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Expression variation: its relevance to emergence of chronic disease and to therapy.

Mayburd AL - PLoS ONE (2009)

Bottom Line: Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes.Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization.The results of the study suggest the need to advance personalized therapy development.

View Article: PubMed Central - PubMed

Affiliation: CPA Global, Alexandria, VA, USA. AMayburd@cpaglobal.com

ABSTRACT

Background: Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well -- including cancer?

Methods: Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results: Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions: A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.

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Related in: MedlinePlus

Expression variability in chronic diseases.Averaged panel variability of gene expression was measured for different chronic disease states, including aging (250–500 probes-sets per a disease).
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pone-0005921-g004: Expression variability in chronic diseases.Averaged panel variability of gene expression was measured for different chronic disease states, including aging (250–500 probes-sets per a disease).

Mentions: To ensure that these observations are not specific for cancer alone, similar analysis was conducted for other classes of disease-related genes, see Figure 4. Mining the database “Genes” at NCBI with keywords corresponding to particular disorders produced gene aliases associated with these disorders based on the analysis of scientific and medical literature. The expression variability trend first discovered for anti-cancer targets vs random genes was confirmed for all major chronic conditions.


Expression variation: its relevance to emergence of chronic disease and to therapy.

Mayburd AL - PLoS ONE (2009)

Expression variability in chronic diseases.Averaged panel variability of gene expression was measured for different chronic disease states, including aging (250–500 probes-sets per a disease).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2692004&req=5

pone-0005921-g004: Expression variability in chronic diseases.Averaged panel variability of gene expression was measured for different chronic disease states, including aging (250–500 probes-sets per a disease).
Mentions: To ensure that these observations are not specific for cancer alone, similar analysis was conducted for other classes of disease-related genes, see Figure 4. Mining the database “Genes” at NCBI with keywords corresponding to particular disorders produced gene aliases associated with these disorders based on the analysis of scientific and medical literature. The expression variability trend first discovered for anti-cancer targets vs random genes was confirmed for all major chronic conditions.

Bottom Line: Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes.Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization.The results of the study suggest the need to advance personalized therapy development.

View Article: PubMed Central - PubMed

Affiliation: CPA Global, Alexandria, VA, USA. AMayburd@cpaglobal.com

ABSTRACT

Background: Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well -- including cancer?

Methods: Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results: Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions: A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.

Show MeSH
Related in: MedlinePlus