Date of Award

Spring 2014

Degree Type


Degree Name

Master of Science (MS)



First Advisor

Kari L. Clase

Committee Chair

Kari L. Clase

Committee Member 1

Jenna L. Rickus

Committee Member 2

John A. Springer


Glioblastoma Multiforme (GBM) is the most common and malignant form of the primary brain tumor. Due to its highly invasive nature, current treatment options have not been able to improve the survival rate in past 20 years. In order to discover GBM therapeutic targets, omics technologies have been widely used to identify potential biomarkers. ^ This research study focused on investigating lipid biomarkers of human GBM orthotopic mouse models employing mass spectrometry. Human tumor cell lines GBM10 and GBM43 were injected in the right cerebral hemisphere and flank sites in NOD/SCID mice (n = 10 mice per group). Left cerebral hemispheres of the mouse brains were harvested as control tissue. After harvesting brain and flank tumors and control brain from the xenograft models, protein, metabolites, and lipids of tumor samples were collected through the simple extraction procedure. These samples were analyzed by reverse phase high performance liquid chromatography - Fourier transform ion cyclotron resonance mass spectrometry (RHPLC-FTMS). FTMS analyzers have the highest resolving power of all MS instruments, which is ideal for complex mixtures such as GBM tissue. Spectra obtained from the FTMS analysis were analyzed using Student t-tests to detect significant differences in tissue profiles at a level of p = 0.05. Compounds below this threshold were identified through a database using the m/z ratio. ^ Lipidomic analysis indicated the possible differentially expressed lipids classes in GBM tissues, and connected to metabolic pathways, tumor proliferation and immunodepression. Most significantly expressed lipids were glycerophospholipids, glycerophosphocholines, glycerophoserines, and triradylglycerols. Accompanying these studies is a collaborative effort to improve the effectiveness and efficiency of computational pipelines that are imperative to the analytics, visualization, identification, and interpretation of the omics data. Only by carefully integrating the computational pipelines can we successfully perform the types of integrative studies needed to advance the identification of cancer biomarkers for diagnosis and prognosis, and our integrative studies serve as a case study for our pipeline advancement efforts.