6,870 research outputs found

    Network-based stratification of tumor mutations.

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    Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

    miRNAs in lung cancer - Studying complex fingerprints in patient's blood cells by microarray experiments

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    <p>Abstract</p> <p>Background</p> <p>Deregulated miRNAs are found in cancer cells and recently in blood cells of cancer patients. Due to their inherent stability miRNAs may offer themselves for blood based tumor diagnosis. Here we addressed the question whether there is a sufficient number of miRNAs deregulated in blood cells of cancer patients to be able to distinguish between cancer patients and controls.</p> <p>Methods</p> <p>We synthesized 866 human miRNAs and miRNA star sequences as annotated in the Sanger miRBase onto a microarray designed by febit biomed gmbh. Using the fully automated Geniom Real Time Analyzer platform, we analyzed the miRNA expression in 17 blood cell samples of patients with non-small cell lung carcinomas (NSCLC) and in 19 blood samples of healthy controls.</p> <p>Results</p> <p>Using t-test, we detected 27 miRNAs significantly deregulated in blood cells of lung cancer patients as compared to the controls. Some of these miRNAs were validated using qRT-PCR. To estimate the value of each deregulated miRNA, we grouped all miRNAs according to their diagnostic information that was measured by Mutual Information. Using a subset of 24 miRNAs, a radial basis function Support Vector Machine allowed for discriminating between blood cellsamples of tumor patients and controls with an accuracy of 95.4% [94.9%-95.9%], a specificity of 98.1% [97.3%-98.8%], and a sensitivity of 92.5% [91.8%-92.5%].</p> <p>Conclusion</p> <p>Our findings support the idea that neoplasia may lead to a deregulation of miRNA expression in blood cells of cancer patients compared to blood cells of healthy individuals. Furthermore, we provide evidence that miRNA patterns can be used to detect human cancers from blood cells.</p

    Insights into the regulation of intrinsically disordered proteins in the human proteome by analyzing sequence and gene expression data

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    Background: Disordered proteins need to be expressed to carry out specified functions; however, their accumulation in the cell can potentially cause major problems through protein misfolding and aggregation. Gene expression levels, mRNA decay rates, microRNA (miRNA) targeting and ubiquitination have critical roles in the degradation and disposal of human proteins and transcripts. Here, we describe a study examining these features to gain insights into the regulation of disordered proteins. Results: In comparison with ordered proteins, disordered proteins have a greater proportion of predicted ubiquitination sites. The transcripts encoding disordered proteins also have higher proportions of predicted miRNA target sites and higher mRNA decay rates, both of which are indicative of the observed lower gene expression levels. The results suggest that the disordered proteins and their transcripts are present in the cell at low levels and/or for a short time before being targeted for disposal. Surprisingly, we find that for a significant proportion of highly disordered proteins, all four of these trends are reversed. Predicted estimates for miRNA targets, ubiquitination and mRNA decay rate are low in the highly disordered proteins that are constitutively and/or highly expressed. Conclusions: Mechanisms are in place to protect the cell from these potentially dangerous proteins. The evidence suggests that the enrichment of signals for miRNA targeting and ubiquitination may help prevent the accumulation of disordered proteins in the cell. Our data also provide evidence for a mechanism by which a significant proportion of highly disordered proteins (with high expression levels) can escape rapid degradation to allow them to successfully carry out their function

    SFSSClass: an integrated approach for miRNA based tumor classification

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    Background: MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. Results: In this article, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is similar to 76.5%, the accuracy of SFSSClass is found to be similar to 82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass (integration of SAMBA, cancer-miRNA network and USC) over USC (eg., similar to 70.5% for SFSSClass versus similar to 58.8% in classifying a set of 17 MDT samples from 9 tumor types, similar to 91.7% for SFSSClass versus similar to 75% in classifying 12 cell lines from 6 tumor types and similar to 382.3% for SFSSClass versus similar to 41.2% in classifying 17 PDT samples from 11 tumor types). Conclusion: In this article, we develop the SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low interclass similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches

    High-throughput miRNA profiling of human melanoma blood samples

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    <p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool.</p> <p>Methods</p> <p>Using a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set.</p> <p>Results</p> <p>A hypothesis test based approch detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR.</p> <p>Conclusions</p> <p>Our study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma.</p
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