Background and Aims Pancreatic cancer is a common malignant digestive system disease that is difficult to diagnose and treat. Most patients have missed the opportunity for surgery at the time of diagnosis. Developing new targets for early diagnosis and treatment of pancreatic cancer is of great significance. Therefore, this study was conducted to screen key genes related to the progression of pancreatic cancer using bioinformatics approach and identify their specific mechanisms that affect tumor progressionMethods The GEO datasets GSE15471, GSE16515, and GSE132956 were selected, and differential gene analysis was performed on these three datasets using the R limma package to identify differentially expressed genes in pancreatic cancer. The Venn diagram was used to determine the intersection of these genes. Metascape was used to perform functional enrichment analysis on the differentially expressed genes, KMPLOT was used to analyze the correlation between these genes and patient survival, and GEPIA was used to validate the differential expressions. The key gene was selected, and its correlation with clinical and pathological information of pancreatic cancer was analyzed using Linkedomics. The biological functions of the interaction factors of the key gene were analyzed using KEGG and GO enrichment analysis. A PPI network was constructed using Cytoscape software to analyze related signaling pathways. The expression of the key gene was subsequently verified in pancreatic cancer tissue samples and cell lines, and cellular functional experiments were conducted to validate its function.Results A total of 177 upregulated genes and 104 downregulated genes were identified in the three GEO datasets. Metascape enrichment analysis revealed that the upregulated genes were enriched in tissue morphogenesis, angiogenesis, cell movement, and cell proliferation, while the downregulated genes were enriched in processes such as metabolism and pancreatic secretion. KMPLOT survival analysis identified six factors (CTTNBP2NL, FGD6, ITGA2, KRT19, S100P, TMPRSS4) that were significantly associated with pancreatic cancer and all of them were risk factors for the survival of pancreatic cancer patients (all P<0.05). GEPIA validation also showed that these genes were significantly upregulated in pancreatic cancer (all P<0.05). Among them, CTTNBP2NL was of unknown function in pancreatic cancer, so it was selected for further study. Clinical correlation analysis showed that CTTNBP2NL was significantly correlated with the N stage of TNM classification in pancreatic cancer and was upregulated with the increase of the stage. PPI analysis revealed 17 proteins that interacted with CTTNBP2NL, and KEGG enrichment analysis found that these proteins were related to the PI3K/Akt and TGF-β pathways. GO enrichment analysis also found that these proteins were related to cell separation and apoptosis. The three GEO datasets showed that CTTNB2NL was highly expressed in pancreatic cancer, and both mRNA and protein expressions of CTTNB2NL were upregulated in pancreatic cancer tissues and cell lines (all P<0.05). Functional experiments showed that in pancreatic cancer cells after CTTNBP2NL silencing, the proliferation, migration and invasion were significantly inhibited while the apoptosis was significantly increased, and meanwhile, the activity of the PI3K/Akt signaling pathway was significantly inhibited (all P<0.05).Conclusions CTTNBP2NL is highly expressed in pancreatic cancer and closely associated with the proliferation, migration, and invasion of pancreatic cancer cells. Its mechanism of action may be related to the activation of the PI3K/Akt signaling pathway. CTTNBP2NL can potentially serve as a prognostic biomarker and diagnostic target for pancreatic cancer.