Identification of a 5‑microRNA signature and hub miRNA‑mRNA interactions associated with pancreatic cancer

Abstract

miRNA-gene axes have been reported to serve an important role in the carcinogenesis of pancreatic cancer (PC). The aim of the present study was to systematically identity the microRNA signature and hub molecules, as well as hub miRNA-gene axes, and to explore the potential biomarkers and mechanisms associated with the carcinogenesis of PC. Eleven microRNA profile datasets were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and ArrayExpress databases, and a meta-analysis was performed to identify the differentially expressed miRNAs (DEMs) between tumor tissue and normal tissue. Subsequently, a diagnostic regression model was constructed to identify PC based on The Cancer Genome Atlas (TCGA) miRNA sequence data by using the least absolute shrinkage and selection operator (LASSO) method. In addition, GSE41368 was downloaded, and a weighted gene co-expression network analysis (WGCNA) was performed to obtain the gene module associated with carcinogenesis by using the TCGAbiolinks and WGCNA packages, respectively. Finally, miRNA-gene networks were constructed and visualized using Cytoscape software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A total of 14 DEMs were identified, and a 5-microRNA-based score generated by the LASSO regression model provided a high accuracy for identifying PC [area under the curve (AUC)=0.918]. In addition, 44 miRNA-mRNA interactions were constructed, and 4 hub genes were screened on the basis of the above bioinformatic tools and databases. Furthermore, 14 biological process (BP) functions and 6 KEGG pathways were identified according to gene set enrichment analysis (GSEA). In summary, the present study applied integrated bioinformatics approaches to generate a holistic view of PC, thereby providing a basis for further clinical application of the 5-miRNA signature and the identified hub molecules, as well as the miRNA-gene axes, which could serve as diagnostic markers and potential treatment targets.

A network‐based variable selection approach for identification of modules and biomarker genes associated with end‐stage kidney disease

“…DNA, RNA, etc.) play an important role in regulating biological functions, 9 network-based approaches, including WGCNA, have been widely used in kidney research and other biomedical studies [12][13][14]28 to capture altered molecular networks and pathways. Once driver modules or pathways are identified, it is crucial to pinpoint key genes for in-depth pathophysiological study and intervention target identification.…”

“…Variable selection has become a routinely used method for identifying relevant biomarkers, 11 and Lasso regression is widely used for binary classifications. 14 The combined use of co-expression network-based analysis and variable selection techniques can be powerful for identifying novel biomarkers and developing predictive models for ESKD risks. Indeed, the predictive model based on the five selected differentially expressed genes showed a strong discrimination power to classify ESKD and controls in the validation dataset.…”

“…8 In addition, variable selection approaches may detect meaningful phenotype-genotype relationships. 11 Therefore, the combined use of WGCNA and variable selection may help to detect novel genes associated with a number of diseases [12][13][14] and construct predictive models.…”

Identification of key genes associated with the progression of intrahepatic cholangiocarcinoma using weighted gene co‑expression network analysis

“…High-throughput microarray and RNA sequencing technology, which are able to identify changes in gene expression, have been widely used in studying the etiology of numerous cancer types, including hepatocellular carcinoma (3), breast cancer (4) and pancreatic cancer (5). Screening novel biomarkers for tumor diagnosis and the pathways associated with tumor development, recurrence and drug resistance, are also critical.…”

Peer Review #1 of "Identification of crucial genes in abdominal aortic aneurysm by WGCNA (v0.1)"

“…Zhang & Horvath, 2005). Furthermore, WGCNA can be used not only to construct co-expression networks based on coding RNA expression data but also to construct noncoding RNA coexpression networks, such as miRNA co-expression networks and lncRNA co-expression networks (Hu et al, 2019;Ma et al, 2019).…”