Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method.
| Title: | Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method. |
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| Authors: | Jia Wen, Quitadamo, Andrew, Hall, Benika, Xinghua Shi |
| Source: | BMC Genomics; 2017 Suppl. 7, Vol. 18, p21-30, 10p, 3 Diagrams, 3 Charts, 1 Graph |
| Subject Terms: | COLON cancer, EPISTASIS (Genetics), MICRORNA, NEOPLASTIC cell transformation, PHENOTYPES |
| Abstract: | Background: Colon cancer is a leading cause of worldwide cancer death. It has become clear that microRNAs (miRNAs) play a role in the progress of colon cancer and understanding the effect of miRNAs on tumorigenesis could lead to better prognosis and improved treatment. However, most studies have focused on studying differentially expressed miRNAs between tumor and non-tumor samples or between stages in tumor tissue. Limited work has conducted to study the interactions or epistasis between miRNAs and how the epistasis brings about effect on tumor progression. In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas. Results: We develop a workflow composed of multiple steps for feature selection based on the Empirical Bayesian Elastic Net (EBEN) method. First, we identify the main effects using a model with only main effect on the phenotype. Second, a corrected phenotype is calculated by removing the significant main effect from the original phenotype. Third, we select features with epistatic effect on the corrected phenotype. Finally, we run the full model with main and epistatic effects on the previously selected main and epistatic features. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer. Many of miRNAs with main effect on colon cancer have been previously reported to be associated with colon cancer, and the majority of the epistatic miRNAs share common target genes that could explain their epistasis effect on the pathological stages of colon cancer. We also find many of the target genes of detected miRNAs are associated with colon cancer. Go Ontology Enrichment Analysis of the experimentally validates targets of main and epistatic miRNAs, shows that these target genes are enriched for biological processes associated with cancer progression. Conclusion: Our results provide a set of candidate miRNAs associated with colon cancer progression that could have potential translational and therapeutic utility. Our analysis workflow offers a new opportunity to efficiently explore epistatic interactions among genetic and epigenetic factors that could be associated with human diseases. Furthermore, our workflow is flexible and can be applied to analyze the main and epistatic effect of various genetic and epigenetic factors on a wide range of phenotypes. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jia+Wen%22">Jia Wen</searchLink><br /><searchLink fieldCode="AR" term="%22Quitadamo%2C+Andrew%22">Quitadamo, Andrew</searchLink><br /><searchLink fieldCode="AR" term="%22Hall%2C+Benika%22">Hall, Benika</searchLink><br /><searchLink fieldCode="AR" term="%22Xinghua+Shi%22">Xinghua Shi</searchLink> – Name: TitleSource Label: Source Group: Src Data: BMC Genomics; 2017 Suppl. 7, Vol. 18, p21-30, 10p, 3 Diagrams, 3 Charts, 1 Graph – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22COLON+cancer%22">COLON cancer</searchLink><br /><searchLink fieldCode="DE" term="%22EPISTASIS+%28Genetics%29%22">EPISTASIS (Genetics)</searchLink><br /><searchLink fieldCode="DE" term="%22MICRORNA%22">MICRORNA</searchLink><br /><searchLink fieldCode="DE" term="%22NEOPLASTIC+cell+transformation%22">NEOPLASTIC cell transformation</searchLink><br /><searchLink fieldCode="DE" term="%22PHENOTYPES%22">PHENOTYPES</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Colon cancer is a leading cause of worldwide cancer death. It has become clear that microRNAs (miRNAs) play a role in the progress of colon cancer and understanding the effect of miRNAs on tumorigenesis could lead to better prognosis and improved treatment. However, most studies have focused on studying differentially expressed miRNAs between tumor and non-tumor samples or between stages in tumor tissue. Limited work has conducted to study the interactions or epistasis between miRNAs and how the epistasis brings about effect on tumor progression. In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas. Results: We develop a workflow composed of multiple steps for feature selection based on the Empirical Bayesian Elastic Net (EBEN) method. First, we identify the main effects using a model with only main effect on the phenotype. Second, a corrected phenotype is calculated by removing the significant main effect from the original phenotype. Third, we select features with epistatic effect on the corrected phenotype. Finally, we run the full model with main and epistatic effects on the previously selected main and epistatic features. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer. Many of miRNAs with main effect on colon cancer have been previously reported to be associated with colon cancer, and the majority of the epistatic miRNAs share common target genes that could explain their epistasis effect on the pathological stages of colon cancer. We also find many of the target genes of detected miRNAs are associated with colon cancer. Go Ontology Enrichment Analysis of the experimentally validates targets of main and epistatic miRNAs, shows that these target genes are enriched for biological processes associated with cancer progression. Conclusion: Our results provide a set of candidate miRNAs associated with colon cancer progression that could have potential translational and therapeutic utility. Our analysis workflow offers a new opportunity to efficiently explore epistatic interactions among genetic and epigenetic factors that could be associated with human diseases. Furthermore, our workflow is flexible and can be applied to analyze the main and epistatic effect of various genetic and epigenetic factors on a wide range of phenotypes. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of BMC Genomics is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s12864-017-4130-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 21 Subjects: – SubjectFull: COLON cancer Type: general – SubjectFull: EPISTASIS (Genetics) Type: general – SubjectFull: MICRORNA Type: general – SubjectFull: NEOPLASTIC cell transformation Type: general – SubjectFull: PHENOTYPES Type: general Titles: – TitleFull: Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jia Wen – PersonEntity: Name: NameFull: Quitadamo, Andrew – PersonEntity: Name: NameFull: Hall, Benika – PersonEntity: Name: NameFull: Xinghua Shi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 10 Text: 2017 Suppl. 7 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 14712164 Numbering: – Type: volume Value: 18 Titles: – TitleFull: BMC Genomics Type: main |
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