Abstract:
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) data depository. 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.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Kwon MS,Kim Y,Lee S,Namkung J,Yun T,Yi SG,Han S,Kang M,Kim SW,Jang JY,Park Tdoi
10.1186/1471-2164-16-S9-S4subject
Has Abstractpub_date
2015-01-01 00:00:00pages
S4issn
1471-2164pii
1471-2164-16-S9-S4journal_volume
16 Suppl 9pub_type
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