Drug Compound Prediction-based Analysis of Cigarette Smoking to Pancreatic Cancer Patients

Abstract

Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.