ReviewCancers2020
Targeting Glycans and Heavily Glycosylated Proteins for Tumor Imaging.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 18 citations in OpenAlex.
- Glypican-3 targeted radiopharmaceuticals for hepatocellular carcinoma: a review of molecular platforms.EJNMMI research · 2026Review
- The Dual Role of Natural Peptides in Cancer Therapy: Anticancer and Immunomodulatory Perspectives.Oncology research · 2026Review
- Advances and prospects of precision nanomedicine in personalized tumor theranostics.Frontiers in cell and developmental biology · 2024Review
- A tumor-associated heparan sulfate-related glycosaminoglycan promotes the generation of functional regulatory T cells.Cellular & molecular immunology · 2023Article
- The Role of Clinical Glyco(proteo)mics in Precision Medicine.Molecular & cellular proteomics : MCP · 2023Article
- SerumBiomolecules · 2023Article
- Bladder Cancer Cells Interaction with Lectin-Coated Surfaces under Static and Flow Conditions.International journal of molecular sciences · 2023Article
- Specific (sialyl-)Lewis core 2Theranostics · 2022Article
- Review
- Article
- Targeting the "Sweet Side" of Tumor with Glycan-Binding Molecules Conjugated-Nanoparticles: Implications in Cancer Therapy and Diagnosis.Nanomaterials (Basel, Switzerland) · 2021Review
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 at 2 institutions in 2 countries.
Funding
Abstract
Real-time tumor imaging techniques are increasingly used in oncological surgery, but still need to be supplemented with novel targeted tracers, providing specific tumor tissue detection based on intra-tumoral processes or protein expression. To maximize tumor/non-tumor contrast, targets should be highly and homogenously expressed on tumor tissue only, preferably from the earliest developmental stage onward. Unfortunately, most evaluated tumor-associated proteins appear not to meet all of these criteria. Thus, the quest for ideal targets continues. Aberrant glycosylation of proteins and lipids is a fundamental hallmark of almost all cancer types and contributes to tumor progression. Additionally, overexpression of glycoproteins that carry aberrant glycans, such as mucins and proteoglycans, is observed. Selected tumor-associated glyco-antigens are abundantly expressed and could, thus, be ideal candidates for targeted tumor imaging. Nevertheless, glycan-based tumor imaging is still in its infancy. In this review, we highlight the potential of glycans, and heavily glycosylated proteoglycans and mucins as targets for multimodal tumor imaging by discussing the preclinical and clinical accomplishments within this field. Additionally, we describe the major advantages and limitations of targeting glycans compared to cancer-associated proteins. Lastly, by providing a brief overview of the most attractive tumor-associated glycans and glycosylated proteins in association with their respective tumor types, we set out the way for implementing glycan-based imaging in a clinical practice.
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What OpenQuestion 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 OpenQuestion graph.