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Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Jeong HH, Leem S, Wee K, Sohn KA - J Ovarian Res (2015)

Bottom Line: Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome.However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Engineering, Ajou University, Suwon, 443-749, Republic of Korea. libe@ajou.ac.kr.

ABSTRACT

Background: Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.

Methods: We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.

Results: We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.

Conclusions: We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

No MeSH data available.


Related in: MedlinePlus

Four-way Venn diagram summarizing the number of shared and unique GO terms enriched in the network from each profile
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Fig7: Four-way Venn diagram summarizing the number of shared and unique GO terms enriched in the network from each profile

Mentions: We first compared the number of enriched GO terms from each constructed network (Fig. 7). The mRNA profile revealed the greatest number of significant terms among the single networks, which was expected. There was no shared GO term between the CNA and methylation profiles, which may suggest distinct functional roles for each profile on clinical outcome. I0.8∃ indicated the greatest number of enriched GO terms with 62 additional BP (Biological Process), 21 CC (Cellular Component), and 11 MF (Molecular Function) terms, which were not found in networks constructed by any of the single genomic profiles. Therefore, the integration of networks may provide a better insight into the gene interaction landscape associated with clinical outcome.Fig. 7


Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Jeong HH, Leem S, Wee K, Sohn KA - J Ovarian Res (2015)

Four-way Venn diagram summarizing the number of shared and unique GO terms enriched in the network from each profile
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4491426&req=5

Fig7: Four-way Venn diagram summarizing the number of shared and unique GO terms enriched in the network from each profile
Mentions: We first compared the number of enriched GO terms from each constructed network (Fig. 7). The mRNA profile revealed the greatest number of significant terms among the single networks, which was expected. There was no shared GO term between the CNA and methylation profiles, which may suggest distinct functional roles for each profile on clinical outcome. I0.8∃ indicated the greatest number of enriched GO terms with 62 additional BP (Biological Process), 21 CC (Cellular Component), and 11 MF (Molecular Function) terms, which were not found in networks constructed by any of the single genomic profiles. Therefore, the integration of networks may provide a better insight into the gene interaction landscape associated with clinical outcome.Fig. 7

Bottom Line: Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome.However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Computer Engineering, Ajou University, Suwon, 443-749, Republic of Korea. libe@ajou.ac.kr.

ABSTRACT

Background: Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.

Methods: We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.

Results: We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.

Conclusions: We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

No MeSH data available.


Related in: MedlinePlus