Potential role of Toll-like receptor 2 expression and polymorphisms in colon cancer susceptibility in the Saudi Arabian population
Bibliographic record
Abstract
Background: Inflammation is a fundamental factor that contributes to the development and progression of several types of cancer including colon cancer. Toll-like receptors ( TLRs ) and their signaling pathways have been reported to be associated with chronic inflammation and thereby induced cancer. Our aim was to investigate the expression and polymorphisms of TLR2 and their association with colon cancer. Methods: Real-time PCR and immunohistochemistry were used to investigate TLR2 gene expression and to evaluate the potential risk of predisposition to colon cancer caused by three tagging single-nucleotide polymorphisms (SNPs) on TLR2 , including rs3804100, rs4696480, and rs3804099. TaqMan assay was conducted on samples from 115 patients with colon cancer and 102 age- and sex-matched normal individuals. Results: We found that, TLR2 was highly expressed in epithelial colon cancer cells and both TLR2 mRNA and protein levels, and significantly decreased in tumor tissues compared to normal tissues. Two of three TLR2 SNPs increased the risk of colon cancer. However, TLR2 rs3804099 increased the risk of colon cancer development by more than 3.8- and 5-fold in female patients and patients aged less than 57 years, respectively. The T allele of TLR2 rs3804100 showed a significant association with patients less than 57 years. In silico analysis of the TLR2 nucleotide substitution in SNP rs3804100 and rs3804099 determined that 67% and 70% probability of these single nucleotide variants alter splicing phenotypes, rs3804100 more specifically result on activating an additional splice site. Genotype and allele frequencies of rs4696480 were similar between the overall study populations. Thus, TLR2 rs4696480 appear to be not involved in colon cancer in our study population. Conclusions: There was a significant link between innate immunity deregulation through disruption of the TLRs and potential development of colon cancer. These SNPs can be used as screening markers for predicting colon cancer risk earlier in life to implement necessary prevention. Keywords: colon cancer, gene expression, genotyping, polymorphism, Toll-like receptors, innate immunity
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".