A <scp>DIA</scp> ‐ <scp>MS</scp> ‐based proteomics approach to find potential serum prognostic biomarkers in glioblastoma patients
Bibliographic record
Abstract
No blood‐based protein biomarkers are currently available for routine clinical use to determine the prognosis of patients with glioblastoma (GB). We performed data‐independent acquisition mass spectrometry (DIA‐MS)‐based proteomics on 96 presurgical serum samples from patients with GB and 30 serum samples from healthy controls to identify such markers. Among the 622 serum proteins differentially expressed between the GB and control groups, 191 had a |log 2 (fold change)| ≥ 0.58 and an area under the curve ≥ 0.75. An analysis of their prognostic value revealed that high levels of IL1R2 and low levels of CRTAC1 and HRG were associated with poor survival. Multivariate Cox regression analysis identified IL1R2 as an independent prognostic factor for PFS and CRTAC1 as an independent prognostic factor for OS. The concentration of CRTAC1 in serum samples from an independent cohort of short‐ and long‐term survivors of GB (STS and LTS, respectively) by ELISA was shown to be lower in the STS than in the LTS group. CRTAC1, HRG, and IL1R2 could potentially be used to better inform prognosis and predict treatment response in GB patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".