Abstract
BackgroundIt is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression.MethodsWe divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC.ResultsA total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC.ConclusionOverall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.
Highlights
It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC)
We provided the result of differentially expressed genes (DEGs) among four groups and common DEGs in supplementary Table 4 (Table S4), and DEGs in each group were chosen for the following enrichment analysis
Identification and validation of upstream miRNA Based on the result from candidate hub genes, we further identified the upstream miRNA of those genes through the miRTarBase database that included the Prediction and validation of upstream long noncoding RNA (lncRNA) To predict the upstream lncRNA of candidate miRNAs, the miRNet database was employed for screening miRNA-linked lncRNAs
Summary
It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Multiple RNAs can interact with each other via miRNA response elements (MREs) and assemble as a competing endogenous RNA (ceRNA) network [7]. In this network, lncRNA can act as “sponges” to absorb and bind miRNA, thereby weakening their binding ability to mRNA and regulating gene expression at the transcriptional and post-transcriptional levels. It is necessary to figure out the relationship between the ceRNA network and cancer-related pathways using integrated bioinformatics analysis
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