Novel sequence variants in the TLR6 gene associated with advanced breast cancer risk in the Saudi Arabian population
Bibliographic record
Abstract
Herein, we evaluated the association of the Toll-like receptor 6 (TLR6) single nucleotide polymorphisms (SNPs) rs3796508 (Val327Met) and rs5743810 (Ser249Pro) with breast cancer (BC) susceptibility in Saudi Arabian women, using in silico analysis. We found no significant differences in genotypic and allelic frequencies for rs3796508 between the BC patients (n = 127) and healthy individuals (n = 116). However, 86% of the BC patients, versus 98% of the healthy controls, carried the rs5743810 Pro allele (OR = 0.103, CI = 0.036-0.293, P = 0.00001). Advanced analysis based on the comparison of the estrogen receptor (ER)-positive and -negative patients with the healthy controls indicated a significant association between rs5743810 allelic frequency and BC risk protection (OR = 0.100, CI = 0.034-0.297, P = 0.00001 for ER+ BC cases; OR = 0.102, CI = 0.033-0.318, P = 0.00001 for ER-BC cases). Furthermore, rs5743810 was associated with BC risk protection at either above or below 48 years of age at diagnosis (OR = 0.101, CI = 0.022-0.455, P = 0.00037 for age ≤48 years; OR = 0.120, CI = 0.028-0.519, P = 0.00087 for age >48 years). Such associations were not found for rs3796508. In silico analysis indicated that these SNPs had neutral effects within the TLR6 structure, confirming the protective role of rs5743810. Our findings therefore suggest a strong association between rs5743810 and protection against BC risk in Saudi Arabian women. Importantly, the rs5743810 Pro allele could be a potential BC diagnostic biomarker in this ethnic 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 | 0.000 | 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) | 0.002 | 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".