Identifying cancer specific signaling pathways based on the dysregulation between genes |
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| Affiliation: | 1. State Key Lab of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China;2. Department of Ophthalmology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China;1. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090, Segrate, Milan, Italy;2. Department of Pharmacological & Biomolecular Sciences (DiSFeB), University of Milan, Via Pascal 36, 20133, Milan, Italy;1. Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA;2. Molecular and Cell Biology Department, University of Connecticut, Storrs, CT, USA;1. Key Laboratory for Developmental Genes and Human Disease, Ministry of Education, Institute of Life Sciences, Southeast University, Nanjing 210096, China;2. Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226001, China;3. Joint Research Institute of Southeast University and Monash University, Suzhou 215123, China;4. The First Clinical Medical School, Anhui Medical University, 81 Meishan Road, Hefei 230032, China;5. Department of Neurobiology, Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, 211166 Jiangsu, China;6. Institute for Stem Cell and Regeneration, Chinese Academy of Science, Beijing, China;7. Jiangsu Province High-Tech Key Laboratory for Bio-Medical Research, Southeast University, Nanjing 211189, China;1. Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, Yogyakarta, 55281, Indonesia;2. Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, Yogyakarta, 55281, Indonesia;3. Study Program of Biotechnology, Faculty of Sciences and Technology, Universitas Aisyiah Yogyakarta, Jalan Ringroad Barat No.63, Mlangi Nogotirto, Gamping, Nogotirto, Sleman, Yogyakarta, 55592, Indonesia;1. College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;2. School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China;3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China;4. Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, Guizhou, China;5. College of Software, Jilin University, Changchun, Jilin 130012, China |
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| Abstract: | A large collection of studies has shown that the occurrence of cancer is related to the functional dysfunction of the pathways. Identification of cancer-related pathways could help researchers understand the mechanisms of complex diseases well. Whereas, most current signaling pathway analysis methods take no account of the gene interaction variations within pathways. Furthermore, considering that some pathways have connection with two or more cancer types, while some are likely to be cancer-type specific pathways. Identifying cancer-type specific pathways contributes to interpreting the different mechanisms of different cancer types. In this study, we first proposed a pathway analysis method named Pathway Analysis of Intergenic Regulation (PAIGR) to identify pathways with dysregulation between genes and compared the performance of this method with four existing methods on four colorectal cancer (CRC) datasets. The results showed that PAIGR could find cancer-related pathways more accurately. Moreover, in order to explore the relationship between the identified pathways and the cancer type, we constructed a pathway interaction network, in which nodes and edges represented pathways and interactions between pathways respectively. Highly connected pathways were considered to play a central role in an extensive range of biological processes, while sparsely connected pathways are considered to have certain specificity. Our results showed that pathways identified by PAIGR had a low nodal degree (i.e., a few numbers of interactions), which suggested that most of these pathways were cancer-type specific. |
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| Keywords: | Dysregulation between genes Pathway analysis Node degree Cancer-type specific pathway |
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