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Journal of Ovarian Research
Published in: Journal of Ovarian Research 1/2016

Open Access 01-12-2016 | Research

A network-pathway based module identification for predicting the prognosis of ovarian cancer patients

Authors: Xin Wang, Shan-shan Wang, Lin Zhou, Li Yu, Lan-mei Zhang

Published in: Journal of Ovarian Research | Issue 1/2016

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Abstract

Background

This study aimed to screen multiple genes biomarkers based on gene expression data for predicting the survival of ovarian cancer patients.

Methods

Two microarray data of ovarian cancer samples were collected from The Cancer Genome Atlas (TCGA) database. The data in the training set were used to construct Reactome functional interactions network, which then underwent Markov clustering, supervised principal components, Cox proportional hazard model to screen significantly prognosis related modules. The distinguishing ability of each module for survival was further evaluated by the testing set. Gene Ontology (GO) functional and pathway annotations were performed to identify the roles of genes in each module for ovarian cancer.

Results

The network based approach identified two 7-gene functional interaction modules (31: DCLRE1A, EXO1, KIAA0101, KIN, PCNA, POLD3, POLD2; 35: DKK3, FABP3, IRF1, AIM2, GBP1, GBP2, IRF2) that are associated with prognosis of ovarian cancer patients. These network modules are related to DNA repair, replication, immune and cytokine mediated signaling pathways.

Conclusions

The two 7-gene expression signatures may be accurate predictors of clinical outcome in patients with ovarian cancer and has the potential to develop new therapeutic strategies for ovarian cancer patients.
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Metadata
Title
A network-pathway based module identification for predicting the prognosis of ovarian cancer patients
Authors
Xin Wang
Shan-shan Wang
Lin Zhou
Li Yu
Lan-mei Zhang
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Journal of Ovarian Research / Issue 1/2016
Electronic ISSN: 1757-2215
DOI
https://doi.org/10.1186/s13048-016-0285-0

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