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Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

Jin N, Wu H, Miao Z, Huang Y, Hu Y, Bi X, Wu D, Qian K, Wang L, Wang C, Wang H, Li K, Li X, Wang D - Sci Rep (2015)

Bottom Line: Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored.In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma.Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

ABSTRACT
Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

No MeSH data available.


Related in: MedlinePlus

12-gene module risk score analysis of ovarian cancer.(A) The distribution of the 12-gene module risk score. Patients were divided into a high-risk group (Red) or a low-risk group (Blue) using the median risk score as the cutoff point. (B) Heatmap of the module genes’ expression profiles. Rows and columns represented genes and patients, respectively.
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f2: 12-gene module risk score analysis of ovarian cancer.(A) The distribution of the 12-gene module risk score. Patients were divided into a high-risk group (Red) or a low-risk group (Blue) using the median risk score as the cutoff point. (B) Heatmap of the module genes’ expression profiles. Rows and columns represented genes and patients, respectively.

Mentions: To identify module biomarkers of ovarian cancer, we first adopted a network-based simulated annealing approach to search putative modules by integrating survival information, PPI network and gene expression. Under the criteria with module score ranked in the top 1% (module score > 6.15) and p < 0.01, a total of 71 modules were identified in the constructed survival and differential co-expression PPI network between longer- versus shorter- survival patients. Then, for each module, we evaluated its predictive ability for survival of ovarian cancer patients, as described in the Materials and Methods. Notably, 27 of 71 modules were found to be significantly associated with overall survival of ovarian cancer patients in the training dataset (p < 0.1). Among all the survival-associated modules, the predictive ability of only a 12-gene module (Fig. 1, Table 1 and Supplementary Table S1), was further confirmed in independent internal dataset (In training dataset, log-rank p = 2.09E-3; In test dataset, log-rank p = 0.014). Gene Ontology functional annotation on the 12-gene module was presented in Supplementary Table S2. The distribution of the module genes’ risk scores and heatmap of the module genes’ expression profiles were shown in Fig. 2.


Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

Jin N, Wu H, Miao Z, Huang Y, Hu Y, Bi X, Wu D, Qian K, Wang L, Wang C, Wang H, Li K, Li X, Wang D - Sci Rep (2015)

12-gene module risk score analysis of ovarian cancer.(A) The distribution of the 12-gene module risk score. Patients were divided into a high-risk group (Red) or a low-risk group (Blue) using the median risk score as the cutoff point. (B) Heatmap of the module genes’ expression profiles. Rows and columns represented genes and patients, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4477367&req=5

f2: 12-gene module risk score analysis of ovarian cancer.(A) The distribution of the 12-gene module risk score. Patients were divided into a high-risk group (Red) or a low-risk group (Blue) using the median risk score as the cutoff point. (B) Heatmap of the module genes’ expression profiles. Rows and columns represented genes and patients, respectively.
Mentions: To identify module biomarkers of ovarian cancer, we first adopted a network-based simulated annealing approach to search putative modules by integrating survival information, PPI network and gene expression. Under the criteria with module score ranked in the top 1% (module score > 6.15) and p < 0.01, a total of 71 modules were identified in the constructed survival and differential co-expression PPI network between longer- versus shorter- survival patients. Then, for each module, we evaluated its predictive ability for survival of ovarian cancer patients, as described in the Materials and Methods. Notably, 27 of 71 modules were found to be significantly associated with overall survival of ovarian cancer patients in the training dataset (p < 0.1). Among all the survival-associated modules, the predictive ability of only a 12-gene module (Fig. 1, Table 1 and Supplementary Table S1), was further confirmed in independent internal dataset (In training dataset, log-rank p = 2.09E-3; In test dataset, log-rank p = 0.014). Gene Ontology functional annotation on the 12-gene module was presented in Supplementary Table S2. The distribution of the module genes’ risk scores and heatmap of the module genes’ expression profiles were shown in Fig. 2.

Bottom Line: Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored.In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma.Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

ABSTRACT
Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

No MeSH data available.


Related in: MedlinePlus