ArticleBioinformatics (Oxford, England)2021
Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 35 citations in OpenAlex.
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- PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.Frontiers in genetics · 2024Article
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- Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis.Frontiers in immunology · 2023Article
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- Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme.BMC bioinformatics · 2020Article
- Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis.Frontiers in genetics · 2020Article
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Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
motivationThe 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.
resultsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.