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

Min-Seok Kwon, Yongkang Kim, Seungyeoun Lee, Junghyun Namkung, Taegyun Yun, Sung Yi, Sangjo Han, Meejoo Kang, Sun Kim, Jin-Young Jang, Taesung Park
2015 BMC Genomics  
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
more » ... ave-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.
doi:10.1186/1471-2164-16-s9-s4 pmid:26328610 pmcid:PMC4547403 fatcat:j4ibronbbjbtxpeb4rq2di4cce