ArticleTheoretical biology & medical modelling2015
Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia.
Article in Theoretical biology & medical modelling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 18 citations in OpenAlex.
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- Data-driven learning of Boolean networks and functions by optimal causation entropy principle.Patterns (New York, N.Y.) · 2022Article
- Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes.Scientific reports · 2017Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood.
methodsIn this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state.
resultsThe result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer.
conclusionThe differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system.
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