstrated that the module genes were significantly enriched in several important pathways.

引用

页码：186 / 194

页数：9

共 73 条

[51] Molecular sampling of prostate cancer: a dilemma for predicting disease progression [J].

Sboner, Andrea ;

Demichelis, Francesca ;

Calza, Stefano ;

Pawitan, Yudi ;

Setlur, Sunita R. ;

Hoshida, Yujin ;

Perner, Sven ;

Adami, Hans-Olov ;

Fall, Katja ;

Mucci, Lorelei A. ;

Kantoff, Philip W. ;

Stampfer, Meir ;

Andersson, Swen-Olof ;

Varenhorst, Eberhard ;

Johansson, Jan-Erik ;

Gerstein, Mark B. ;

Golub, Todd R. ;

Rubin, Mark A. ;

Andren, Ove .

BMC MEDICAL GENOMICS, 2010, 3

[52] ETS transcription factors and their emerging roles in human cancer [J].

Seth, A ;

Watson, DK .

EUROPEAN JOURNAL OF CANCER, 2005, 41 (16) :2462-2478

[53]

Simon N, 2011, J STAT SOFTW, V39, P1

[54]

Song J., 2018, BRIEF BIOINFORM

[55] PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy [J].

Song, Jiangning ;

Li, Fuyi ;

Leier, Andr ;

Marquez-Lago, Tatiana T. ;

Akutsu, Tatsuya ;

Haffari, Gholamreza ;

Chou, Kuo-Chen ;

Webb, Geoffrey I. ;

Pike, Robert N. .

BIOINFORMATICS, 2018, 34 (04) :684-687

[56] PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework [J].

Song, Jiangning ;

Li, Fuyi ;

Takemoto, Kazuhiro ;

Haffari, Gholamreza ;

Akutsu, Tatsuya ;

Chou, Kuo-Chen ;

Webb, Geoffrey I. .

JOURNAL OF THEORETICAL BIOLOGY, 2018, 443 :125-137

[57]

St John Jason, 2012, J Cancer Sci Ther, V4, P94

[58]

Su Z. D., 2018, BIOINFORMATICS

[59] ETS fusion genes in prostate cancer [J].

Tandefelt, Delila Gasi ;

Boormans, Joost ;

Hermans, Karin ;

Trapman, Jan .

[60] HBPred: a tool to identify growth hormone-binding proteins [J].

Tang, Hua ;

Zhao, Ya-Wei ;

Zou, Ping ;

Zhang, Chun-Mei ;

Chen, Rong ;

Huang, Po ;

Lin, Hao .

INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES, 2018, 14 (08) :957-964

← 1 2 3 4 5 6 7 8 →