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Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor with rapid progression and poor prognosis. In the present study, 11 high-quality microarray datasets, comprising 334 tumor samples and 151 non-tumor samples from the Gene Expression Omnibus, were screened, and integrative meta-analysis of expression data was used to identify gene signatures that differentiate between PDAC and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two-way hierarchical clustering analysis was performed for all DEGs using the gplots package in R software. Hub genes were then determined through protein-protein interaction network analysis using NetworkAnalyst. In addition, functional annotation and pathway enrichment analyses of all DEGs were conducted in the Database for Annotation, Visualization, and Integrated Discovery. The expression levels and Kaplan-Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing PDAC with normal tissues. Of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium-binding protein A6 (S100A6) were the top upregulated genes, and kinesin family member 1A, Dymeclin and β-secretase 1 were the top downregulated genes. Reverse transcription-quantitative PCR was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in PDAC compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member C overexpression was strongly associated with longer overall survival times. In addition, network-based meta-analysis confirmed growth factor receptor-bound protein 2 and histone deacetylase 5 as pivotal hub genes in PDAC compared with normal tissue. In conclusion, the results of the present meta-analysis identified PDAC-related gene signatures, providing new perspectives and potential targets for PDAC diagnosis and treatment. [ABSTRACT FROM AUTHOR]