OBJECTIVE: Pancreatic cancer is a deadly disease with poor prognosis. However, comprehensive understanding about its pathogenesis remains insufficient. In this study, we aimed to find potential novel approaches for the treatment of pancreatic cancer and explore the regulatory mechanisms underlying pancreatic cancer progression.
MATERIALS AND METHODS: The gene expression profile data GSE32688 were downloaded from Gene Expression Omnibus database followed by background correction and normalization through GCRMA (GC Robust Multi-array Average) method. Then DEGs (differentially expressed genes) were identified using t-test method and DEGs-related PPIs (protein-protein interaction) were extracted from STRING database. The PPI networks were constructed by calculating the pearson correlation coefficient under different conditions. Moreover, the network was divided into a number of unit modules, and KEGG pathway and GO analysis were performed for genes in module networks using clusterProfiler.
RESULTS: In total, 199 DEGs (165 up-regulated genes and 34 down-regulated genes) were screened between tumor and normal samples. The integrated DEG. PPI network was established by comparing two different networks under tumor and normal conditions respectively. The top ten genes with high degrees such as ANLN, PSRC1 and ECT2 were identified in the integrated network, and they were mainly enriched in cell cycle pathway.
CONCLUSIONS: ECT2 and PSRC1 might be used as two novel biomarkers for diagnosis and management of pancreatic cancer.Free PDF Download
To cite this article
J. Long, X.-D. Wu, Z. Liu, Y.-H. Xu, C.-L. Ge
Integrated regulatory network involving differently expressed genes and protein-protein interaction on pancreatic cancer
Eur Rev Med Pharmacol Sci
Vol. 19 - N. 13