Identification of Key Transcription Factors in Pancreatic Cancer Using Network Analysis

Background and aim: pancreatic cancer (pc) is the seventh most common cause of cancer-related deaths worldwide. prognosis of pc is very poor with a five-year survival rate about 8%. the treatment of pancreatic cancer is limited due to difficulties associated with surgical removal, and poor sensitivity to radiotherapy and chemotherapy. therefore, identification of an effective therapeutic target is required to improve patient outcome. transcription factors are the key players which contribute in a considerable number of human diseases such as cancers. currently, transcription factors are targeted for cancer therapy. therefore, in this study, we identified some tfs that have critical role in pancreatic cancer and can be considered as therapeutic targets for the development of anticancer drugs.methods: transcriptomics data of normal pancreatic tissue and pancreatic cancer were retrieved from the cancer genome atlas (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. transcription factors (tfs) were identified using the human transcription factors database. the string database was applied to evaluate the interactions of tfs. cytoscape plugin cytohubba was employed to explore the top hub genes in tfs.results: the results indicated that 798 genes were differentially expressed between pancreatic cancer and normal tissues. twenty-eight tfs were identified among the degs, which belonged to 14 diverse tf families. the network was constructed from tfs which contain 17 nodes (fli1, klf1, bach2, pax5, bcl11a, pou2f2, spi1, irf8, ikzf1, tbx21, ikzf3, tfec, plek, nkx2-5, eomes, sox3, nr5a1) and 38 edges. three genes include spi1, irf8 and ikzf1 with top node degrees were selected as the hub genes.conclusion: the identified tfs in the current study may provide potential targets for the diagnosis and treatment of pancreatic cancer.