Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach.

You Zhou,Fei Yuan,Meng Wang,Changhong Lu,Kai Wu,Jing Yang,Xiangyin Kong,Yu-Dong Cai

doi:10.1155/2015/462363

Abstract

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.

Highlights

The prostate is a gland in the male reproductive system that surrounds the prostatic urethra and affects urinary function
We collected genes related to prostate cancer using the following approaches: (I) 143 reviewed genes were chosen from UniProt [25] using the search terms, “human,” “prostatic cancer,” and “reviewed”; (II) 86 genes were chosen from the TSGene Database (Tumor Suppressor Gene Database, http://bioinfo.mc.vanderbilt.edu/ TSGene/cancer type.cgi [26]) after the Entrez IDs were converted into their official symbols; and (III) 96 genes were retrieved from the NCI (National Cancer Institute, https://gforge.nci.nih.gov, released 2009.6) database [27]
Due to the large number of chemicals, we only considered chemicals with KEGG (Kyoto Encyclopedia of Genes and Genomes) records [41] to reduce search space

Summary

Introduction

The prostate is a gland in the male reproductive system that surrounds the prostatic urethra and affects urinary function. Prostate cancer can affect sexual function, such as erection and ejaculation. Prostate cancer risk is associated with age, family disease history, and race. It is not monogenic; many genes are involved. While it is time consuming and expensive to identify genes or chemicals related to prostate cancer using traditional approaches, the development of computer science can overcome these obstacles by building effective computational methods. We proposed an alternative computational method to identify new candidate genes and chemicals related to prostate cancer. By applying a shortest path approach in the hybrid network, we extracted genes and chemicals related to prostate cancer. Several of the identified genes and chemicals were investigated in related prostate cancer literature

Materials and Methods

Results and Discussion

Conclusions