Identification of key pathways and gene changes in primary pancreatic stellate cells after cross-talk with pancreatic cancer cells (BXPC-3) using bioinformatics analysis

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Abstract

It is well known that as the king of cancer, pancreatic ductal adenocarcinoma (PDAC) has malignant biological behavior and poor prognosis. The interaction between pancreatic stellate cells and PDAC cells promotes PDAC development. The aim of this study was to describe gene characteristics in pancreatic stellate cell (PSCs) after cross-talk with BXPC-3 and unravel their underlying mechanisms. The expression profiling analysis of genes in PSCs was performed after 48 h co-culture with primary BXPC-3. The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis and gene ontology (GO) analysis were performed and the differentially expressed genes (DEGs) were identified by Agilent GeneSpring GX program. In total, 1804 DEGs were filtered out in PSCs, including 958 up-regulated genes and 846 down-regulated genes. GO analysis showed that the up-regulated DEGs were significantly enriched in biological processes (BP) such as defense response, immune system processes and immune response, while the down-regulated DEGs were significantly enriched in biological regulation and cytoskeleton organization. KEGG pathway analysis showed that 28 pathways were up-regulated and 5 were down-regulated. By constructing PPI network, we selected 10 key genes (IL6, IL8, IL1B, BCL2, CCL2, CSF2, KIT, ICAM1, PTPRC and IGF1) and significantly enriched pathways. In conclusion, the current study suggests that the filtered DEGs contribute to our understanding of the molecular mechanisms underlying the interaction between PSCs and pancreatic cancer cells, and might be used as molecular targets to further the study the role of tumor microenvironment in the PDAC progression.

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Tang, D., Wu, Q., Yuan, Z., Xu, J., Zhang, H., Jin, Z., … Wang, D. (2019). Identification of key pathways and gene changes in primary pancreatic stellate cells after cross-talk with pancreatic cancer cells (BXPC-3) using bioinformatics analysis. Neoplasma, 66(3), 446–458. https://doi.org/10.4149/neo_2018_180925N714

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