Novel strategy of liver cancer treatment with modified antisense oligonucleotides targeting human vasohibin‐2
Bibliographic record
Abstract
Vasohihibin-2 (VASH2) is a homolog of vasohibin-1 (VASH1) and is overexpressed in various cancers. Vasohihibin-2 acts on both cancer cells and cancer microenvironmental cells. Previous analyses have shown that VASH2 promotes cancer progression and abrogation of VASH2 results in significant anticancer effects. We therefore propose VASH2 to be a practical molecular target for cancer treatment. Modifications of antisense oligonucleotide (ASO) such as bridged nucleic acids (BNA)-based modification increases the specificity and stability of ASO, and are now applied to the development of a number of oligonucleotide-based drugs. Here we designed human VASH2-ASOs, selected an optimal one, and developed 2',4'-BNA-based VASH2-ASO. When systemically administered, naked 2',4'-BNA-based VASH2-ASO accumulated in the liver and showed its gene-silencing activity. We then examined the effect of 2',4'-BNA-based VASH2-ASO in liver cancers. Intraperitoneal injection of naked 2',4'-BNA-based VASH2-ASO exerted a potent antitumor effect on orthotopically inoculated human hepatocellular carcinoma cells. The same manipulation also showed potent antitumor activity on the splenic inoculation of human colon cancer cells for liver metastasis. These results provide a novel strategy for the treatment of primary as well as metastatic liver cancers by using modified ASOs targeting VASH2.
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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".