Helicase-like transcription factor expression is associated with a poor prognosis in Non-Small-Cell Lung Cancer (NSCLC)
Bibliographic record
Abstract
BACKGROUND: The relapse rate in early stage non-small cell lung cancer (NSCLC) after surgical resection is high. Prognostic biomarkers may help identify patients who may benefit from additional therapy. The Helicase-like Transcription Factor (HLTF) is a tumor suppressor, altered in cancer either by gene hypermethylation or mRNA alternative splicing. This study assessed the expression and the clinical relevance of wild-type (WT) and variant forms of HLTF RNAs in NSCLC. METHODS: We analyzed online databases (TCGA, COSMIC) for HLTF alterations in NSCLC and assessed WT and spliced HLTF mRNAs expression by RT-ddPCR in 39 lung cancer cell lines and 171 patients with resected stage I-II NSCLC. RESULTS: In silico analyses identified HLTF gene alterations more frequently in lung squamous cell carcinoma than in adenocarcinoma. In cell lines and in patients, WT and I21R HLTF mRNAs were detected, but the latter at lower level. The subgroup of 25 patients presenting a combined low WT HLTF expression and a high I21R HLTF expression had a significantly worse disease-free survival than the other 146 patients in univariate (HR 1.96, CI 1.17-3.30; p = 0.011) and multivariate analyses (HR 1.98, CI 1.15-3.40; p = 0.014). CONCLUSION: A low WT HLTF expression with a high I21R HLTF expression is associated with a poor DFS.
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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 | 0.000 | 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".