1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Co-Expression Analysis for Identification of Critical Genes in Pancreatic Cancer
Paper ID : 1171-ICB10
Authors:
Sahar Akrami *1, Ali Niazi1, Ahmad Tahmasebi1, Amin Ramezani2, Abbas Alemzadeh3, Ali Moghadam1
1Institute of Biotechnology, Shiraz University, Shiraz, Iran
2Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
3Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, Iran
Abstract:
Introduction: Pancreatic ductal adenocarcinoma (PDAC) is known as king of carcinoma. PDAC is a distinctly aggressive cancer, with a 5‑year survival rate of <1.0%. Considerable efforts have been made to identify potential PDAC biomarkers that may be used to develop anti‑metastatic treatments and improve prognostic evaluation. Novel biomarkers for PDAC are urgently needed because of its poor prognosis. Recently, computational analyses using high-throughput expression data have helped to recognize putative molecular mechanisms involved in various cancers. Despite the importance of differentially expressed genes (DEGs) identification, this strategy mostly focuses on the discovery of gene contents and suffers from exploring relationships among genes. Co-expression network analyses allow us to apply a system-level view of gene-gene connections. In this study, we carried out both methods of finding the DEGs and also network analysis by co-expression method, to find the possible biomarker candidates for PDAC. Method: Transcriptomics data of normal pancreatic tissue and pancreatic cancer, of pancreatic cancer, were retrieved from TCGA database. To normalize data and identify the differentially expressed genes (DEGs), the edgeR package was used with an FDR of ≤0.01 and a |fold change| ≥1. Co-expression method was applied with Hmisc package in R based on Pearson correlation. The network was visualized with Cytoscape, and finally with the help of CytoNCA plug-in, hub genes were topologically identified. Result: A total of 798 DEGs which were differentially expressed between pancreatic cancer and normal tissues were found. The network constructed by the co-expression analysis showed 890 nodes and 108742 edges. PCNA, CD49B, CEP250-AS1, MTOR_Ps2448 and PI3KP110ALPHA with top node degrees were selected as the hub genes. Generally these genes are involved in mismatch repair, base excision repair, DNA replication and cell cycle. Conclusion: The analysis suggests that these genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.
Keywords:
Co-expression analysis; Pancreatic Cancer; Biomarker; Network Analysis
Status : Paper Accepted (Poster Presentation)