ReviewDrug design, development and therapy2025
Lipid Nanovesicles in Cancer Treatment: Improving Targeting and Stability of Antisense Oligonucleotides.
Review in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- The role of splicing factor SRSF3 in cancer progression: Mechanisms, biomarkers, and therapeutic implications.Translational oncology · 2026Review
- Targeting molecular pathways in neuropathic cancer pain: from mechanisms to novel therapeutics.iScience · 2026Review
- Exploring the oncogenic mechanism of plasmacytoma variant translocation 1 (PVT1) gene in solid cancers; emphasis on microRNA regulation pathways.Cancer cell international · 2026Review
- Targeting TLK2 with antisense oligonucleotides as a new strategy in acute myeloid leukemia.Frontiers in oncology · 2026Article
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
- Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.Molecular medicine (Cambridge, Mass.) · 2025Article
- Gene network changes after exposure to nanoliposomes containing antisense miR-21 and miR-373 in bladder cancer Cells: AnBiochemistry and biophysics reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer remains a leading cause of mortality worldwide, accounting for approximately 10 million deaths annually. Standard treatments, including surgery, radiotherapy, and chemotherapy, often result in damage to healthy cells and severe toxic side effects. In recent years, antisense technology therapeutics, which interfere with RNA translation through complementary base pairing, have emerged as promising approaches for cancer treatment. Despite the availability of various antisense oligonucleotide (ASO) drugs on the market, challenges such as poor active targeting and susceptibility to clearance by circulating enzymes remain. Compared with other delivery systems, lipid nanovesicle (LNV) delivery systems offer a potential solution that uniquely enhances ASO targeting and stability. Studies have shown that LNVs can increase the accumulation of ASOs in tumor sites several-fold, significantly reducing systemic toxic reactions and demonstrating increased therapeutic efficiency in preclinical models. Additionally, LNVs can protect ASOs from enzymatic degradation within the body, extending their half-life and thus enhancing their therapeutic effects. This paper provides a comprehensive review of recent examples and applications of LNV delivery of ASOs in cancer treatment, highlighting their unique functions and outcomes. Furthermore, this paper discusses the key challenges and potential impacts of this innovative approach to cancer therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.