Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the , and genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.