Purpose: This study aimed to identify the underlying mechanisms in pancreatic cancer (PC) carcinogenesis and those as potential prognostic biomarkers, which can also be served as new therapeutic targets of PC.
Methods: Differentially expressed genes (DEGs) were identified between PC tumor tissues and adjacent normal tissue samples from a public GSE62452 dataset, followed by functional and pathway enrichment analysis. Then, protein-protein interaction (PPI) network was constructed and prognosis-related genes were screened based on genes in the PPI network, before which prognostic gene-related miRNA regulatory network was constructed. Functions of the prognostic gene in the network were enriched before which Kaplan-Meier plots were calculated for significant genes. Moreover, we predicted related drug molecules based on target genes in the miRNA regulatory network. Furthermore, another independent GSE60979 dataset was downloaded to validate the potentially significant genes.
Results: In the GSE62452 dataset, 1017 significant DEGs were identified. Twenty-six important prognostic-related genes were found using multivariate Cox regression analysis. Through pathway enrichment analysis and miRNA regulatory analysis, we found that the 5 genes, such as Interleukin 22 Receptor Subunit Alpha 1 (IL22RA1), BCL2 Like 1 (BCL2L1), STAT1, MYC Proto-Oncogene (MYC), and Signal Transducer And Activator Of Transcription 2 (STAT2), involved in the Jak-STAT signaling pathway were significantly associated with prognosis. Moreover, the expression change of these 5 genes was further validated using another microarray dataset. Additionally, we identified camptothecin as an effective drug for PC.
Conclusion: IL22RA1, BCL2L1, STAT1, MYC, and STAT2 involved in the Jak-STAT signaling pathway may be significantly associated with prognosis of PC.