MicroRNAs Expression in Triple Negative vs Non Triple Negative Breast Cancer in Tunisia: Interaction with Clinical Outcome
Bibliographic record
Abstract
INTRODUCTION: MicroRNAs are small, non coding regulatory molecules containing approximately 21 to 25 nucleotides. They function as controllers of expression at post transcriptional levels of most human protein-coding genes and play an essential role in cell signaling pathways. The objective of the present study is to evaluate the expression profile of the following micro-RNAs: miR-10b, miR-17, miR-21, miR-34a, miR-146a, miR-148a and miR-182, and to determine their possible interaction in triple-negative and non triple-negative primary breast cancers based on clinical outcome. METHODS: 60 triple-negative and non triple-negative breast cancer cases, along with their corresponding normal samples were investigated in relation to the expression of the seven studied miRNAs using qPCR Syber Green. RESULTS: We observed that miR-21, miR-146a and miR-182 were significantly over expressed in triple negative breast cancer. Moreover, miR-10b, miR-21 and miR-182 were significantly associated to lymph node metastases occurrence in triple negative breast carcinoma while only miR-10b was associated with grade III in non triple negative breast cancer cases. Almost all the analyzed microRNAs were strongly associated with patients' genico-obstetric history in non triple negative breast cancer cases except for miR-34a. All the studied microRNAs were strongly correlated with the use of the contraceptive pills in non triple negative breast cancer groups. The additive effect of hormonal factors in triple negative breast cancer cases showed an association with all the studied miRs except for miR-34 and miR-146a. CONCLUSION: The studied microRNAs are strongly influenced by environmental factors especially with hormonal patients' history. Moreover, miR-10b, miR-21 and miR-182 could be defined as biomarkers in breast cancer to predict both lymph node metastases and grade III occurrence.
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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".