Objective Diagnosis of a glioblastoma (GBM) is triggered by the onset of symptoms and based on cerebral imaging and histological examination. Serum based biomarkers may support detection of GBM. Here, we explored serum protein concentrations in GBM patients and used data mining to explore profiles of biomarkers and whether these associate with clinical status of the GBM patients. Methods Gene and protein expression data for astrocytoma and GBM were used to identify secreted proteins differentially expressed in tumors versus normal brain tissues. The tumor expression and serum concentrations of 14 candidate proteins were analyzed in 23 GBM patients and 9 healthy subjects. Data mining methods involving all 14 proteins were applied as an initial evaluation step to find clinically informative profiles. Results Data mining identified a serum protein profile formed by BMP2, HSP70 and CXCL10 that allowed correct assignment into the GBM group with a specificity and sensitivity of 89% and 96%, respectively (p<0.0001, Fischer's exact test). Survival after tumor resection for more than 15 month was associated with a profile formed by TSP1, HSP70 and IGFBP3, allowing correct assignments in all cases (p<0.0001, Fischer's exact test). No correlation was found with tumor size and age of the patient. Conclusion The study shows that robust serum profiles for GBM may be identified by data mining based on a relatively small study cohort. Profiles of more than one biomarker allow a more specific assignment to the GBM and survival group than single proteins, confirming earlier attempts to correlate single markers with cancer. These findings will be a basis for validation in a larger sample size.