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Powerful data analysis improves gene expression technology

Laboratories are demanding ever more sensitivity and reliability from their gene expression analysis technologies. Here we look at what these technologies have to offer.

Les laboratoires exigent une sensibilité et une fiabilité de plus en plus élevées de leurs technologies d’expression génétique. Voici un aperçu de ce que ces technologies peuvent nous offrir.

Labors verlangen von ihren Genexpressions- analysetechnologien mehr Empfindlichkeit und Zuverlässigkeit. Hier zeigen wir, was diese Technologien zu bieten haben.


epresenting roughly 30 per cent of all primary brain tumours, meningiomas

are classified by the World Health Organisation (WHO) into three tumour grades based on histopathological criteria: grade I for benign slow-growing tumours, and grades II and III for atypical and anaplastic meningiomas.

However, pathologically benign meningiomas can recur, even after operations to remove them. And the clinical course of this substantial minority of meningiomas is difficult to predict – not least because the molecular mechanisms underlying them is poorly understood.

Taking advantage of Qlucore Omics Explorer’s powerful data analysis capabilities, its speed, and its ability to work on any PC, Dr Bárbara Meléndez and her colleagues in the Unidad de Investigación de Patología Molecular (Molecular Pathology

Research Unit) at Hospital Virgen de la Salud in Toledo, Spain, are investigating how such generally benign meningiomas can start to exhibit the sort of histology and recurrence usually associated with more aggressive tumours.

In order to do this, Meléndez and her team are using Qlucore Omics Explorer to perform gene expression and methylation analyses of brain tumour samples and then validating them with data from other databases.

To begin with, the researchers reduce variance to identify groups of samples. Ten they perform supervised analyses to identify differentially methylated or expressed genes.

In addition, oligonucleotide probes are used to analyse gene expression. Each sample can generate around 41,000 gene expression data, resulting in huge data files because

of the number of samples involved (Fig. 1).

Using Qlucore principal component analysis (PCA) the team can identify groups of tumours that have the same histopathology, despite being different at the molecular level. Such differences can be used to help identify targets for different therapies and to improve on disease prognoses.

Also important is Qlucore’s 3D graphics capability: “3D lets us see individual groups that we don’t always see with cluster analysis alone. So while the diagnosis might be the same and the tumours have the same histology, they might be different pathologically. Tis is very important because it also gives us an idea of which tumours behave a little bit differently to other samples in that group. So they should not really be in that group,” adds Meléndez.

And it is here that Qlucore has helped the team to a dramatic discovery. “Expression analyses allowed us to identify that meningiomas can be classified into an aggressive and a non- aggressive group - despite WHO classification criteria establishing three malignancy groups and about 15 histopathological subtypes. Tese findings identify tumours that recur in the aggressive group, even if they have a benign histopathological diagnosis. Supervised analyses have allowed us to identify gene expression profiles associated with more aggressive tumours – findings that have been confirmed by analysing the methylation data with Qlucore,” she concludes.

Fig. 1. An example on the user interface when markers are identified for different groups.

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