Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer. (December 2015)

• Record Type: Journal Article
• Authors: Kwon, Min-Seok; Kim, Yongkang; Lee, Seungyeoun; Namkung, Junghyun; Yun, Taegyun; Yi, Sung; Han, Sangjo; Kang, Meejoo; Kim, Sun; Jang, Jin-Young; Park, Taesung
• Is Part Of: BMC genomics. Volume 16:Number 9(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/1471-2164-16-S9-S4
• Languages: English
• ISSNs: 1471-2164

Abstract

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) 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.