Association between <em>TLR-9</em> polymorphisms and colon cancer susceptibility in Saudi Arabian female patients
Bibliographic record
Abstract
Objective: The authors aimed to explore the relationship between the expression/polymorphisms of TLR-9 and susceptibility to colon cancer development in the Saudi Arabian population. Methods: In total, blood samples from 115 patients with colon cancer and 102 participants without colon cancer were analyzed in this study. Three single-nucleotide polymorphisms (SNPs) were selected from the TLR-9 gene, including two sites within the TLR-9 gene’s promoter region (rs352144 and rs187084) and one site in a TLR-9 intron region (rs5743839). Odds ratios (ORs) and 95% confidence intervals (CIs) were computed from logistic regression models after adjusting for age, gender, and tumor localization. To investigate the differential expression of TLR-9 in colon cancer, TLR-9 expression was evaluated using quantitative real-time reverse transcription polymerase chain reaction on 40 matched normal and colon tissues. Results: The authors found that TLR-9 expression was decreased in colon cancer tissues as compared with that in normal tissues. Moreover, significant associations between the TLR-9 rs187084 SNP and colon cancer risk were observed in female patients only. In rs187084, the T allele had a significantly lower frequency (2.8 times) in female cancer patients than in controls (0.27 vs 0.41). The TLR-9 rs352139 and rs352144 SNPs were significantly associated with colon cancer development when the tumor was located in the rectal area. Conclusion: The findings support the hypothesis that TLR-9 has an anticancer role in colon cancer development. Furthermore, genetic variation may influence colon cancer development, and SNPs in TLR-9 could serve as biomarkers for decision making in the treatment of females with rectal cancer. Keywords: Innate, immunity, TLR polymorphisms, rs187084, rs352139, rs352144
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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 | 0.000 |
| 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".