<p>LncRNA SRA1 may play a role in the uterine leiomyoma tumor growth regarding the <em>MED12</em> mutation pattern</p>
Bibliographic record
Abstract
Background: Uterine leiomyomas (ULMs) are benign uterine tumors that are estrogen-dependent. Recent studies suggest that the abnormal expression of the steroid receptor RNA activator 1 (SRA1) long non-coding RNA (lncRNA) might participate in the mechanisms of tumorigenesis of some hormone-dependent tumors including breast cancer. SRA1 is known to enhance the transcriptional activity of steroid receptors and also promotes steroidogenesis. The level of steroid hormones, such as estrogen and the progesterone, and their receptors play an important role in the development and growth of leiomyoma. The aim of the present study was to determine the expression level of lncRNA SRA1 in ULM tissues considering the MED12 mutation pattern. Methods: Mutation screening was performed for MED12 exons 1 and 2 and the intronic flanking regions using Sanger sequencing in 60 ULM tissues. Quantitative real-time polymerase chain reaction (qRT-PCRs) was performed in order to estimate the expression of lncRNA SRA1 in leiomyoma samples with and without MED12 gene mutations. The expression results were analyzed by using LinReg and REST software. Results: Mutations were detected in exon 2 of the MED12 in 28 (46.67%) ULM samples; including, 21 (75%) missense mutations and 7 (25%) in-frame deletions. No mutation was detected in the MED12 exon 1. LncRNA SRA1 was over-expressed in ULM samples without MED12 mutation compared with ULM samples harboring MED12 mutation (Expression ratio=2.5, P -value=0.004). Conclusion: Present results suggest that lncRNA SRA1 may explain the phenotypic difference observed in the tumor size of ULM samples considering MED12 mutation pattern. Therefore, it serves as a good therapeutic target and provides new insight into understanding the disease molecular mechanism. Keywords: MED12 , mutation, SRA1, lncRNA, uterine leiomyoma
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".