Oncotarget

Research Papers:

Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network

Tiejun Zhang, Xiaojuan Wang and Zhenyu Yue _

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Oncotarget. 2017; 8:71105-71116. https://doi.org/10.18632/oncotarget.20537

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Abstract

Tiejun Zhang1, Xiaojuan Wang2 and Zhenyu Yue2

1GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong 511436, China

2Institute of Health Sciences, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China

Correspondence to:

Zhenyu Yue, email: [email protected]

Keywords: pancreatic cancer, candidate genes, gene co-expression, protein-protein interaction network, subnetwork extraction algorithm

Received: May 14, 2017     Accepted: July 29, 2017     Published: August 24, 2017

ABSTRACT

Pancreatic cancer (PC) is one of the most common causes of cancer mortality worldwide. As the genetic mechanism of this complex disease is not uncovered clearly, identification of related genes of PC is of great significance that could provide new insights into gene function as well as potential therapy targets. In this study, we performed an integrated network method to discover PC candidate genes based on known PC related genes. Utilizing the subnetwork extraction algorithm with gene co-expression profiles and protein-protein interaction data, we obtained the integrated network comprising of the known PC related genes (denoted as seed genes) and the putative genes (denoted as linker genes). We then prioritized the linker genes based on their network information and inferred six key genes (KRT19, BARD1, MST1R, S100A14, LGALS1 and RNF168) as candidate genes of PC. Further analysis indicated that all of these genes have been reported as pancreatic cancer associated genes. Finally, we developed an expression signature using these six key genes which significantly stratified PC patients according to overall survival (Logrank p = 0.003) and was validated on an independent clinical cohort (Logrank p = 0.03). Overall, the identified six genes might offer helpful prognostic stratification information and be suitable to transfer to clinical use in PC patients.


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