BACKGROUND: Pancreatic cancer (PAC) is a lethal cancer and it is essential to develop accurate diagnostic and prognostic biomarkers for PAC.

MATERIAL AND METHODS: An integrated microarray analysis of PAC was conducted to identify differentially expressed genes (DEGs) between PAC and non-tumor controls. Expression of DEGs were further confirmed by The Cancer Genome Atlas and the Genotype-Tissue Expression. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and protein–protein integration network construction were performed to further research the biological functions of DEGs. Receiver-operating characteristic analysis and survival analysis were used to evaluate the diagnostic and prognostic value of DEGs for PAC.

RESULTS: Seventeen microarray datasets were downloaded from Gene Expression Omnibus to conduct the integrated microarray analysis. A total of 1136 DEGs (596 upregulated and 540 downregulated DEGs) in PAC tissues compared with non-tumor controls were identified. Pancreatic secretion (Kegg: 04972), insulin signaling pathway (Kegg: 04910), and several cancer-related pathways including pathways in cancer (Kegg: 05200), MAPK signaling pathway (Kegg: 04010), and pancreatic cancer (Kegg: 05212) were enriched for DEGs in PAC. Seven DEGs (AHNAK2, CDH3, IFI27, ITGA2, LAMB3, SLC6A14, and TMPRSS4) were found to have both great diagnostic and prognostic value for PAC. High expression of these 7 DEGs were significantly associated with poor prognosis of patients with PAC.

CONCLUSIONS: These 7 DEGs might be potential diagnostic and prognostic biomarkers for PAC and help uncovering the mechanism of PAC.

Keywords: Biological Markers, Diagnosis, Microarray Analysis, Pancreatic Neoplasms, Biomarkers, Tumor, Computational Biology, Databases, Genetic, Datasets as Topic, Down-Regulation, Gene Regulatory Networks, Oligonucleotide Array Sequence Analysis, Pancreas, Protein Interaction Mapping, Protein Interaction Maps, Tissue Array Analysis