Microarray-Based Platforms and Strategies for the Development of Molecular-Centric Diagnostic Tools

A special issue of Microarrays (ISSN 2076-3905).

Deadline for manuscript submissions: closed (31 July 2015) | Viewed by 11833

Special Issue Editor


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Guest Editor
Wake Forest Institute for Regenerative Medicine, Wake Forest University, Winston Salem, NC 27156, USA
Interests: gene expression profiling; diagnostic biomarkers for complex disease; peripheral biomarkers for diagnosis and disease monitoring; peripheral biomarkers for assessing therapeutic efficacy; developing classifiers

Special Issue Information

Dear Colleagues,

The heterogeneous and sometimes overlapping presentation in complex diseases can result in an inability to provide a timely and definitive diagnosis. The clinical practices of gastroenterology (e.g., in the case of Crohn’s disease versus ulcerative colitis) and urology (e.g., in the case of interstitial cystitis versus bladder pain syndrome) offer but two examples among many wherein it is not always possible to provide a clear discrimination between two similar disease presentations. Assessing disease stage or disease progression (e.g., in Parkinson’s disease) can present a similar challenge. These diagnostic ambiguities can ultimately result in less than optimal treatment strategies and, therefore, less than optimal outcomes. In this era of developing a more personalized approached to patient care, the discovery of additional robust (i.e., both sensitive and specific) diagnostic tools to assist the clinician is an absolute priority.

Molecular profiling—specifically those approaches that employ microarray-based platforms—of both patient-derived disease tissues, and also peripheral tissues (e.g., blood, urine, CSF), provides the research community with a formidable set of technologies to bring to bear for the development of these new clinical diagnostic tools. This special issue focuses on all aspects of microarray-based biomarker development for the diagnosis, monitoring, and treatment of complex disease. The issue will emphasize the various microarray-based approaches currently in use across a variety of diseases.

Dr. Stephen J. Walker
Guest Editor

Manuscript Submission Information

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Keywords

  • microarray
  • biomarker
  • diagnostic
  • peripheral biomarker
  • personalized medicine
  • classifiers

Published Papers (2 papers)

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Research

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Article
Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample
by Samuel Chao, Changming Cheng and Choong-Chin Liew
Microarrays 2015, 4(4), 671-689; https://doi.org/10.3390/microarrays4040671 - 10 Dec 2015
Cited by 4 | Viewed by 4929
Abstract
Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological [...] Read more.
Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the “noisy” variability of whole blood. Methods: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes. Results: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls. Conclusion: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests. Full article
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Article
Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer
by Clare Coveney, David J. Boocock, Robert C. Rees, Suha Deen and Graham R. Ball
Microarrays 2015, 4(3), 324-338; https://doi.org/10.3390/microarrays4030324 - 17 Jul 2015
Cited by 3 | Viewed by 6680
Abstract
The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a [...] Read more.
The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy. Full article
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