A network-pathway based module identification for predicting the prognosis of ovarian cancer patients.
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.
Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China.
Xin
X
Wang
Outpatient Pharmacy, Outpatient Department, NO.16 Chengzhuang Fengtai Distinct, Beijing, 100071, China.
Shan-Shan
SS
Wang
Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China.
Lin
L
Zhou
Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China.
Li
L
Yu
Department of Gynaecology and Obstetrics, The 306 Hospital of PLA, Beijing, 100037, China. zhangzhlm@hotmail.com.
Lan-Mei
LM
Zhang
D014408:Biomarkers, Tumor; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D053208:Kaplan-Meier Estimate; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000401:mortality*; D011379:Prognosis
D014408:Biomarkers, Tumor
Markov clustering; Ovarian cancer; Prognosis; Reactome functional interactions; Supervised principal components