Comparative Analysis of Amino Acid Profiles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS)

Sep 24, 2026

Brain tumors account for a small but significant fraction of newly diagnosed cancers and frequently cause major neurological dysfunction, leading to poor functional status and disability. Biochemical markers of intracranial neoplasms have limited diagnostic value. This study uses LC-ESI-MS/MS with multivariate statistics to compare amino acid metabolic profiles across glioblastoma, meningioma, and an osteoarthritis spine control group, identifying lysine, histidine, -aminoadipic acid, and phenylalanine as discriminative markers. Diagnostic performance was assessed by ROC analysis, with phenylalanine showing the best results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Gliomas and high-grade gliomas

Meningiomas and contributing factors

Pathophysiological basis of symptoms

Molecular mechanisms of intracranial tumor development

Diagnostic and monitoring approaches

molecules Article Comparative Analysis of Amino Acid Proﬁles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS) Piotr Ko´sli ´nski 1, *, Robert Pluskota 1, Marcin Koba 1, Zygmunt Siedlecki 2 and Maciej ´Sniegocki 2 Citation: Ko´sli ´nski, P.; Pluskota, R.; Koba, M.; Siedlecki, Z.; ´Sniegocki, M. Comparative Analysis of Amino Acid Proﬁles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS) . Molecules 2023, 28, 7699. [https://](https://)[ ](https://)[doi.org/10.3390/molecules28237699](doi.org/10.3390/molecules28237699) Academic Editors: Xianjiang Li and Wen Ma Received: 4 October 2023 Revised: 20 November 2023 Accepted: 21 November 2023 Published: 22 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Toxicology and Bromatology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toru ´n, dr. A. Jurasza 2, 85-089 Bydgoszcz, Poland; [pluskota.r@gmail.com](pluskota.r@gmail.com) (R.P.); [kobamar@cm.umk.pl](kobamar@cm.umk.pl) (M.K.) 2 Department of Neurosurgery, Neurotraumatology and Pediatric Neurosurgery, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toru ´n, 85-094 Bydgoszcz, Poland; [siedlecki@cm.umk.pl](siedlecki@cm.umk.pl) (Z.S.); [sniegocki@cm.umk.pl](sniegocki@cm.umk.pl) (M.´S .) * [Correspondence: piotr.koslinski@cm.umk.pl](Correspondence: piotr.koslinski@cm.umk.pl) Abstract: Brain tumors account for 1% of all cancers diagnosed de novo. Due to the speciﬁcity of the anatomical area in which they grow, they can cause signiﬁcant neurological disorders and lead to poor functional status and disability. Regardless of the results of biochemical markers of intracranial neoplasms, they are currently of no diagnostic signiﬁcance. The aim of the study was to use LC-ESI-MS/MS in conjunction with multivariate statistical analyses to examine changes in amino acid metabolic proﬁles between patients with glioblastoma, meningioma, and a group of patients treated for osteoarthritis of the spine as a control group. Comparative analysis of amino acids between patients with glioblastoma, meningioma, and the control group allowed for the identiﬁcation of statistically signiﬁcant differences in the amino acid proﬁle, including both exogenous and endogenous amino acids. The amino acids that showed statistically signiﬁcant differences (lysine, histidine, 􀀋-aminoadipic acid, phenylalanine) were evaluated for diagnostic usefulness based on the ROC curve. The best results were obtained for phenylalanine. Classiﬁcation trees were used to build a model allowing for the correct classiﬁcation of patients into the study group (patients with glioblastoma multiforme) and the control group, in which cysteine turned out to be the most important amino acid in the decision-making algorithm. Our results indicate amino acids that may prove valuable, used alone or in combination, toward improving the diagnosis of patients with glioma and meningioma. To better assess the potential utility of these markers, their performance requires further validation in a larger cohort of samples. Keywords: glioblastoma; meningioma; amino acid; biomarkers; LC-MS 1. Introduction Intracranial neoplasms account for 2% of cancers, and deaths during these cancers account for 3% of all cancer deaths worldwide. Gliomas are among the most common CNS neoplasms and the most common primary malignant tumors. From a practical point of view, glial tumors are divided into low-grade gliomas (LGGs) and high-grade gliomas (HGGs) [

FAQs

What analytical method and statistics were used to compare amino acid profiles?

The study applied LC-ESI-MS/MS together with multivariate statistical analyses to examine amino acid metabolic profile changes across glioblastoma, meningioma, and a control group.

Which amino acids showed statistically significant differences, and how were they evaluated?

Statistically significant differences were found for lysine, histidine, -aminoadipic acid, and phenylalanine. Their diagnostic usefulness was evaluated using ROC curve analysis.

What classification approach was used, and which amino acid was most important?

Classification trees were used to build a model for assigning patients to study versus control groups. Cysteine emerged as the most important amino acid in the decision-making algorithm.

Sep 24, 2026

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