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

Tiejun Zhang,Zhenyu Yue,Xiaojuan Wang

doi:10.18632/oncotarget.20537

Tiejun Zhang, Zhenyu Yue + Show 1 more

Open Access

https://doi.org/10.18632/oncotarget.20537

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Journal: Oncotarget	Publication Date: Aug 24, 2017
Citations: 10	License type: cc-by

Affiliation: Guangzhou Medical University, Anhui University

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.

Highlights

Pancreatic cancer (PC) is a common type of cancer with an estimated 53,070 new cases and 41,780 deaths expected in the United States in 2016 [1]
Numerous events have been found involved in pancreatic tumorigenesis which can be acted as signatures for diagnosis and further clinical application, such as mutations found in well-known cancer genes (KRAS, TP53, SMAD4, and CDKN2A) [3]
This work was aimed at using seed genes and a common biological network containing gene coexpression and protein-protein interaction (PPI) information to discover candidate genes related to the pathogenesis of PC with a subnetwork extraction algorithm

Summary

Introduction

Pancreatic cancer (PC) is a common type of cancer with an estimated 53,070 new cases and 41,780 deaths expected in the United States in 2016 [1]. Several mutations in PIK3CA, PALB2 and TP53 involve in the carcinogenesis of PC. These mutations account for only a fraction of the total PC burden. By the time of diagnosis, pancreatic cancer has often spread through the body due to less symptoms in the disease's early stages. 25% of people survive one year and 5% live for five years after diagnosis [2]. Numerous events have been found involved in pancreatic tumorigenesis which can be acted as signatures for diagnosis and further clinical application, such as mutations found in well-known cancer genes (KRAS, TP53, SMAD4, and CDKN2A) [3]. Despite significant improvements in www.impactjournals.com/oncotarget the early molecular diagnosis and treatment decisions of PC, new therapies and biomarkers are still needed

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