A network-pathway based module identification for predicting the prognosis of ovarian cancer patientsReport as inadecuate

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Journal of Ovarian Research

, 9:73

First Online: 02 November 2016Received: 28 August 2016Accepted: 25 October 2016


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

MethodsTwo 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.

ResultsThe 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.

ConclusionsThe 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.

KeywordsOvarian cancer Reactome functional interactions Markov clustering Supervised principal components Prognosis  Download fulltext PDF

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

Source: https://link.springer.com/

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