MIR-34C REGULATES THE PROLIFERATION AND APOPTOSIS OF LUNG CANCER CELLS
Bibliographic record
Abstract
PURPOSE: As miR-34c acts as a tumor suppressant for multiple cancers, the purpose of this study was to investigate that role that miR-34c plays in the proliferation and apoptosis of lung cancer. METHODS: The expression of miR-34c in 600 patients with lung cancer was quantitatively analyzed with real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) technology and correlated to clinical pathological parameters. The CCK-8 analysis and flow cytometry were carried out to detect cell proliferation and apoptosis in miR-34c-mimic transfected cell lines. Moreover, the regulation of miR-34c to interleukin-6 (IL-6) in cell lines was detected by western blot, qRT-PCR and dual-luciferase reporter assay. RESULTS: The expression of miR-34c was downregulated in lung cancer compared with adjacent normal tissues. The expression level of miR-34c was linked to stromal invasion. Furthermore, overexpressing miR-34c played an active role in effectively inhibiting cell proliferation and inducing apoptosis. In addition, a significant inverse relationship was exhibited between the expression of miR-34c and IL-6 in tumor tissues. CONCLUSION: At the molecular level, IL-6 can be used as a direct target of miR-34c in the treatment of lung cancer cells and miR-34c can be used as an effective biomarker and therapeutic target for lung cancer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".