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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

Overlap and wiring diagram between the regulatory genes and cell death genes.(A) Venn plot, showing a nonrandom amount of overlap between the 151 significantly regulatory genes and 727 cell death genes. (B) The interaction wiring of 21 cell death genes and their wiring connections on the 12-gene module. The nodes in red or blue indicated whether the genes have been verified as being related to ovarian cancer or not, respectively. The node sizes indicated the significance of the genes in regulating the 12-gene module.
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f4: Overlap and wiring diagram between the regulatory genes and cell death genes.(A) Venn plot, showing a nonrandom amount of overlap between the 151 significantly regulatory genes and 727 cell death genes. (B) The interaction wiring of 21 cell death genes and their wiring connections on the 12-gene module. The nodes in red or blue indicated whether the genes have been verified as being related to ovarian cancer or not, respectively. The node sizes indicated the significance of the genes in regulating the 12-gene module.

Mentions: Notably, cell death, an established cancer hallmark, might serve as a promising candidate in prevention and treatment of ovarian cancer. We further explored the relationships between the 12-gene module biomarker and cell death genes from our miRDeathDB2728, and HADB29 and DeathBase30. A nonrandom amount of overlap was observed between the 151 significantly regulating genes and 727 cell death genes (pā€‰=ā€‰1.12E-5, hypergeometric test; Fig. 4A), suggesting potential clinical benefit for tumor suppression via regulating cell death. For example, STAT3, contributing to oncogenesis by inhibition of apoptosis, interacts with LCK leading to T-cell transformation by Herpesvirus saimiri (HVS)38. Specifically, 18 of 21 overlapping genes were found to be tightly clustered together pointing to the module biomarker (Fig. 4B). Moreover, majority of the overlapping genes were known to be cancer genes, whose close association with ovarian cancer have been confirmed as presented in detail in Table 3. For example, BCL2L1, as a key protein in regulating programmed cell death or apoptosis, was found to be dysregulated in ovarian cancer cell lines and specimens that promoted cancer progression39. The hsa-miR-335-5p was regarded as an invasion suppressor, whose dysregulation drove cancer transformation by targeting Bcl-w2440.


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)

Overlap and wiring diagram between the regulatory genes and cell death genes.(A) Venn plot, showing a nonrandom amount of overlap between the 151 significantly regulatory genes and 727 cell death genes. (B) The interaction wiring of 21 cell death genes and their wiring connections on the 12-gene module. The nodes in red or blue indicated whether the genes have been verified as being related to ovarian cancer or not, respectively. The node sizes indicated the significance of the genes in regulating the 12-gene module.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Overlap and wiring diagram between the regulatory genes and cell death genes.(A) Venn plot, showing a nonrandom amount of overlap between the 151 significantly regulatory genes and 727 cell death genes. (B) The interaction wiring of 21 cell death genes and their wiring connections on the 12-gene module. The nodes in red or blue indicated whether the genes have been verified as being related to ovarian cancer or not, respectively. The node sizes indicated the significance of the genes in regulating the 12-gene module.
Mentions: Notably, cell death, an established cancer hallmark, might serve as a promising candidate in prevention and treatment of ovarian cancer. We further explored the relationships between the 12-gene module biomarker and cell death genes from our miRDeathDB2728, and HADB29 and DeathBase30. A nonrandom amount of overlap was observed between the 151 significantly regulating genes and 727 cell death genes (pā€‰=ā€‰1.12E-5, hypergeometric test; Fig. 4A), suggesting potential clinical benefit for tumor suppression via regulating cell death. For example, STAT3, contributing to oncogenesis by inhibition of apoptosis, interacts with LCK leading to T-cell transformation by Herpesvirus saimiri (HVS)38. Specifically, 18 of 21 overlapping genes were found to be tightly clustered together pointing to the module biomarker (Fig. 4B). Moreover, majority of the overlapping genes were known to be cancer genes, whose close association with ovarian cancer have been confirmed as presented in detail in Table 3. For example, BCL2L1, as a key protein in regulating programmed cell death or apoptosis, was found to be dysregulated in ovarian cancer cell lines and specimens that promoted cancer progression39. The hsa-miR-335-5p was regarded as an invasion suppressor, whose dysregulation drove cancer transformation by targeting Bcl-w2440.

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