Abstract
Pancreatic duct adenocarcinoma (PDAC) is a highly malignant type of cancer with a low five-year survival rate. Gene alterations are crucial to the molecular pathogenesis of PDAC. Therefore, the present study analyzed gene expression profiles to reveal genes involved in the tumorigenesis of PDAC. A total of eight gene expression profiles (GSE15471, GSE16515, GSE41368, GSE62165, GSE62452, GSE71729, GSE71989 and GSE91035) and a PDAC dataset were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) database, respectively. Differentially expressed genes (DEGs) were screened using functional annotation, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network construction. A Cox proportional hazards model was then constructed and used to analyze the data. A total of 136 DEGs (67 up- and 69 downregulated genes) were identified between PDAC tissues and normal tissues. The 'extracellular matrix-related' genes were the most enriched in the GO term analysis. 'Pancreatic secretion', 'phosphoinositide-3-kinase-protein kinase B/Akt (PI3K-Akt) signaling pathway', 'protein digestion and absorption' and 'ECM-receptor interaction' were the most enriched categories in KEGG pathway analysis. Following PPI network construction, the 10 most significant genes [albumin, epidermal growth factor, matrix metalloproteinase (MMP) 9, epidermal growth factor receptor, fibronectin 1, MMP1, plasminogen activator inhibitor-1, tissue inhibitor of metalloproteinase 1, plasminogen activator urokinase (PLAU) and PLAU receptor) exhibiting a high degree of connectivity, were identified as the hub genes likely to be associated with the pathogenesis of PDAC. In addition, a prognostic predictive system for PDAC, composed of five genes (laminin subunit γ 2, laminin subunit β 3, serpin family B member 5, amphiregulin and secreted frizzled related protein 4), was constructed. This was validated in the GSE62452 dataset (using 66 PDAC samples with outcome data) and TCGA PDAC dataset (using 146 PDAC samples with outcome data). In conclusion, the present study revealed potential hub genes involved in PDAC progression, providing directive significance for individualized clinical decision-making and molecular-targeting therapy in patients with PDAC.