Toll-like receptor-4 as a predictor of clinical outcomes of estrogen receptor-negative breast cancer in Saudi women
Bibliographic record
Abstract
Abstract: The aim of this study was to investigate the association of the common polymorphisms of Toll-like receptor 4 (TLR-4) with breast cancer development in the Saudi Arabian population. Four TLR-4 polymorphisms (rs2770150, rs10759931, rs10759932, and rs4986790) were studied using 127 breast cancer patients and 117 controls. Relative expression of TLR-4 protein in the breast tumor and the matched normal breast tissues was determined in a large cohort of 70 clinical breast samples in a tissue micro-array format by immunohistochemistry using a specific anti-TLR-4 antibody. Our results demonstrated an increase in TLR-4 expression in estrogen receptor (ER)-, postmenopausal breast cancer patients compared to normal. We also demonstrated that the G allele of single-nucleotide polymorphism rs10759931 was found to be significantly higher in frequency among patients (36.3%) compared to the control group (26.7%), suggesting that this polymorphism is strongly associated with the development of breast cancer in this ethnic population. In addition, the TLR-4 polymorphism rs2770150 was shown to be highly correlated with breast cancer in patients over 48 years of age. The TLR-4 polymorphism rs4986790 was also found to be associated with this malignancy in the ER- patient groups. Our results suggested firstly that the variation in TLR-4 gene expression may influence breast cancer development and secondly a closely linked association between TLR-4 gene polymorphism and ER status. Our study provides support for a better understanding of the implication of TLR-4 polymorphism in breast tumorigenesis and for its eventual use as a cancer biomarker. Keywords: Toll-like receptor 4, single-nucleotide polymorphisms, breast cancer, Saudi population, innate immunity, estrogen receptor
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".