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Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

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dc.contributor.authorKwon, Min-Seok-
dc.contributor.authorKim, Yongkang-
dc.contributor.authorLee, Seungyeoun-
dc.contributor.authorNamkung, Junghyun-
dc.contributor.authorYun, Taegyun-
dc.contributor.authorYi, Sung Gon-
dc.contributor.authorHan, Sangjo-
dc.contributor.authorKang, Meejoo-
dc.contributor.authorKim, Sun Whe-
dc.contributor.authorJang, Jin-Young-
dc.contributor.authorPark, Taesung-
dc.date.accessioned2017-02-07T06:15:12Z-
dc.date.available2017-02-07T06:15:12Z-
dc.date.issued2015-08-17-
dc.identifier.citationBMC Genomics, 16(Suppl 9):S4ko_KR
dc.identifier.urihttps://hdl.handle.net/10371/100481-
dc.descriptionThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.ko_KR
dc.description.abstractAbstract

Background
microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies.


Methods
In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer.


Results
Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis.


Conclusions
Our prediction models have strong potential for the diagnosis of pancreatic cancer.
ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.titleIntegrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancerko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor권민석-
dc.contributor.AlternativeAuthor김용강-
dc.contributor.AlternativeAuthor이승연-
dc.contributor.AlternativeAuthor남궁정현-
dc.contributor.AlternativeAuthor윤태균-
dc.contributor.AlternativeAuthor이성곤-
dc.contributor.AlternativeAuthor한상조-
dc.contributor.AlternativeAuthor강미주-
dc.contributor.AlternativeAuthor김선휘-
dc.contributor.AlternativeAuthor장진영-
dc.contributor.AlternativeAuthor박태성-
dc.identifier.doi10.1186/1471-2164-16-S9-S4-
dc.language.rfc3066en-
dc.rights.holderKwon et al.;-
dc.date.updated2017-01-06T10:07:09Z-
Appears in Collections:
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
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