Format

Send to

Choose Destination
J Cancer Res Clin Oncol. 2017 Dec;143(12):2571-2579. doi: 10.1007/s00432-017-2497-0. Epub 2017 Aug 28.

Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach.

Author information

1
Department of Urinary Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
2
Department of Urinary Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, China.
3
National Center for Liver Cancer, Shanghai, 201805, China.
4
Department of Urinary Surgery, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
5
BGI-Wuhan, Wuhan BGI Clinical Laboratory Limited Company, Wuhan, 430075, Hubei, China.
6
Department of Urinary Surgery, Third Affiliated Hospital, Second Military Medical University, Shanghai, 200438, China. xingangcui@126.com.
7
Department of Urinary Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China. danfengxu_urology@163.com.
8
Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China. dr.wenhuiliu@hotmail.com.

Abstract

PURPOSE:

Prostate cancer is one of the leading causes of cancer death for male. In the present study, we applied an integrated bioinformatics approach to provide a novel perspective and identified some hub genes of prostate cancer.

METHOD:

Microarray data of fifty-nine prostate cancer were downloaded from Gene Expression Omnibus. Gene Ontology and pathway analysis were applied for differentially expressed genes between high and low grade prostate cancer. Weighted gene coexpression network analysis was applied to construct gene network and classify genes into different modules. The most related module to high grade prostate cancer was identified and hub genes in the module were revealed. Ingenuity pathway analysis was applied to check the chosen module's relationship to high grade prostate cancer. Hub gene's expression profile was verified with clinical samples and a dataset from The Cancer Genome Atlas project.

RESULT:

3193 differentially expressed genes were filtered and gene ontology and pathway analysis revealed some cancer- and sex hormone-related results. Weighted gene coexpression network was constructed and genes were classified into six modules. The red module was selected and ingenuity pathway analysis confirmed its relationship with high grade prostate cancer. Hub genes were identified and their expression profile was also confirmed.

CONCLUSION:

The present study applied integrate bioinformatics approaches to generate a holistic view of high grade prostate cancer and identified hub genes could serve as prognosis markers and potential treatment targets.

KEYWORDS:

Gene ontology; Hub gene; Pathway analysis; Prognostic marker; Prostate cancer; WGCNA

PMID:
28849390
DOI:
10.1007/s00432-017-2497-0
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center