miR-1291 targets mucin 1 inhibiting cell proliferation and invasion to promote cell apoptosis in esophageal squamous cell carcinoma
Bibliographic record
Abstract
MicroRNAs (miRNAs) are well known as important regulators in various cancer development. In the present study, we focused on the expression and biological function of miR-1291 in esophageal squamous cell carcinoma (ESCC). Compared with adjacent non-tumorous tissue samples, qRT-PCR data showed significant downregulation of miR-1291 in 54 ESCC tissue samples (P<0.05), which was also significantly associated with lymph node metastases and clinical stage (P<0.05). Cell Counting Kit-8 (CCK-8), colony formation, Transwell and flow cytometric apoptosis assays were performed to detect the effect of miR-1291 upregulation, and the results showed inhibition of the proliferation, invasion and promotion of apoptosis in EC9706 and EC-1 cells. Using bioinformatic analyses, we found that mucin 1 (MUC1) was a potential target for miR-1291. Luciferase assays were performed to reveal that miR-1291 inhibited MUC1 expression by targeting the seed region of MUC1 3'-untranslated region (3'UTR). We also found that the expression of MUC1 lacking in 3'UTR abrogated the anti-invasion and pro-apoptosis function of miR-1291. Our results demonstrated the importance of miR-1291 in targeting MUC1 for the regulation of esophagus cancer growth, invasion and apoptosis, and may be helpful for developing new targets for early diagnosis or new therapeutic targets for ESCC.
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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 | 0.000 |
| 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 | 0.001 |
| 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".