Association between Genetic Polymorphisms of miR-1307, miR- 1269, miR-3117 and Breast Cancer Risk in a Sample of South East Iranian Women
Bibliographic record
Abstract
INTRODUCTION: MicroRNAs (miRNAs) play an essential role in the susceptibility and development of cancer cells. OBJECTIVE: Examining the dependency of breast cancer risk with genetic polymorphisms of miR-1307, miR-1269, and miR-3117 in a sample of Iranian women (southeast region). METHODS: The case-control study consisted of 520 individuals (260 diagnosed BC patients, 260 healthy individuals). The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used for genotyping of miR-1307 rs7911488, miR-1269 rs73239138, and miR-3117 (rs4655646 and rs7512692) polymorphisms. RESULTS AND CONCLUSION: This study provided evidence that miR-1307 rs7911488 polymorphism significantly reduced the risk of BC in heterozygous AG genotype, as well as dominant (AG+GG) genotype and G allele. A significant correlation was found between dominant (AA+AG) genotype, the A allele and protection against BC due to miR-1269 rs73239138 in the sample of study. In contrast, our findings suggested that AG genotype and G allele of miR-3117 rs4655646 polymorphism could increase BC's susceptibility among the southeastern Iranian females. The miR-3117 rs7512692 variant also increased the risk of BC in codominant, dominant and recessive models, as well as the T allele. The possible dependency of miR-1307, miR-1269, and miR-3117 variants with patients' clinicopathological characteristics and BC was also studied. It was concluded that there is a correlation between miR-3117 rs7512692 variant and tumor grade (p=0.031); also, a correlation between miR-1269 rs73239138 variant and progesterone receptor status (p=0.006). The current investigation revealed that miR-1307, miR-1269, and miR-3117 polymorphisms might play a crucial role in the Iranian population's vulnerability to BC.<br />.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".