Real-Time RT-PCR Quantification of Human Telomerase Reverse Transcriptase Splice Variants in Tumor Cell Lines and Non–Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: We developed and validated a real-time reverse transcription (RT)-PCR for the quantification of 4 individual human telomerase reverse transcriptase (TERT) splice variants (alpha+beta+, alpha-beta+, alpha+beta-, alpha-beta-) in tumor cell lines and non-small cell lung cancer (NSCLC). METHODS: We used in silico designed primers and a common TaqMan probe for highly specific amplification of each TERT splice variant, PCR transcript-specific DNA external standards as calibrators, and the MCF-7 cell line for the development and validation of the method. We then quantified TERT splice variants in 6 tumor cell lines and telomerase activity and TERT splice variant expression in cancerous and paired noncancerous tissue samples from 28 NSCLC patients. RESULTS: In most tumor cell lines, we observed little variation in the proportion of TERT splice variants. The alpha+beta- splice variant showed the highest expression and alpha-beta+ and alpha-beta- the lowest. Quantification of the 4 TERT splice variants in NSCLC and surrounding nonneoplastic tissues showed the highest expression percentage for the alpha+beta- variant in both NSCLC and adjacent nonneoplastic tissue samples, followed by alpha+beta+, with the alpha-beta+ and alpha-beta- splice variants having the lowest expression. In the NSCLC tumors, the alpha+beta+ variant had higher expression than other splice variants, and its expression correlated with telomerase activity, overall survival, and disease-free survival. CONCLUSIONS: Real-time RT-PCR quantification is a specific, sensitive, and rapid method that can elucidate the biological role of TERT splice variants in tumor development and progression. Our results suggest that the expression of the TERT alpha+beta+ splice variant may be an independent negative prognostic factor for NSCLC 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".