Expression data from glioblastoma tissue and normal brain tissue samples (from epilepsy surgery)

The characterization of glioblastoma has provided invaluable data related to this molecularly heterogeneous disease. Recent advances in high-throughput microarrays have received extensive attention and made substantial progress in reconstructing the gene regulatory network of medical biology. Using microarray analysis, significant differences in gene expression between normal and disease tissues have been observed. However, as a result of the underlying shortcomings of microarray technology, such as small sample size, measurement error, and information insufficiency, unveiling this disease mechanism has remained a major challenge to glioblastoma research. Hence, GO, pathway information, network-based approaches and machine learning algorithms have been employed to identify the mechanisms underlying this disease. We identified the differentially expressed genes (DEGs) between 9 glioblastoma samples and 9 normal brain samples. The datasets available in this analysis contained 18 samples, including 9 glioblastoma tissue samples and 9 normal brain tissue samples from epilepsy surgery. All specimens had confirmed pathological diagnosis and were classified according to the World Health Organization (WHO) criteria. All the tumor samples were obtained from primary surgery. All the tissue were collectd for RNA extraction and hybridization on Affymetrix microarrays, and for other analysis.