Le Zhang1,2,3, Guangdi Liu4,5, Meijing Kong4, Tingting Li6, Dan Wu7, Xiaobo Zhou7, Chuanwei Yang8, Lei Xia9, Zhenzhou Yang9, Luonan Chen10,11,12. 1. College of Computer Science. 2. Medical Big Data Center, Sichuan University, Chengdu 610065, China. 3. Chongqqing Zhongdi Medical Information Technology Co., Ltd, Chongqing 401320, China. 4. College of Computer and Information Science, Southwest University, Chongqing 400715, China. 5. Library of Chengdu University, Chengdu University, Chengdu 610106, China. 6. College of Mathematics and Statistics, Southwest University, Chongqing 400715, China. 7. Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA. 8. Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 9. Cancer Center, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing 400042, China. 10. Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. 11. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China. 12. Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.
Abstract
MOTIVATION: The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway. RESULTS: We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway. AVAILABILITY AND IMPLEMENTATION: We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway. RESULTS: We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway. AVAILABILITY AND IMPLEMENTATION: We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Fubo Ma; Ming Xiao; Lin Zhu; Wen Jiang; Jizhe Jiang; Peng-Fei Zhang; Kang Li; Min Yue; Le Zhang Journal: Front Genet Date: 2022-09-14 Impact factor: 4.772