Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy
Bibliographic record
Abstract
BACKGROUND: Stereotactic body radiotherapy (SBRT) is an effective treatment for hepatocellular carcinoma (HCC). This study sought to identify differentially expressed plasma metabolites in HCC patients at baseline and early during SBRT, and to explore if changes in these metabolites early during SBRT may serve as biomarkers for radiation-induced liver injury and/or tumour response. METHODS: Forty-seven HCC patients were treated with SBRT on previously published prospective trials. Plasma samples were collected at baseline and after one to two fractions of SBRT, and analysed by GC/MS and LC/MS for untargeted and targeted metabolomics profiling, respectively. FINDINGS: Sixty-nine metabolites at baseline and 62 metabolites after one to two fractions of SBRT were differentially expressed, and strongly separated the Child Pugh (CP) B from the CP A HCC patients. These metabolites are associated with oxidative stress and alterations in hepatic cellular metabolism. Differential upregulation of serine, alanine, taurine, and lipid metabolites early during SBRT from baseline was noted in the HCC patients who demonstrated the greatest increase in CP scores at three months post SBRT, suggesting that high protein and lipid turnover early during SBRT may portend increased clinical liver toxicity. Twenty annotated metabolites including fatty acids, glycerophospholipids, and acylcarnitines were differentially upregulated early during SBRT from baseline and separated patients with complete/partial response from those with stable disease at three months post SBRT. INTERPRETATION: Dysregulation of amino acid and lipid metabolism detected early during SBRT are associated with subsequent clinical liver injury and tumour response in HCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".