Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer

Abstract

Background: It 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.

Methods: We 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.

Results: A 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.

Conclusion: Overall, 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.

Keywords: Cancer pathways; Competing endogenous RNA; Integrated analysis; Pancreatic cancer; Progression.

Fig. 1

Workflow presenting the process of establishing the pathway-related ceRNA network in pancreatic cancer

Fig. 2

Screening differentially expressed genes (DEGs) among four groups by R software (v3.6.1). a–d The volcano plots of DEGs in GPL570, GPL6244, GPL13667, TCGA and GTEx with thresholds of |log2FC| > 1, adjust P value < 0.05. The red dots and green dots represent the upregulated and downregulated DEGs separately. The black dots mean no significantly different genes. e The top 10 up and down regulated DEGs identified by an integrated analysis of four groups

Fig. 3

KEGG enrichment analysis for DEGs and identifying hub genes in cancer-related pathways of PC. The top ten significantly enriched the KEGG pathway in GPL570 (a), GPL6244 (b), GPL13667 (c), and TCGA_GTEx (d) respectively. e The distribution of enriched KEGG pathways in GPL570, GPL6244, GPL13667, and TCGA_GTEx. f The top 20 hub genes of DEGs in pathway in cancer. The depth of color represents the connection degree of genes. Red represents a high connection degree and yellow represents low connection. The number of lines represents the degree of connection between genes, and the more lines, the tighter the connection

Fig. 4

Screening and validating the expression roles and prognosis values of key genes in PC. a-f Validating expression roles and prognosis values of key genes in hub genes using GEPIA and Kaplan–Meier plotter databases

Fig. 5

Identifying the key long noncoding RNA and constructing the pathway-related ceRNA network in PC. a-b Validating the expression and prognostic value of four key lncRNAs using GEPIA and Kaplan–Meier plotter databases. c The potential mRNA-miRNA-lncRNA regulatory network related to PC prognosis was drawn by Cytoscape software (v3.7.2). The ellipse, triangle, and diamond shape represents mRNA, miRNA, and lncRNA respectively

Fig. 6

The coexpression analysis of ceRNA and schematic representations of PVT1/miR-20b/CCND1 ceRNA regulatory network. a-c The coexpression analysis indicated PVT1 was negatively correlated with miR-20b and meanwhile positively correlated with CCND1. d Schematic representations of pathway-related PVT1/miR-20b/CCND1 ceRNA regulatory network in the progression of PC