MiR-153 reduces stem cell-like phenotype and tumor growth of lung adenocarcinoma by targeting Jagged1
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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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) | 0.001 | 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".