Dysregulated Signal Propagation in a MYC-associated Boolean Gene Network in B-cell Lymphoma

Abstract

By the modern molecular biological approaches that exploit the availability of high quality gene expression data, it is made clear that flexible and robust responses of cellular programs are encoded in the relations between gene expression values. These relations naturally define a network where they stand for edges between the nodes that stand for the genes. The wiring of these networks often found to be dysregulated in cancer. Different system biological approaches that rely on correlations, differential equations and logical analysis are used to probe these relations in gene expression data especially. In our work we investigated selected biological functions in aggressive germinal center B-cell lymphoma in terms of a logical analysis of gene-regulation in Boolean space and a signal propagation algorithm considering network topology based on gene expression data. We especially aimed at studying the activity of the MYC gene as a key player. It is shown that the functional output of a gene network is affected by the states of the genes and also by the wirings between them. Our results support the key function of MYC in lymphoma biology. In addition, we showed that genes can alter functional output of the network by alternative mechanisms like reducing the variance in propagating signal and locking it to a certain level.

Telomere Length Maintenance and Its Transcriptional Regulation in Lynch Syndrome and Sporadic Colorectal Carcinoma

“…It estimates the transcriptional activity of each pathway node in terms of PSF-scores making use of the local pathway topology and of gene expression fold changes compared to average expression as described in Nersisyan et al (55, 56). The impact and specifics of PSF-pathway analyses compared with gene set approaches were demonstrated recently in a series of applications to characterize aberrant pathway activation in the context of different diseases (45, 57–59).…”

PSF toolkit: an R package for pathway curation and topology-aware analysis

“…Correcting pathway topology ensures that the pathway accurately represents the known interactions and relationships among its components ( Arakelyan and Nersisyan, 2013 ). It also enhances the interpretability and relevance of the pathway in the context of the research question or experimental data, enabling researchers to focus on the specific interactions that are most relevant to their analysis or hypothesis ( Volkan Çakır et al, 2017 ). Pathway analysis methods often rely on the topological information of pathways to assess their functional significance or identify key components or regulators.…”

“…Pathways also may exhibit context-specific variations or modifications under different experimental conditions or specific disease states. Correcting pathway topology enables researchers to incorporate such context-specific information into the analysis ( Volkan Çakır et al, 2017 ).…”