Investigating the Potential of Lipids for Use as Biomarkers for Glioblastoma via an Untargeted Lipidomics Approach

Abstract

Objective: The types and functions of lipids involved in glioblastoma (GB) are not well known. Lipidomics is a new field that examines cellular lipids on a large scale and novel aplication of lipidomics in the biomedical sciences have emerged. This study aimed to investigate the potential of blood lipids for use as biomarkers for the diagnosis of GB via untargated lipidomic approach. Gaining a deeper understanding of lipid metabolism in patients with GB can contribute to the early diagnosis with GB patiens and also development of novel and better therapeutic options.

Methods: This study was performed using blood samples collected from 14 patients (eight females and six males) and 14 controls (eight females and six males). Lipids were extracted from blood samples and quantified using phosphorus assay. Lipid profiles of between patients with GB and controls were compared via an untargeted lipidomics approach using 6530 Accurate-Mass Q-TOF LC/MS mass spectrometer.

Results: According to the results obtained using the untargeted lipidomics approach, differentially regulated lipid species, including fatty acid (FA), glycerolipid (GL), glycerophospholipid (PG), saccharolipid (SL), sphingolipid (SP), and sterol lipid (ST) were identified between in patients with GB and controls.

Conclusion: Differentially regulated lipids were identified in patients with GB, and these lipid species were predicted as potential biomarkers for diagnosis of GB.

Keywords: Biomarkers; Cancer; Electrospray Ionization mass spectrometry; Glioblastoma; Lipidomics.

Fig. 1.

Principal component analysis (2d-PcA; A), partial least squares discriminant analysis (2d-PLS-dA; b), t-test (c), fold change (Fc) (d), and volcano (Vc) (e) plots of patients with glioblastoma and controls in positive ion mode.

Fig. 2.

Orthogonal projections to latent structures discriminant analysis (OrthoPLSdA) diagram (A) and Orthogonal projections to latent structures discriminant analysis-variable importance projection (OrthoPLSdA-VIP) score (b) plot for lipid types different between patients with glioblastoma and controls (positive ion mode).

Fig. 3.

Principal component analysis (2d-PcA; A), partial least squares discriminant analysis (2d-PLS-dA; b), t-test (c), fold change (Fc) (d), and volcano (Vc) (e) plots of patients with glioblastoma and controls in negative ion mode.

Fig. 4.

Orthogonal projections to latent structures discriminant analysis (OrthoPLSdA) diagram (A) and Orthogonal projections to latent structures discriminant analysis-variable importance projection (OrthoPLSdA-VIP) score (b) plot for lipid types different between patients with glioblastoma and controls (negative ion mode).