# Integrative Multi-Omics Analysis Reveals the Characteristic Metabolic Signature of Glioma and Enables Plasma-Based Liquid Biopsy

## TL;DR

This study identifies a plasma-based metabolic signature for glioma that can be used as a non-invasive diagnostic tool.

## Contribution

The novel contribution is a plasma-based liquid biopsy model using 7 metabolites that accurately detects glioma with high specificity.

## Key findings

- Aberrations in 'Alanine, aspartate, and glutamate metabolism' and 'TCA cycle' are common across glioma subtypes and progression.
- A plasma-based model with 7 metabolites achieved high diagnostic accuracy (AUC = 0.964) for adult glioma.
- The model showed higher sensitivity for glioma (0.885) compared to pancreatic cancer (0.800), indicating tumor selectivity.

## Abstract

Liquid biopsy strategies for glioma leveraging metabolic features remain inadequately investigated. Herein, we performed liquid chromatography-mass spectrometry-based metabolomic and proteomic analyses on 189 tissue samples from 122 adult glioma patients, and nuclear magnetic resonance-based targeted metabolomic profiling on plasma samples from 430 participants encompassing 82 adult glioma patients, 53 pediatric primary brain tumor patients, 80 pancreatic cancer patients, and 215 nontumor controls. The results demonstrate that aberrations in “Alanine, aspartate, and glutamate metabolism” and “tricarboxylic acid (TCA) cycle” pathways are ubiquitous across subtypes and progression of glioma. Notably, these signatures could be captured in plasma, thereby reflecting shared metabolic features between tumor tissues and circulation. Based on these findings, we developed a liquid biopsy model comprising 7 plasma metabolites (including creatine, lactic acid, succinic acid, N,N-dimethylglycine, 2-oxoglutaric acid, acetic acid, and glutamic acid). This model achieved high diagnostic accuracy in independent test sets (area under the curve = 0.964 for adult glioma set; and 0.925 for pediatric primary brain tumor set). Meanwhile, the model exhibited a higher sensitivity of 0.885 for glioma compared to 0.800 for pancreatic cancer, providing evidence to support the tumor selectivity of the model. Together, we present a plasma-based metabolomic classifier that faithfully mirrors the core metabolic reprogramming of glioma and can serve as a readily available liquid biopsy tool.