131,102 research outputs found
An evaluation of DNA-damage response and cell-cycle pathways for breast cancer classification
Accurate subtyping or classification of breast cancer is important for
ensuring proper treatment of patients and also for understanding the molecular
mechanisms driving this disease. While there have been several gene signatures
proposed in the literature to classify breast tumours, these signatures show
very low overlaps, different classification performance, and not much relevance
to the underlying biology of these tumours. Here we evaluate DNA-damage
response (DDR) and cell cycle pathways, which are critical pathways implicated
in a considerable proportion of breast tumours, for their usefulness and
ability in breast tumour subtyping. We think that subtyping breast tumours
based on these two pathways could lead to vital insights into molecular
mechanisms driving these tumours. Here, we performed a systematic evaluation of
DDR and cell-cycle pathways for subtyping of breast tumours into the five known
intrinsic subtypes. Homologous Recombination (HR) pathway showed the best
performance in subtyping breast tumours, indicating that HR genes are strongly
involved in all breast tumours. Comparisons of pathway based signatures and two
standard gene signatures supported the use of known pathways for breast tumour
subtyping. Further, the evaluation of these standard gene signatures showed
that breast tumour subtyping, prognosis and survival estimation are all closely
related. Finally, we constructed an all-inclusive super-signature by combining
(union of) all genes and performing a stringent feature selection, and found it
to be reasonably accurate and robust in classification as well as prognostic
value. Adopting DDR and cell cycle pathways for breast tumour subtyping
achieved robust and accurate breast tumour subtyping, and constructing a
super-signature which contains feature selected mix of genes from these
molecular pathways as well as clinical aspects is valuable in clinical
practice.Comment: 28 pages, 7 figures, 6 table
Mechanically probing the folding pathway of single RNA molecules
We study theoretically the denaturation of single RNA molecules by mechanical
stretching, focusing on signatures of the (un)folding pathway in molecular
fluctuations. Our model describes the interactions between nucleotides by
incorporating the experimentally determined free energy rules for RNA secondary
structure, while exterior single stranded regions are modeled as freely jointed
chains. For exemplary RNA sequences (hairpins and the Tetrahymena thermophila
group I intron), we compute the quasi-equilibrium fluctuations in the
end-to-end distance as the molecule is unfolded by pulling on opposite ends.
Unlike the average quasi-equilibrium force-extension curves, these fluctuations
reveal clear signatures from the unfolding of individual structural elements.
We find that the resolution of these signatures depends on the spring constant
of the force-measuring device, with an optimal value intermediate between very
rigid and very soft. We compare and relate our results to recent experiments by
Liphardt et al. [Science 292, 733-737 (2001)].Comment: 10 pages, 8 figures, revised version, to be published in Biophys.
A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures
MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available
Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma.
BACKGROUND: Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to ligand stimulation and transfected with EGFR, RELA/p65, or HRASVal12D.
RESULTS: The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines.
CONCLUSIONS: Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies
Noise characteristics of the Escherichia coli rotary motor
The chemotaxis pathway in the bacterium Escherichia coli allows cells to
detect changes in external ligand concentration (e.g. nutrients). The pathway
regulates the flagellated rotary motors and hence the cells' swimming
behaviour, steering them towards more favourable environments. While the
molecular components are well characterised, the motor behaviour measured by
tethered cell experiments has been difficult to interpret. Here, we study the
effects of sensing and signalling noise on the motor behaviour. Specifically,
we consider fluctuations stemming from ligand concentration, receptor switching
between their signalling states, adaptation, modification of proteins by
phosphorylation, and motor switching between its two rotational states. We
develop a model which includes all signalling steps in the pathway, and discuss
a simplified version, which captures the essential features of the full model.
We find that the noise characteristics of the motor contain signatures from all
these processes, albeit with varying magnitudes. This allows us to address how
cell-to-cell variation affects motor behaviour and the question of optimal
pathway design. A similar comprehensive analysis can be applied to other
two-component signalling pathways.Comment: 22 pages, 7 figures, 3 tutorials, supplementary information;
submitted manuscrip
Pathway analysis reveals functional convergence of gene expression profiles in breast cancer
Abstract Background A recent study has shown high concordance of several breast-cancer gene signatures in predicting disease recurrence despite minimal overlap of the gene lists. It raises the question if there are common themes underlying such prediction concordance that are not apparent on the individual gene-level. We therefore studied the similarity of these gene-signatures on the basis of their functional annotations. Results We found the signatures did not identify the same set of genes but converged on the activation of a similar set of oncogenic and clinically-relevant pathways. A clear and consistent pattern across the four breast cancer signatures is the activation of the estrogen-signaling pathway. Other common features include BRCA1-regulated pathway, reck pathways, and insulin signaling associated with the ER-positive disease signatures, all providing possible explanations for the prediction concordance. Conclusion This work explains why independent breast cancer signatures that appear to perform equally well at predicting patient prognosis show minimal overlap in gene membership.</p
- …