The contribution of XRCC6/Ku70 to hepatocellular carcinoma in Taiwan.
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is a neoplasm for which the prevalence and mortality rates are very high in Taiwan. The DNA non-homologous end-joining repair gene XRCC6/Ku70 plays an important role in the repair of DNA double-strand breaks (DSBs) induced by both exogenous and endogenous DNA-damaging agents. Defects in overall DSB repair capacity can lead to genomic instability and carcinogenesis. In this study, we investigated the contribution of variant XRCC6 in relation to the risk of HCC, from the levels of DNA, RNA and protein. MATERIALS AND METHODS: In this hospital-based case-control study, we collected 298 patients with HCC and 298 cancer-free controls, with frequency matched by age and gender. Firstly, the associations of XRCC6 promoter T-991C (rs5751129), promoter G-57C (rs2267437), promoter A-31G (rs132770), and intron-3 (rs132774) polymorphisms with HCC risk in this Taiwanese population were evaluated. Secondly, 30 HCC tissue samples with variant genotypes were tested to estimate the XRCC6 mRNA expression by real-time quantitative reverse transcription. Finally, the HCC tissue samples of variant genotypes were examined by immunohistochemistry and western blotting to estimate their XRCC6 protein expression levels. RESULTS: Compared with the TT genotype, the TC and CC genotypes conferred a significantly increased risk of HCC [adjusted odds ratio (aOR)=2.43 and 3.52, 95% confidence interval (CI)=1.52-4.03 and 1.18-13.36, p=0.0003 and 0.0385, respectively]. The mRNA and protein expression levels in HCC tissues revealed statistically significantly lower XRCC6 mRNA and protein expressions in the HCC samples with TC/CC genotypes compared with those with the TT genotype (p=0.0037 and 0.0003, respectively). CONCLUSION: Our multi-approach findings at the DNA, RNA and protein levels suggested that XRCC6 may play an important role in HCC carcinogenesis in the Taiwanese population.
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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 | 0.001 |
| 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".