The Reticulon-4 3-bp Deletion/Insertion Polymorphism Is Associated with Structural mRNA Changes and the Risk of Breast Cancer: A Population-Based Case–Control Study with Bioinformatics Analysis
Bibliographic record
Abstract
Breast cancer (BC) is a complex disease caused by molecular events that disrupt cellular survival and death. Discovering novel biomarkers is still required to better understand and treat BC. The reticulon-4 (RTN4) gene, encoding Nogo proteins, plays a critical role in apoptosis and cancer development, with genetic variations affecting its function. We investigated the rs34917480 in RTN4 and its association with BC risk in an Iranian population sample. We also predicted the rs34917480 effect on RTN4 mRNA structure and explored the RTN4’s protein–protein interaction network (PPIN) and related pathways. In this case–control study, 437 women (212 BC and 225 healthy) were recruited. The rs34917480 was genotyped using AS-PCR, mRNA secondary structure was predicted with RNAfold, and PPIN was constructed using the STRING database. Our findings revealed that this variant was associated with a decreased risk of BC in heterozygous (p = 0.012), dominant (p = 0.015), over-dominant (p = 0.017), and allelic (p = 0.035) models. Our prediction model showed that this variant could modify RTN4’s mRNA thermodynamics and potentially its translation. RTN4’s PPIN also revealed a strong association with apoptosis regulation and key signaling pathways highly implicated in BC. Consequently, our findings, for the first time, demonstrate that rs34917480 could be a protective factor against BC in our cohort, probably via preceding mechanisms.
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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".