MiR-153 reduces stem cell-like phenotype and tumor growth of lung adenocarcinoma by targeting Jagged1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cancer stem cells (CSCs) have been proposed to be responsible for tumor recurrence and chemo-resistance. Previous studies suggested that miR-153 played essential roles in lung cancer. However, the molecular mechanism of miR-153 in regulating the stemness of non-small cell lung cancer (NSCLC) remains poorly understood. In this study, we investigated the role of miR-153 in regulation of the stemness of NSCLC. METHODS: The stemness property of lung stem cancer cells was detected by sphere formation assay, immunofluorescence, and Western blot. Luciferase reporter assay was performed to investigate the direct binding of miR-153 to the 3'-UTR of JAG1 mRNA. Animal study was conducted to evaluate the effect of miR-153 on tumor growth in vivo. The clinical relevance of miR-153 in NSCLC was evaluated by Rt-PCR and Kaplan-Meier analysis. RESULTS: MiR-153 expression was decreased in lung cancer tissues. Reduced miR-153 expression was associated with lung metastasis and poor overall survival of lung cancer patients. Jagged1, one of the ligands of Notch1, is targeted by miR-153 and inversely correlates with miR-153 in human lung samples. More importantly, we found that miR-153 inhibited stem cell-like phenotype and tumor growth of lung adenocarcinoma through inactivating the Jagged1/Notch1 axis. CONCLUSION: MiR-153 suppresses the stem cell-like phenotypes and tumor growth of lung adenocarcinoma by targeting Jagged1 and provides a potential therapeutic target in lung cancer therapy.
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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 | 0.001 | 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 it