SynthesisReproductive biology and endocrinology : RB&E2023
Liposomal delivery of gene therapy for ovarian cancer: a systematic review.
Synthesis in Reproductive biology and endocrinology : RB&E, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Targeting lncRNAs to Overcome Cancer Therapy Resistance: Advances in RNA Therapeutics and Delivery Strategies.Cancers · 2026Review
- Nanoparticle based siRNA therapeutics for ovarian cancer overcoming drug resistance and future directions.Discover nano · 2026Review
- Potential of CXCR1/2 as a target for the treatment of inflammation and cancer (Review).Experimental and therapeutic medicine · 2025Review
- Advances in Liposomal Interleukin and Liposomal Interleukin Gene Therapy for Cancer: A Comprehensive Review of Preclinical Studies.Pharmaceutics · 2025Review
- Liposomal Formulations: A Recent Update.Pharmaceutics · 2024Review
- A Zeolitic Imidazolate Framework-Based Antimicrobial Peptide Delivery System with Enhanced Anticancer Activity and Low Systemic Toxicity.Pharmaceutics · 2024Article
- Brief Comparison of the Efficacy of Cationic and Anionic Liposomes as Nonviral Delivery Systems.ACS omega · 2024Review
- Targeted Nanocarrier-Based Drug Delivery Strategies for Improving the Therapeutic Efficacy of PARP Inhibitors against Ovarian Cancer.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo systematically identify and narratively synthesize the evidence surrounding liposomal delivery of gene therapy and the outcome for ovarian cancer.
methodsAn electronic database search of the Embase, MEDLINE and Web of Science from inception until July 7, 2023, was conducted to identify primary studies that investigated the effect of liposomal delivery of gene therapy on ovarian cancer outcomes. Retrieved studies were assessed against the eligibility criteria for inclusion.
resultsThe search yielded 564 studies, of which 75 met the inclusion criteria. Four major types of liposomes were identified: cationic, neutral, polymer-coated, and ligand-targeted liposomes. The liposome with the most evidence involved cationic liposomes which are characterized by their positively charged phospholipids (n = 37, 49.3%). Similarly, those with neutrally charged phospholipids, such as 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, were highly researched as well (n = 25, 33.3%). Eight areas of gene therapy research were identified, evaluating either target proteins/transcripts or molecular pathways: microRNAs, ephrin type-A receptor 2 (EphA2), interleukins, mitogen-activated protein kinase (MAPK), human-telomerase reverse transcriptase/E1A (hTERT/EA1), suicide gene, p53, and multidrug resistance mutation 1 (MDR1).
conclusionLiposomal delivery of gene therapy for ovarian cancer shows promise in many in vivo studies. Emerging polymer-coated and ligand-targeted liposomes have been gaining interest as they have been shown to have more stability and specificity. We found that gene therapy involving microRNAs was the most frequently studied. Overall, liposomal genetic therapy has been shown to reduce tumor size and weight and improve survivability. More research involving the delivery and targets of gene therapy for ovarian cancer may be a promising avenue to improve patient outcomes.
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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.