List of communications


High-grade Glioma molecular stratification.

Zafra-Villaverde Antonio, Universidad San Pablo CEU, Madrid, Spain; García-Romero Noemí, IMDEA Nanoscience, Madrid, Spain; Esteban-Rubio Susana, Universidad San Pablo CEU, Madrid; Carrión-Navarro Josefa, Fundación Hospital de Madrid-IMMA, Madrid, Spain; Prat-Acín Ricardo, Hospital Universitario la Fe, Valencia, Spain; Fernández-Carballal Carlos, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Belda-Iniesta Cristobal, Fundación Hospital de Madrid-IMMA, Madrid, Spain, IMDEA Nanoscience, Madrid, Spain; Ayuso-Sacido Angel, Fundación Hospital de Madrid-IMMA, Madrid, Spain, IMDEA Nanoscience, Madrid, Spain, Universidad San Pablo-CEU, Madrid, Spain.


Background
The glioblastoma multiforme stratification has challenged the researchers for many years. This problem is critical at the time of both studying and dealing with this disease. The overexpression, mutation and/or deletion of the genes involved in this tumor classification have been used for the identification and characterization of four subtypes: proneural, classic, mesenquimal and neural. A cohort of tumors have been studied with a specific primers designed for this genes to prove stratification. This classification not only helps to lead more accurately in the future investigations, which could be based in only one subtype and not try to englobe this pathology in its whole set. In clinic, the most common therapies nowadays look for this kind of treatment aside from the benefits for lowering prices and secondary effects of the treatment. It also has a positive retro breeding effect over posterior investigations, thanks to allowing the study of the effects of more selective drugs with the subjacent disease.
Results and Conclusions
We have studied a tissues cohort from a range of neoplastic tests with custom primers designed for representative gens from each subtype of the tumor. These molecular tools have been used to evaluate the expression profile of each one of the tissue samples by QRT-PCR. Here we present a collection of molecular tools targeting a specific gen panel which can be useful to drive a quick and informative glioblastoma biology classification.

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