Uncovering Growth-Suppressive MicroRNAs in Lung Cancer
Bibliographic record
Abstract
PURPOSE: MicroRNA (miRNA) expression profiles improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. This study uncovered unique growth-suppressive miRNAs in lung cancer. EXPERIMENTAL DESIGN: miRNA arrays were done on normal lung tissues and adenocarcinomas from wild-type and proteasome degradation-resistant cyclin E transgenic mice to reveal repressed miRNAs in lung cancer. Real-time and semiquantitative reverse transcription-PCR as well as in situ hybridization assays validated these findings. Lung cancer cell lines were derived from each transgenic line (designated as ED-1 and ED-2 cells, respectively). Each highlighted miRNA was independently transfected into these cells. Growth-suppressive mechanisms were explored. Expression of a computationally predicted miRNA target was examined. These miRNAs were studied in a paired normal-malignant human lung tissue bank. RESULTS: miR-34c, miR-145, and miR-142-5p were repressed in transgenic lung cancers. Findings were confirmed by real-time and semiquantitative reverse transcription-PCR as well as in situ hybridization assays. Similar miRNA profiles occurred in human normal versus malignant lung tissues. Individual overexpression of miR-34c, miR-145, and miR-142-5p in ED-1 and ED-2 cells markedly repressed cell growth. Anti-miR cotransfections antagonized this inhibition. The miR-34c target, cyclin E, was repressed by miR-34c transfection and provided a mechanism for observed growth suppression. CONCLUSIONS: miR-34c, miR-145, and miR-142-5p were repressed in murine and human lung cancers. Transfection of each miRNA significantly repressed lung cancer cell growth. Thus, these miRNAs were growth suppressive and are proposed to exert antineoplastic effects in the lung.
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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 | 0.000 |
| 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 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".