Back

Cholesteryl Ester as a Prognostic Biomarker In IDH-wildtype Glioblastoma

wang, n.; wang, J.; Liu, J.; Zou, J.; Yang, B.; wang, P.; Ji, N.; Yue, S.

2026-05-08 neuroscience
10.64898/2026.05.05.722825 bioRxiv
Show abstract

Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolomide. Although MGMT methylation is routinely conducted to predict chemosensitivity, its efficacy is often compromised. Thus, there is an urgent need to discover more accurate prognostic biomarkers. Cholesteryl ester (CE) has been recently recognized as a key feature of GBM, however, its role in GBM prognosis remains poorly understood. We first employed label-free stimulated Raman scattering (SRS) imaging to quantitatively analyze CE level in intact tumor tissues obtained from IDH-wildtype GBM patients. Our result revealed significantly prolonged 2-year overall survival (OS) in patients with CE level [&ge;] 40% compared to those with CE level < 40%. CE outperformed MGMT methylation for 2-year OS prognosis (AUC: 0.836 vs. 0.763). Importantly, CE also achieved superior prognostic performance over MGMT methylation on an independent cohort, with higher sensitivity (0.856 vs. 0.667), specificity (0.833 vs. 0.583), NPV (1.00 vs. 0.667), PPV (0.833 vs. 0.583). Given synergistic effects between CE and MGMT methylation, we developed a prognostic model combining these two biomarkers. Specially, machine learning (XGBoost) model exhibited optimal performance in the training cohort (AUC: 0.920), and maintained its superior performance on the independent cohort (sensitivity: 0.946, specificity: 0.873, NPV: 1.00; PPV: 0.917). Mechanistically, integrative analysis of TCGA database linked poor prognosis to the coordinated upregulation of genes involved in cholesterol efflux, hydrolysis, transport, and inhibition of de novo synthesis, unraveling a possible underlying mechanism between poor prognosis and cholesterol metabolism. This work identified CE as a prognostic biomarker for IDH-wildtype GBM.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Advanced Science
249 papers in training set
Top 0.8%
12.7%
2
Theranostics
33 papers in training set
Top 0.1%
10.4%
3
Scientific Reports
3102 papers in training set
Top 15%
6.5%
4
eLife
5422 papers in training set
Top 16%
5.0%
5
National Science Review
22 papers in training set
Top 0.3%
4.1%
6
ACS Chemical Neuroscience
60 papers in training set
Top 0.4%
4.1%
7
iScience
1063 papers in training set
Top 4%
3.7%
8
NeuroImage
813 papers in training set
Top 3%
3.7%
50% of probability mass above
9
Nature Communications
4913 papers in training set
Top 46%
2.4%
10
PLOS ONE
4510 papers in training set
Top 49%
1.9%
11
Communications Biology
886 papers in training set
Top 8%
1.7%
12
eBioMedicine
130 papers in training set
Top 1%
1.7%
13
Neuroscience Bulletin
11 papers in training set
Top 0.3%
1.7%
14
International Journal of Molecular Sciences
453 papers in training set
Top 8%
1.5%
15
Small Methods
26 papers in training set
Top 0.6%
1.3%
16
Neuro-Oncology Advances
24 papers in training set
Top 0.3%
1.3%
17
PLOS Computational Biology
1633 papers in training set
Top 21%
1.0%
18
Cell Reports
1338 papers in training set
Top 30%
0.9%
19
Heliyon
146 papers in training set
Top 5%
0.8%
20
Computational and Structural Biotechnology Journal
216 papers in training set
Top 8%
0.8%
21
Science Bulletin
22 papers in training set
Top 0.7%
0.8%
22
Aging
69 papers in training set
Top 3%
0.8%
23
Communications Medicine
85 papers in training set
Top 1%
0.8%
24
Journal of Alzheimer’s Disease
39 papers in training set
Top 1%
0.8%
25
Frontiers in Neuroscience
223 papers in training set
Top 7%
0.8%
26
Cells
232 papers in training set
Top 6%
0.8%
27
Molecular & Cellular Proteomics
158 papers in training set
Top 2%
0.7%
28
Nano Letters
63 papers in training set
Top 3%
0.7%
29
ACS Omega
90 papers in training set
Top 4%
0.7%
30
Frontiers in Public Health
140 papers in training set
Top 9%
0.7%