A pilot study: Metabolic profiling of plasma and saliva samples from newly diagnosed glioblastoma patients

Abstract

Background Despite aggressive treatment, more than 90% of glioblastoma (GBM) patients experience recurrences. GBM response to therapy is currently assessed by imaging techniques and tissue biopsy. However, difficulties with these methods may cause misinterpretation of treatment outcomes. Currently, no validated therapy response biomarkers are available for monitoring GBM progression. Metabolomics holds potential as a complementary tool to improve the interpretation of therapy responses to help in clinical interventions for GBM patients. Methods Saliva and blood from GBM patients were collected pre and postoperatively. Patients were stratified conforming their progression‐free survival (PFS) into favourable or unfavourable clinical outcomes (>9 months or PFS ≤ 9 months, respectively). Analysis of saliva (whole‐mouth and oral rinse) and plasma samples was conducted utilising LC‐QqQ‐MS and LC‐QTOF‐MS to determine the metabolomic and lipidomic profiles. The data were investigated using univariate and multivariate statistical analyses and graphical LASSO‐based graphic network analyses. Results Altogether, 151 metabolites and 197 lipids were detected within all saliva and plasma samples. Among the patients with unfavourable outcomes, metabolites such as cyclic‐AMP, 3‐hydroxy‐kynurenine, dihydroorotate, UDP and cis‐aconitate were elevated, compared to patients with favourable outcomes during pre‐and post‐surgery. These metabolites showed to impact the pentose phosphate and Warburg effect pathways. The lipid profile of patients who experienced unfavourable outcomes revealed a higher heterogeneity in the abundance of lipids and fewer associations between markers in contrast to the favourable outcome group. Conclusion Our findings indicate that changes in salivary and plasma metabolites in GBM patients can potentially be employed as less invasive prognostic biomarkers/biomarker panel but validation with larger cohorts is required.

Diagnostic Value of Salivary Amino Acid Levels in Cancer

“…A study by Bark J.M. et al [43] identified valine among a large number of metabolites, which overlaps with the results of García-Villaescusa A. et al [42] and other studies showing the contribution of valine to cancer diagnosis.…”

“…In one study, additional filtration of contaminants in saliva was performed using a centrifugal filter (Nanosep ® ; Pall Corporation, Port Washington, NY, USA) [ 30 ]. In studies [ 32 , 33 , 36 , 37 , 42 , 43 , 46 ], saliva was frozen without prior centrifugation. In studies [ 26 , 40 , 41 ], proteins were pre-sedimented before freezing.…”

“…The amino acid composition of saliva is determined independently via liquid chromatography [ 26 ], as well as in the course of complex analysis of metabolites using gas chromatography coupled to a mass spectrometer (GC-MS) [ 33 , 45 ], nuclear magnetic resonance (NMR) spectroscopy [ 42 ], and various variants of mass spectrometry [ 24 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 , 36 , 40 , 41 , 43 , 44 , 46 ]. Also surface-enhanced Raman scattering (SERS) Sensors using metal nanoparticles, especially gold nanoparticles (AuNPs) and silver nanoparticles [ 38 ], DNA/Ag NCs-based biosensing system [ 37 ], carbon dots confined in N-doped carbon (CDs@NC) for colorimetric detection of D-Pro and D-Ala [ 39 ] is used; however, in these variants, one or a few amino acids are determined.…”

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

“…However, this has been a minor obstacle in metabolomics studies since the detection methods are highly sensitive [ 146 ]. Given the richness and metabolic response it provides, saliva samples have been used in screening for diagnosis and monitoring T2DM [ 148 , 149 , 150 ], neurological diseases (such as Alzheimer’s [ 151 ] and schizophrenia [ 152 ]), and some types of cancers (such as oral, breast, cerebral, colorectal, and gastric cancers) [ 153 , 154 , 155 ]. It is the ideal biofluid for studying oral diseases including periodontitis [ 156 , 157 ] and dental caries [ 158 , 159 ].…”

Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections

“…In addition to multivariate statistics, it provides strong potential for understanding the key biomarkers or pathways involved in gut processes. A recent study by Muller Bark, et al [ 129 ] applied machine learning (ML) to elaborate the metabolome and lipidome associations of glioblastoma patients through graphical network analysis. The ML analysis indicated homogenous networks with lipid cluster linkages between the key metabolic markers.…”