MMP1 and MMP3 genes promoter nucleotide polymorphism in Cervical cancer in Ouagadougou, Burkina Faso
Bibliographic record
Abstract
Background: Matrix metalloproteinases (MMPs) are enzymes involved in several normal physiological but also pathological processes. Genetic variations of several MMP promoters can influence the transcription and expression of MMPs such as MMP-1 (1607) and MMP-3 (1171) by creating ETS (Erythroblast Transformation Specific) binding sites. Correlations between these SNPs and several biological processes, such as metastases, recurrence of cancers, have been reported in several studies. This study aimed to evaluate the gap in transcription level of polymorphisms of each of the MMP1 and MMP3 genes, in cases of cervical cancer in Burkina Faso. In 92 women with histologically confirmed cervical cancer (CC), we genotyped MMP1 and MMP3 using standard PCR, followed by visualization on agarose gel, and then quantified the transcripts of these two genes via quantitative PCR. Results: We found a high frequency of 2G and 5A alleles in patients with CC. Analysis of the haplotypes indicated a strong associated presence of these two alleles, thus suggesting a synergistic action of the MMP1 and MMP3 polymorphisms to CC. Additionally, we found a high level of transcription linked to the presence of the 2G and 5A alleles in these cancers, suggesting a strong enzymatic activity of MMP1 and MMP3 in CC. Conclusion: The homozygous 2G2G and 5A5A genotypes of the MMP1 and MMP3 genes were the most frequent in the study population. The 2G and 5A alleles were most commonly found in cancerous lesions with a high level of transcription.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".