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

Abstract

BackgroundmicroRNA (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.MethodsIn 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.ResultsUsing 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.ConclusionsOur prediction models have strong potential for the diagnosis of pancreatic cancer.

Network‐based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

“…It is essential and urgent to crack the secret of the carcinogenesis and progression of PC. Although several transcriptomic (including both mRNA and miRNA) studies conducted previously have provided preliminary assessments of gene expression profiles in PC tissues, systematic analysis using network‐based approaches is quite limited, which would allow us for a better understanding of the pathogenesis of PC.…”

“…30 However, the roles of these molecules and their interactions in the regulation of tumorigenesis are not clear. Several integrated analyses of mRNA-miRNA interactions have been reported previously, 24,[31][32][33][34][35][36] which identified new genes and miRNAs contributing to tumor initiation/progression and could help explore the miRNA-mRNA interactions in PC. Almost all of these studies used either cell lines or animal models of PC, or microarray-based methods that have apparent limitations when compared with RNA-seq techniques.…”

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

“…After publication of this article [1] it was brought to the Editors’ attention that there were inaccurate descriptions of validation data in its abstract and list of abbreviations. For the validation, we used the three datasets from Gene Expression Omnibus (GEO), not the Cancer Genome Atlas (TCGA).…”

NMFNA: a non-negative matrix factorization network analysis method for identifying communities and characteristic genes from multi-type pancreatic cancer data

“…Gong et al [5] combined the doubly regularized Cox regression method with the integration of pathway information to detect a comprehensive set of candidate genes with PC survival. Kwon et al [6] introduced the support vector machine to evaluate the diagnostic performance of PC tissues' biomarkers.…”