Serum metabolomics uncovering specific metabolite signatures of intra- and extrahepatic cholangiocarcinoma
Bibliographic record
Abstract
Cholangiocarcinoma (CC) accounts for approximately 25% of all hepatobiliary malignancies, including intra- and extrahepatic cholangiocarcinoma (ICC and ECC) and has a high mortality rate. The clinical manifestations of and liver function tests for the ICC and ECC diseases are too similar to distinguish between them. Diagnosis of ICC and ECC remains difficult because of the lack of sensitive diagnostic tests, although MRI and CT with endoscopic ultrasound provide useful diagnostic information in certain patients, but are invasive, time-consuming or expensive. Early detection is the most effective way to improve the clinical outcome of CC. Serum metabolomics provides a powerful platform for discovering novel biomarkers to improve early diagnosis. This study was performed using a metabolomics method which was used to select serum metabolites to be used for the early diagnosis of CC and to distinguish ICC from ECC. We comprehensively analyzed the serum metabolites in a total of 261 blood samples from CC patients and normal individuals. We found that 75 metabolites were filtered and identified from the serum metabolome, and the levels of 21-deoxycortisol and bilirubin significantly increased while the levels of lysoPC(14:0) and lysoPC(15:0) were significantly reduced in the CC group compared with the control groups. We measured the 4 metabolites of interest in an independent sample comprising 225 cases and 101 controls. Noticeably, external validation of the serum specimens further showed that the biomarker combination could differentiate ECC and ICC patients with high accuracy. This provides a new foundation for serum metabolomics to provide potential biomarkers for the early detection of CC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".