Purpose The current treatment methods available for advanced gastric cancer are not very promising. Hence, it is important to explore novel biomarkers and potential therapeutic agents to treat gastric cancer (GC). This study aimed to identify hub genes associated with GC prognosis and explore potential drugs for its treatment. Materials and Methods Three gene expression data of GC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) and processed to identify the differentially expressed genes (DEGs). We conducted a comprehensive analysis of DEGs, including functional enrichment analysis, construction of protein–protein interaction (PPI) network, identification of hub genes, survival analysis and expression verification of hub genes. Finally, we constructed the network of miRNA–mRNA, and predicted the drugs that might be effective for GC treatment. Results A total of 340 DEGs, including 94 up-regulated and 246 down-regulated genes, were identified. Among the up-regulated DEGs, the enrichment terms were primarily related to tumorigenesis and tumor progression, extracellular matrix organization, and collagen catabolic process. Additionally, 10 hub genes ( FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1, COL5A1, SPARC , and COL4A1 ) were identified, out of which 7 genes were significantly associated with poor overall survival (OS) in GC. The expression levels of these 7 hub genes were verified using real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression Profiling Interactive Analysis) server. A regulatory network of miRNA–mRNA was also constructed, and the top 4 interactive miRNAs (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p) that targeted the most hub genes were identified. Finally, fourteen small molecules were predicted to be effective in treating GC. Conclusion The identification of the hub genes, miRNA–mRNA network, and potential candidate drugs associated with GC provides new insights into the molecular mechanisms and treatment of GC.
Background The pathogenesis of acute pancreatitis (AP) and the relationship between acute pancreatitis and hypertriglyceridemia are complex and not fully understood. The purpose of this study was to identify the hub genes along with common differentially expressed genes (DEGs) between acute pancreatitis and hypertriglyceridemia. Methods We downloaded three gene expression profiles of AP and one gene expression profile of hypertriglyceridemia from the Gene Expression Omnibus (GEO) database and filtered the DEGs based on the above four datasets. Next, we identified the hub genes by performing the Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein–protein interaction (PPI) construction. We also constructed the miRNA-hub gene network and established mouse models with hypertriglyceridemia and AP using a high-fat diet and injection of caerulein (CAE), respectively. Finally, the immunohistochemical analysis was used to verify the differential expressions of hub genes in AP, hypertriglyceridemia, and normal pancreatic tissue. Results A total of 105 DEGs associated with AP and 149 DEGs associated with hypertriglyceridemia were identified. Additionally, we identified six hub genes of AP, all of which were closely related to the cytoskeleton while two DEGs genes were common in both AP and hypertriglyceridemia. We also verified their expression in mouse models. Finally, a network of miRNA-mRNA was also constructed, and the top seven interactive miRNAs ( hsa-mir-1–3p, hsa-mir-5195–3p, hsa-mir-145–5p, hsa-let-7b-5p, hsa-mir-10b-5p, hsa-mir-206 , and hsa-mir-613 ) targeting the most hub genes were identified. Conclusion Overall, we identified six hub genes associated with AP and two common DEGs associated with AP and hypertriglyceridemia along with seven miRNAs that may regulate AP. This study could provide new ideas for further elucidation of the pathogenesis of hypertriglyceridemia-induced acute pancreatitis in the future.
Gelsolin (GSN) can sever actin filaments associated with autophagy. This study investigated how GSN-regulated actin filaments control autophagy in pancreatic ductal epithelial cells (PDECs) in acute pancreatitis (AP). AP was produced in a rat model and PDECs using caerulein (CAE). Rat pancreatic duct tissue and HPDE6-C7 cells were extracted at 6, 12, 24, and 48 h after CAE treatment. HPDE6-C7 cells in the presence of CAE were treated with cytochalasin B (CB) or silenced for GSN for 24 h. Pancreatic histopathology and serum amylase levels were analyzed. Cellular ultrastructure and autophagy in PDECs were observed by transmission electron microscopy after 24 h of CAE treatment. The expression of GSN and autophagy markers LC3, P62, and LAMP2 was evaluated in PDECs by immunohistochemistry and western blotting. Actin filaments were observed microscopically. Amylase levels were highest at 6 h of AP, and pancreatic tissue damage increased over time. Mitochondrial vacuolization and autophagy were observed in PDECs. CAE increased GSN expression in these cells over time, increased the LC3-II/LC3-I ratio and LAMP2 expression at 24 and 6 h of treatment, respectively, and decreased P62 expression at all time points. CB treatment for 24 h decreased the LC3-II/LC3-I ratio and LAMP2 expression, increased P62 levels, but had no impact on GSN expression in CAE-treated PDECs. CAE induced actin depolymerization, and CB potentiated this effect. GSN silencing increased the LC3-II/LC3-I ratio and LAMP2 expression and reduced actin depolymerization in CAE-treated PDECs. GSN may inhibit autophagosome biogenesis and autophagosome-lysosome fusion by increasing actin depolymerization in PDECs in AP.
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