Format

Send to

Choose Destination
J Clin Med. 2019 Aug 2;8(8). pii: E1160. doi: 10.3390/jcm8081160.

Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis.

Wang CCN1, Li CY2,3, Cai JH1, Sheu PC4, Tsai JJP1, Wu MY5,6, Li CJ7,8, Hou MF9,10,11,12.

Author information

1
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan.
2
Department of Surgery, Show Chwan Memorial Hospital, Changhua 500, Taiwan.
3
Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
4
Department of EECS and BME, University of California, Irvine, CA 92697, USA.
5
Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei 231, Taiwan.
6
Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien 970, Taiwan.
7
Department of Obstetrics and Gynecology, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan. nigel6761@gmail.com.
8
Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan. nigel6761@gmail.com.
9
Division of Breast Surgery, Department of Surgery, Center for Cancer Research,Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung 807, Taiwan. mifeho@kmu.edu.tw.
10
Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan. mifeho@kmu.edu.tw.
11
National Sun Yat-Sen University-Kaohsiung Medical University Joint Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan. mifeho@kmu.edu.tw.
12
National Chiao Tung University-Kaohsiung Medical University Joint Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan. mifeho@kmu.edu.tw.

Abstract

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

KEYWORDS:

GEO; TCGA; breast cancer; prognosis; weighted gene coexpression network analysis

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center