ReviewCancers2021
Modeling the Tumor Microenvironment of Ovarian Cancer: The Application of Self-Assembling Biomaterials.
Review in Cancers, 2021. 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
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
8 citing papers in PubMed.
- Extracellular matrix polysaccharides and glycoproteins in ovarian cancer: structural-functional macromolecular perspectives and potential exosome-derived biomarkers.Cell communication and signaling : CCS · 2026Review
- Premature ovarian insufficiency restoration after chemotherapy: current achievements and future prospects on its treatment or management.Journal of ovarian research · 2025Review
- Perfusion-based ex vivo culture of frozen ovarian cancer tissues with preserved tumor microenvironment.NPJ precision oncology · 2025Article
- The C/EBPβ antagonist peptide lucicebtide (ST101) induces macrophage polarization toward a pro-inflammatory phenotype and enhances anti-tumor immune responses.Frontiers in immunology · 2025Article
- Multicompartmentalized Microvascularized Tumor-on-a-Chip to Study Tumor-Stroma Interactions and Drug Resistance in Ovarian Cancer.Cellular and molecular bioengineering · 2024Article
- A Self-Assembled 3D Model Demonstrates How Stiffness Educates Tumor Cell Phenotypes and Therapy Resistance in Pancreatic Cancer.Advanced healthcare materials · 2024Article
- The Multifaceted Role of Connexins in Tumor Microenvironment Initiation and Maintenance.Biology · 2023Review
- Polymeric Hydrogels for In Vitro 3D Ovarian Cancer Modeling.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Ovarian cancer (OvCa) is one of the leading causes of gynecologic malignancies. Despite treatment with surgery and chemotherapy, OvCa disseminates and recurs frequently, reducing the survival rate for patients. There is an urgent need to develop more effective treatment options for women diagnosed with OvCa. The tumor microenvironment (TME) is a key driver of disease progression, metastasis and resistance to treatment. For this reason, 3D models have been designed to represent this specific niche and allow more realistic cell behaviors compared to conventional 2D approaches. In particular, self-assembling peptides represent a promising biomaterial platform to study tumor biology. They form nanofiber networks that resemble the architecture of the extracellular matrix and can be designed to display mechanical properties and biochemical motifs representative of the TME. In this review, we highlight the properties and benefits of emerging 3D platforms used to model the ovarian TME. We also outline the challenges associated with using these 3D systems and provide suggestions for future studies and developments. We conclude that our understanding of OvCa and advances in materials science will progress the engineering of novel 3D approaches, which will enable the development of more effective therapies.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.