Quantification of plasma hTERT DNA in hepatocellular carcinoma patients by quantitative fluorescent polymerase chain reaction
Bibliographic record
Abstract
PURPOSE: To investigate the levels of human telomerase reverse transcriptase (hTERT) DNA in the plasma of patients with hepatocellular carcinoma (HCC), and to evaluate the diagnostic value and correlation of hTERT DNA with clinical parameters in HCC. METHODS: A real-time quantitative fluorescent polymerase chain reaction (FQ-PCR) system was designed and evaluated. Plasma samples were collected from 60 HCC patients, 21 patients with hepatitis B virus (HBV) and 29 healthy controls. Plasma DNA was extracted and quantified by FQ-PCR. The diagnostic value of plasma hTERT DNA levels and their relationships with clinical characteristics were analyzed statistically. RESULTS: Plasma levels of hTERT DNA in HCC patients were significantly higher than in HBV patients (4.18×104±4.94×104 copies/μl vs 1.21×104±6.63×103 copies/μl, P=0.003) and healthy controls (4.18×104±4.94×104 copies/μl vs 1.44×104±6.61×103 copies/μl, P < 0.001). Receiver operating characteristic curve analysis indicated a sensitivity of 64% and a specificity of 90% for the ability of hTERT DNA levels to detect malignancy at a cutoff value of 1.87×104 copies/μl. Association analysis revealed that plasma hTERT DNA levels were closely related to tumor size, portal vein cancer embolus and TNM stage (P=0.013, P=0.010, and P=0.029, respectively), but were not associated with lymph node metastasis, hepatitis B surface antigen, or α-fetoprotein (AFP) (all P > 0.05). The levels of plasma hTERT DNA in HCC patients with AFP ≤20 ng/ml were significantly higher than in HBV patients (4.59×104±4.98×104 copies/μl vs 1.44×104±6.63×103 copies/μl, P=0.016) and in healthy controls (4.59×104±4.98×104 copies/μl vs 1.21×104±6.63×103 copies/μl, P=0.001). CONCLUSIONS: Quantitation of plasma hTERT DNA by FQ-PCR may provide a novel complementary tool with potential clinical applications for the screening and detection of HCC. Plasma hTERT DNA has the potential to be a broad tumor marker for common cancers.
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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 | 0.001 | 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".