Dataset Information

Experimental validation of 5 in-silico predicted glioma biomarkers.

ABSTRACT:

Background

Glioblastoma multiforme (GBM) is a high-grade glioma with poor prognosis. Identification of new biomarkers specific to GBM could help in disease diagnosis. We have developed and validated a bioinformatics method to predict proteins likely to be suitable as glioma biomarkers via a global microarray meta-analysis to identify uncharacterized genes consistently coexpressed with known glioma-associated genes.

Methods

A novel bioinformatics method was implemented called global microarray meta-analysis, using approximately 16,000 microarray experiments to identify uncharacterized genes consistently coexpressed with known glioma-associated genes. These novel biomarkers were validated as proteins highly expressed in human gliomas varying in tumor grades using immunohistochemistry. Glioma gene databases were used to assess delineation of expression of these markers in varying glioma grades and subtypes of GBM.

Results

We have identified 5 potential biomarkers-spondin1, Plexin-B2, SLIT3, fibulin-1, and LINGO1-that were validated as proteins highly expressed on the surface of human gliomas using immunohistochemistry. Expression of spondin1, Plexin-B2, and SLIT3 was significantly higher (P < .01) in high-grade gliomas than in low-grade gliomas. These biomarkers were significant discriminators in grade IV gliomas compared with either grade III or II tumors and also distinguished between GBM subclasses.

Conclusions

This study strongly suggests that this type of bioinformatics approach has high translational potential to rapidly discern which poorly characterized proteins may be of clinical relevance.

SUBMITTER: Towner RA

PROVIDER: S-EPMC3829592 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Experimental validation of 5 in-silico predicted glioma biomarkers.

Towner Rheal A RA Jensen Randy L RL Vaillant Brian B Colman Howard H Saunders Debra D Giles Cory B CB Wren Jonathan D JD

Neuro-oncology 20131024 12

<h4>Background</h4>Glioblastoma multiforme (GBM) is a high-grade glioma with poor prognosis. Identification of new biomarkers specific to GBM could help in disease diagnosis. We have developed and validated a bioinformatics method to predict proteins likely to be suitable as glioma biomarkers via a global microarray meta-analysis to identify uncharacterized genes consistently coexpressed with known glioma-associated genes.<h4>Methods</h4>A novel bioinformatics method was implemented called globa ...[more]

PMID: 24158112

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Project description:Computational methods have been widely used for the prediction of protein subcellular localization. However, these predictions are rarely validated experimentally and as a result remain questionable. Therefore, experimental validation of the predicted localizations is needed to assess the accuracy of predictions so that such methods can be confidently used to annotate the proteins of unknown localization. Previously, we published a method called ngLOC that predicts the localization of proteins targeted to ten different subcellular organelles. In this short report, we describe the accuracy of these predictions using experimental validations.We have experimentally validated the predicted subcellular localizations of 114 human proteins corresponding to nine different organelles in normal breast and breast cancer cell lines using live cell imaging/confocal microscopy. Target genes were cloned into expression vectors as GFP fusions and cotransfected with RFP-tagged organelle-specific gene marker into normal breast epithelial and breast cancer cell lines. Subcellular localization of each target protein is confirmed by colocalization with a co-expressed organelle-specific protein marker. Our results showed that about 82.5% of the predicted subcellular localizations coincided with the experimentally validated localizations. The highest agreement was found in the endoplasmic reticulum proteins, while the cytoplasmic location showed the least concordance. With the exclusion of cytoplasmic location, the average prediction accuracy increased to 90.4%. In addition, there was no difference observed in the protein subcellular localization between normal and cancer breast cell lines.The experimentally validated accuracy of ngLOC method with (82.5%) or without cytoplasmic location (90.4%) nears the prediction accuracy of 89%. These results demonstrate that the ngLOC method can be very useful for large-scale annotation of the unknown subcellular localization of proteins.

Project description:BACKGROUND:Restriction site-associated DNA sequencing (RADseq) has revolutionized the study of wild organisms by allowing cost-effective genotyping of thousands of loci. However, for species lacking reference genomes, it can be challenging to select the restriction enzyme that offers the best balance between the number of obtained RAD loci and depth of coverage, which is crucial for a successful outcome. To address this issue, PredRAD was recently developed, which uses probabilistic models to predict restriction site frequencies from a transcriptome assembly or other sequence resource based on either GC content or mono-, di- or trinucleotide composition. This program generates predictions that are broadly consistent with estimates of the true number of restriction sites obtained through in silico digestion of available reference genome assemblies. However, in practice the actual number of loci obtained could potentially differ as incomplete enzymatic digestion or patchy sequence coverage across the genome might lead to some loci not being represented in a RAD dataset, while erroneous assembly could potentially inflate the number of loci. To investigate this, we used genome and transcriptome assemblies together with RADseq data from the Antarctic fur seal (Arctocephalus gazella) to compare PredRAD predictions with empirical estimates of the number of loci obtained via in silico digestion and from de novo assemblies. RESULTS:PredRAD yielded consistently higher predicted numbers of restriction sites for the transcriptome assembly relative to the genome assembly. The trinucleotide and dinucleotide models also predicted higher frequencies than the mononucleotide or GC content models. Overall, the dinucleotide and trinucleotide models applied to the transcriptome and the genome assemblies respectively generated predictions that were closest to the number of restriction sites estimated by in silico digestion. Furthermore, the number of de novo assembled RAD loci mapping to restriction sites was similar to the expectation based on in silico digestion. CONCLUSIONS:Our study reveals generally high concordance between PredRAD predictions and empirical estimates of the number of RAD loci. This further supports the utility of PredRAD, while also suggesting that it may be feasible to sequence and assemble the majority of RAD loci present in an organism's genome.

Dataset Information

Experimental validation of 5 in-silico predicted glioma biomarkers.

Background

Methods

Results

Conclusions

Publications

Experimental validation of 5 in-silico predicted glioma biomarkers.

Similar Datasets

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