MicroRNA-145 regulates oncolytic herpes simplex virus-1 for selective killing of human non-small cell lung cancer cells
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide, and novel treatment modalities to improve the prognosis of patients with advanced disease are highly desirable. Oncolytic virotherapy is a promising approach for the treatment of advanced NSCLC. MicroRNAs (miRNAs) may be a factor in the regulation of tumor-specific viral replication. The purpose of this study was to investigate whether miRNA-145 regulated oncolytic herpes simplex virus-1 (HSV-1) can selectively kill NSCLC cells with reduced collateral damage to normal cells. METHODS: We incorporated 4 copies of miRNA-145 target sequences into the 3'-untranslated region of an HSV-1 essential viral gene, ICP27, to create AP27i145 amplicon viruses and tested their target specificity and toxicity on normal cells and lung cancer cells in vitro. RESULTS: miRNA-145 expression in normal cells was higher than that in NSCLC cells. AP27i145 replication was inversely correlated with the expression of miRNA-145 in infected cells. This oncolytic HSV-1 selectively reduced cell proliferation and prevented the colony formation of NSCLC cells. The combination of radiotherapy and AP27i145 infection was significantly more potent in killing cancer cells than each therapy alone. CONCLUSIONS: miRNA-145-regulated oncolytic HSV-1 is a promising agent for the treatment of NSCLC.
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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) | 0.001 | 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".