Novel target genes and a valid biomarker panel identified for cholangiocarcinoma
Bibliographic record
Abstract
Cholangiocarcinoma is notoriously difficult to diagnose, and the mortality rate is high due to late clinical presentation. CpG island promoter methylation is frequently seen in cancer development. In the present study, we aimed at identifying novel epigenetic biomarkers with the potential to improve the diagnostic accuracy of cholangiocarcinoma. Microarray data analyses of cholangiocarcinoma cell lines treated with epigenetic drugs and their untreated counterparts were compared with previously published gene expression profiles of primary tumors and with non-malignant controls. Genes responding to the epigenetic treatment that were simultaneously downregulated in primary cholangiocarcinoma compared with controls (n = 43) were investigated for their promoter methylation status in cancer cell lines from the gastrointestinal tract. Genes commonly methylated in cholangiocarcinoma cell lines were subjected to quantitative methylation-specific polymerase chain reaction in a total of 93 clinical samples (cholangiocarcinomas and non-malignant controls). CDO1, DCLK1, SFRP1 and ZSCAN18, displayed high methylation frequencies in primary tumors and were unmethylated in controls. At least one of these four biomarkers was positive in 87% of the tumor samples, with a specificity of 100%. In conclusion, the novel methylation-based biomarker panel showed high sensitivity and specificity for cholangiocarcinoma. The potential of these markers in early diagnosis of this cancer type should be further explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".