Restoration of fragile histidine triad expression restores Chk2 activity in response to ionizing radiation in oral squamous cell carcinoma cells
Bibliographic record
Abstract
Early in tumorigenesis, a DNA damage-response network is activated in preneoplastic cells that delays or prevents cancer. Activation of the Chk2 G(2)/M checkpoint kinase and loss of fragile histidine triad (Fhit) tumor suppressor expression increase cellular susceptibility to DNA-damaging 'oncogenic' stressors, particularly in precursor or precancerous lesions. To understand the mechanism of oral carcinogenesis, we assessed the association between phosphorylated Chk2 (pChk2) and Fhit expression in oral squamous cell carcinoma. Loss of Fhit expression was an early event during oral carcinogenesis, whereas a decrease in the number of pChk2-positive cells was associated with tumor progression. Although tyrosine 114 is known to be essential to Fhit's tumor-suppressing activity, both wild-type and tyrosine 114 mutant Fhit increased the population of subG(1) DNA-containing HSC-3 OSCC cells with elevated pChk2 levels. In particular, when cells were exposed to ionizing radiation, pChk2 levels were upregulated dramatically, as were those of its downstream target Cdc25C. Knockdown of Fhit with FHIT small interfering RNA diminished the ionizing radiation-induced Chk2 phosphorylation in HEK293 cells. Furthermore, Fhit-deficient mice demonstrated a decrease in the number of pChk2-positive cells not only in dysplastic lesions but also in N-nitrosobenzylamine-induced papilloma of the forestomach, suggesting that lack of Fhit expression and the resultant defects of the ataxia telangiectasia mutated-Chk2 pathway can cause a difference in the incidence of N-nitrosobenzylamine-induced forestomach lesions. These findings suggest that Fhit plays a key role in the regulation of the ataxia telangiectasia mutated-Chk2 DNA damage response during oral carcinogenesis.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".