A network-pathway based module identification for predicting the prognosis of ovarian cancer patients.
J Ovarian Res
; 9(1): 73, 2016 Nov 02.
Article
em En
| MEDLINE
| ID: mdl-27806724
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Ovarianas
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Biomarcadores Tumorais
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Regulação Neoplásica da Expressão Gênica
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Redes Reguladoras de Genes
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
Idioma:
En
Revista:
J Ovarian Res
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
China