Proteomic dataset: Profiling of glioma C6 and astrocytes rat cell lines before and after co-cultivation.

Human multiforme glioblastoma is characterized by an unfavorable prognosis, low survival rate and extremely limited possibilities for therapy. Rat C6 glioma is an experimental model for the study of glioblastoma growth and invasion. It has been shown that the growth and development of the tumor is accompanied by changes in the surrounding normotypic tissues [1]. These changes create a favorable environment for the development of the tumor and give it an evolutionary advantage [2]. Description of changes occurring in normotypic cells of the body upon their contact with tumor cells is of great interest. We have grown C6 glioma cells and rat astrocytes, as well as astrocyte cells co-cultured together with C6 glioma. We performed proteome-wide LC-MS analysis of these experimental groups. The data includes LC-MS/MS raw files and exported MaxQuant and ProteinPilot search results with fasta. Dataset published in the PRIDE repository project accession PXD026776.

Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics.

Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.