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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 parameters of random genes vs the parameters of therapeutic anti-cancer targets.Presented is a comparison of expression parameters for random genes (grey bars), cancer-related genes, both target and non-target (striped bars), proposed and developing anti-cancer targets (checkered bars) and successful anti-cancer targets (black bars). The parameters of expression were estimated as described in the Methods. The differential expression refers to the comparison between norm and cancer. The confidence intervals were computed with the significance level α = 0.05
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pone-0005921-g001: Expression parameters of random genes vs the parameters of therapeutic anti-cancer targets.Presented is a comparison of expression parameters for random genes (grey bars), cancer-related genes, both target and non-target (striped bars), proposed and developing anti-cancer targets (checkered bars) and successful anti-cancer targets (black bars). The parameters of expression were estimated as described in the Methods. The differential expression refers to the comparison between norm and cancer. The confidence intervals were computed with the significance level α = 0.05

Mentions: Figure 1 presents normalized levels of expression, consistency of differential expression and integrated panel variability for 54675 probe-sets comprising the high density U133 Plus 2.0 microarray platform by Affymetrix. For cancer-related genes (∼2900 probe-sets) variability is higher in norm as compared to random genes. The same refers to differential expression and expression. For prospective anti-cancer targets, the expression parameters correlate with the extent of clinical development, being higher for FDA approved targets (black bars) as compared with the mix of target and non-targets (striped bars, “cancer-related” category).


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

Mayburd AL - PLoS ONE (2009)

Expression parameters of random genes vs the parameters of therapeutic anti-cancer targets.Presented is a comparison of expression parameters for random genes (grey bars), cancer-related genes, both target and non-target (striped bars), proposed and developing anti-cancer targets (checkered bars) and successful anti-cancer targets (black bars). The parameters of expression were estimated as described in the Methods. The differential expression refers to the comparison between norm and cancer. The confidence intervals were computed with the significance level α = 0.05
© Copyright Policy
Related In: Results  -  Collection

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

pone-0005921-g001: Expression parameters of random genes vs the parameters of therapeutic anti-cancer targets.Presented is a comparison of expression parameters for random genes (grey bars), cancer-related genes, both target and non-target (striped bars), proposed and developing anti-cancer targets (checkered bars) and successful anti-cancer targets (black bars). The parameters of expression were estimated as described in the Methods. The differential expression refers to the comparison between norm and cancer. The confidence intervals were computed with the significance level α = 0.05
Mentions: Figure 1 presents normalized levels of expression, consistency of differential expression and integrated panel variability for 54675 probe-sets comprising the high density U133 Plus 2.0 microarray platform by Affymetrix. For cancer-related genes (∼2900 probe-sets) variability is higher in norm as compared to random genes. The same refers to differential expression and expression. For prospective anti-cancer targets, the expression parameters correlate with the extent of clinical development, being higher for FDA approved targets (black bars) as compared with the mix of target and non-targets (striped bars, “cancer-related” category).

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