Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
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Wen-Xing Li1,2,*, Kan He3,4,*, Ling Tang3,*, Shao-Xing Dai2,5, Gong-Hua Li2,5, Wen-Wen Lv6, Yi-Cheng Guo2, San-Qi An2,5, Guo-Ying Wu3, Dahai Liu3, Jing-Fei Huang2,5,7,8,9
1Institute of Health Sciences, Anhui University, Hefei 230601, Anhui, China
2State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
3Center for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
4Department of Biostatistics, School of Life Sciences, Anhui University, Hefei 230601, Anhui, China
5Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, Yunnan, China
6Hongqiao International Institute of Medicine, Shanghai Tongren Hospital/Faculty of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
7KIZ-SU Joint Laboratory of Animal Models and Drug Development, College of Pharmaceutical Sciences, Soochow University, Kunming 650223, Yunnan, China
8Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan, Kunming 650223, Yunnan, China
9Chinese University of Hong Kong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming 650223, Yunnan, China
*These authors have contributed equally to this work
Dahai Liu, email: firstname.lastname@example.org
Jing-Fei Huang, email: email@example.com
Keywords: breast cancer, tissue specific, gene expression, transcription factors, GSEA
Received: August 15, 2016 Accepted: December 07, 2016 Published: December 27, 2016
Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.
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