Identification of Key Genes in Pancreatic Ductal Adenocarcinoma Gene Expression Profile by Integrative Analysis

Identification of Key Genes in Pancreatic Ductal Adenocarcinoma Gene Expression Profile by Integrative Analysis
Romina Seifollahi ASl,^1,* Mahsa Mousakhan Bakhtiari,² Mohammad amin Mahmanzar,³
1. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3. Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
Introduction: Among all of the cancer types, beyond one-quarter is considered as the gastrointestinal ones. Pancreatic cancer is rated as the 7th mortal cancer globally. With a median survival of 6 months or less, Pancreatic ductal adenocarcinoma (PDAC) (an exocrine tumor ) the fourth lethal malignancy in developed countries. With a median of 6 months or less, PDAC is the fourth lethal malignancy in developed countries. The aggressive nature of the tumor, which discomfits its diagnosis, chemotherapy and radiotherapy resistance, and moreover the poor specificity and sensitivity of PDAC’s biomarkers ( CA19-9, the sole biomarker in the clinical investigations ) are the current challenges in this illness. There is a bright outlook on the development in diagnosis, therapeutic, and prognosis of PDAC, based on biological improvement and integrated bioinformatics analysis In this study, we aim to collect all of the microarray data related to PDAC’s gene expression by bioinformatics appliances, and analysis the GSE data downloaded from the NCBI GEO database. We draw a protein-protein interaction network (PPI) to evaluate up regulations and down regulations the gene expression of coding genes to perform pathway analysis as an attempt to respond the mentioned challenges.
Methods: datasets of pancreatic ductal adenocarcinoma were searched using the keywords: ‘pancreatic ductal adenocarcinoma, ‘Homo sapiens’ and ‘Expression profiling by array’ against the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). To identify differentially expressed genes (DEGs), each dataset was analyzed by limma R package in Bio conductor. Significant differential expression was determined as a log fold change ≥ |1| and adjusted p-value threshold of 0.05. to find the correlation between collected genes, draw Protein-Protein Interaction network (PPI) and report the hub genes.
Results: After a systematic review, seven GSE profiles from 34 datasets (GSE102238, GSE46234, GSE63111, GSE78229, GSE89997, GSE62452 ) which include 315 cases, were included. To the evaluated quality of data. Heat map and principle Component Analysis (PCA) was performed.
Conclusion: Based on PPI networks, top 10 DEGS such as ATM, BRCA2, MSH2, PTEN with high Degree and Between collected AND Pathway analysis were performed.
Keywords: keyword : pancreatic ductal adenocarcinoma, Gene expression, insilico