Base excision repair DNA genetic variant OGG1 Ser326Cys among Saudi population: a comparative approach with worldwide ethnic group variations
Bibliographic record
Abstract
BACKGROUND: DNA integrity is affected by both internal and external damage, but repair mechanisms usually restore it. Base excision repair (BER) is crucial for genome maintenance, with mutations in BER genes linked to higher cancer risk. The 8-oxoguanine DNA glycosylase-1 (OGG1) gene is vital in the BER pathway. Polymorphisms in DNA repair genes can reduce repair efficiency and elevate cancer risk. This study examined the frequency of the OGG1 exon 7 C>G (Ser326Cys) variant in the Saudi population and compared it globally.METHODS: The PubMed (Medline) internet database was examined for epidemiological studies on various ethnic groups. Allele and genotype frequencies of OGG1exon 7 C>G was determined. All statistical analysis was done using SPSS 21 software.RESULTS: The minor allele (G) frequency in Saudi population was 24%. It was higher in comparison with Iraq, Iran, Italy, Germany, USA, Pakistan and Belarus. Subsequently, Minor allele frequency G was found higher in Thailand (50%), Türkiye (33%), South Korea (52%), Japan (46%), India (27%), Algeria (29%), Canada (28%), China (36%), and Norway (27%) compare to Saudi population. Significant frequency distributions of variant genotype were observed for Thailand (P=0.041), and South Korea (P=0.012) compared with Saudi Arabian population.CONCLUSIONS: The findings of this study indicate that, in comparison to other ethnic populations, the frequency of the OGG1exon 7 C>G DNA repair gene shows a different pattern in the Saudi population, which may be related to ethnic variance. This might help with cancer propensity in various ethnic groups and high-risk screening of those exposed to environmental toxins.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".