Introduction: Pancreatic ductal adenocarcinoma (PDAC) is known as king of carcinoma. PDAC is adistinctly aggressive cancer, with a ۵-year survival rate of <۱.۰%. Considerable efforts have been made toidentify potential PDAC biomarkers that may be used to develop anti-metastatic treatments and improveprognostic evaluation. Novel biomarkers for PDAC are urgently needed because of its poor prognosis.Recently, computational analyses using high-throughput expression data have helped to recognize putativemolecular mechanisms involved in various cancers. Despite the importance of differentially expressed genes(DEGs) identification, this strategy mostly focuses on the discovery of gene contents and suffers fromexploring relationships among genes. Co-expression network analyses allow us to apply a system-level viewof gene-gene connections. In this study, we carried out both methods of finding the DEGs and also networkanalysis by co-expression method, to find the possible biomarker candidates for PDAC. Method:Transcriptomics data of normal pancreatic tissue and pancreatic cancer, of pancreatic cancer, were retrievedfrom TCGA database. To normalize data and identify the differentially expressed genes (DEGs), the edgeRpackage was used with an FDR of ≤۰.۰۱ and a |fold change| ≥۱. Co-expression method was applied withHmisc package in R based on Pearson correlation. The network was visualized with Cytoscape, and finallywith the help of CytoNCA plug-in, hub genes were topologically identified. Result: A total of ۷۹۸ DEGswhich were differentially expressed between pancreatic cancer and normal tissues were found. The networkconstructed by the co-expression analysis showed ۸۹۰ nodes and ۱۰۸۷۴۲ edges. PCNA, CD۴۹B, CEP۲۵۰-AS۱, MTOR_Ps۲۴۴۸ and PI۳KP۱۱۰ALPHA with top node degrees were selected as the hub genes. Generallythese genes are involved in mismatch repair, base excision repair, DNA replication and cell cycle.Conclusion: The analysis suggests that these genes may be potential diagnostic biomarkers and/or therapeuticmolecular targets in patients with PDAC.