Glioblastoma is the most common and deadly type of brain cancer. Over the past decade, several divergent genetic pathways have been implicated in the initiation, progression and clinical outcome of this disease. As our understanding of GBM expands and identifies actionable targets specific to individual tumors, there will be a pressing need for the development of new tools that will maximize the use of limited clinical samples to enable the employment of personalized care paradigms. We used PrimePCR validated assays to generate a custom real-time PCR screening panel, containing 74 previously published mRNA targets showing gene expression changes in glioblastoma, and five house-keeping genes. A cohort of 19 frozen brain specimens were analyzed, including WHO Grade II oligodendroglioma (n = 3), WHO Grade II astrocytoma (n = 2), WHO Grade III astrocytoma (n = 1), and glioblastoma (n = 13). Four normal brain samples were also analyzed. We performed RNA extraction, followed by cDNA synthesis, multiplexed pre-amplification and SYBR-based qPCR, to generate expression profiles on all samples. We demonstrated that the workflow shows high tolerance to variation in RNA quality (RIN 8.5-4) and high sensitivity in detection. cDNA input that is equivalent to 3 ng of starting RNA was sufficient to conduct accurate semiquantitative analysis of the panel of 79 assays. Using principal component analysis, we were able to accurately separate glioblastoma from low-grade glioma. The two WHO Grade III tumors analyzed clustered with glioblastoma, but showed more similarity to Grade II gliomas. In this study, we have shown the feasibility of consolidating high-throughput data into a single functional panel capable of accurately classifying glioma specimens based solely on semiquantitative gene expression profiling.