Detection of diagnostic and treatmental markers in pancreas cancers by in silico methods

Objectives: Pancreatic cancer is among the cancers with high mortality. Most of the pancreatic cancers are diagnosed in metastatic dieaseas therefore survival times are short. Early diagnosis of pancreatic cancer has a huge impact on treatment and survival time therefore focusing on diagnosis in early stage is much more important. Gene/genes with biomarker properties that can be used for pancreatic cancer diagnosis and treatment and/or pathways that are important in disease formation haven't been fully understanded. In this study, we aimed to find new molecular targets that can diagnose panceatic cancer in early stages and impact treatment options for pancreatic cancer, which has an aggressive course and rapid metastasis. Material and Method: GEO (Gene Expression Omnibus) database was used to find genes that are expressed differently in pancreatic ductal adenocarcinoma compared to normal tissue. In total, microarray data of 68 normal tissues and 81 pancreatic ductal adenocarcinomas were collected. These data sets were processed in the RStudio program, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genes (KEGG), Hallmark and Metabolite pathway analysis were performed. The STRING (Search tool for the retrieval of interacting genes) database was used for protein to protein interaction analysis from these obtained genes and their images were obtained by transferring them to Cytoscape. Results: 107 genes with statistically significant changes were obtained from the microarray data of 68 normal tissues and 81 pancreatic ductal adenocarcinoma. It was seen that 26 of these genes were down-regulated and 81 of them were up-regulated. Along with pathway analysis, it was shown that genes in pancreatic cancer act through pathways such as extracellular matrix regulation and epithelial-mesenchymal transition. With the protein-protein interaction analysis, it was seen that FN1, COL1A2, TIMP1, POSTN, COL3A1, MMP1, EGF, THBS2, ALB, ITGA2 are located at the center of this interaction network. Conclusion: This study identified many differentially expressed genes in pancreatic cancer and will be helpful for further research to identify diagnostic/prognostic markers and new therapeutic targets.