MétaCan
Menu
← Back to cohort
Record W4416417077 · doi:10.3390/curroncol32110650

Serum p-Cresol and 7-HOCA Levels and Fatty Acid and Purine Metabolism Are Associated with Survival, Progression, and Molecular Classification in GB—Serum Proteome and Metabolome Analysis Pre vs. Post Up-Front Chemoirradiation

2025· article· en· W4416417077 on OpenAlexvenueno aff
Andra Krauze, M. Li, Yingdong Zhao, Shreya Chappidi, Theresa Cooley Zgela, Mary Sproull, Megan Mackey, Kevin Camphausen

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsMetabolomeProteomePurine metabolismMetabolomicsPurineFatty acid metabolismFatty acidMetabolism

Abstract

fetched live from OpenAlex

Background: Glioblastoma (GB) is the most common primary brain tumor, with poor prognosis, significant neurological symptoms, and near-universal recurrence. Biomarker development is often limited by the scarcity of tumor tissue available for study. Noninvasive serum-based profiling offers potential to improve outcomes. Purpose: This study examined serum proteomic and metabolomic profiles pre- and post-concurrent chemoirradiation (CRT) to identify associations with patient outcomes and molecular classification, and to explore relevant signaling and metabolic pathways. Methods: Serum samples from 109 GB patients, obtained prior to and following completion of CRT, were analyzed with each patient serving as their own control, using a SOMAScan® proteomic assay (7289 proteins) and metabolomics (SECIM, 6015 compounds). Clinical data were obtained through chart review. Proteomic and metabolomic changes were examined at baseline (prior to CRT) and in alteration (pre- vs. post-CRT) for their association with overall survival (OS), progression-free survival (PFS), MGMT, and IDH status. Cox models, gene set enrichment analysis (Hallmark, GSEA), and Kaplan–Meier survival analysis were used. Results: Several hundred proteins and metabolites were associated with OS and PFS. MGMT status was known in 60% and IDH in 38% of patients. Pre-CRT DLST (HR 11.7, p < 0.001, adj p = 0.01) was the only protein significantly associated with OS. Pre-CRT, and higher 7-HOCA was linked to worse OS (HR 1.3) and PFS (HR 1.5), while increased p-cresol was associated with improved OS (HR 0.8) and PFS (HR 0.9). Kaplan–Meier analysis based on signal alteration post-CRT vs. pre-CRT, revealed superior OS with lower DLST and MSR1 and superior PFS with higher PGAM2 and ATG5, and lower 7-HOCA. Pathway analysis linked improved PFS to fatty acid metabolism, citric acid cycle, and purine biosynthesis. MGMT and IDH class comparisons revealed associations primarily with amino acid and fatty acid metabolism. Both MGMT methylation and IDH mutation correlated with increased PLAG12B expression, with significance only for MGMT (p < 0.001). IDH mutation was associated with decreased MSR1 (p = 0.047) and p-cresol (p < 0.001). Conclusions: Serum-based fatty acid and purine metabolism pathways are associated with OS and PFS in GB. 7-HOCA and p-cresol emerged as potential biomarkers linked to treatment response and molecular subtype. These findings support further investigation of noninvasive biospecimens for clinically actionable biomarkers in GB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.326
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Oncology→Same topicCancer, Hypoxia, and Metabolism→French-language works237,207→