Evidence map›Paper›PMID 28635657›Full record

ReviewCancers2017

Selection of Nucleic Acid Aptamers Targeting Tumor Cell-Surface Protein Biomarkers.

Marie-Cécile Mercier, Monique Dontenwill, Laurence Choulier

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 107 citations in OpenAlex.

  1. Aptamer targeting proprotein convertase subtilisin/kexin type 9:Research in pharmaceutical sciences · 2026
    Article
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  13. Smart nanoparticles for cancer therapy.Signal transduction and targeted therapy · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Marie-Cécile MercierUMR 7213 CNRS, Laboratoire de Biophotonique et Pharmacologie, Tumoral Signaling and Therapeutic Targets, Université de Strasbourg, Faculté de Pharmacie, 67401 Illkirch, France. marie-cecile.mercier@unistra.fr.
Monique DontenwillUMR 7213 CNRS, Laboratoire de Biophotonique et Pharmacologie, Tumoral Signaling and Therapeutic Targets, Université de Strasbourg, Faculté de Pharmacie, 67401 Illkirch, France. monique.dontenwill@unistra.fr.
Laurence ChoulierUMR 7213 CNRS, Laboratoire de Biophotonique et Pharmacologie, Tumoral Signaling and Therapeutic Targets, Université de Strasbourg, Faculté de Pharmacie, 67401 Illkirch, France. laurence.choulier@unistra.fr.
Laboratoire de Biophotonique et Pharmacologie · FRUniversité de Strasbourg · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aptamers are nucleic acids referred to as chemical antibodies as they bind to their specific targets with high affinity and selectivity. They are selected via an iterative process known as 'selective evolution of ligands by exponential enrichment' (SELEX). Aptamers have been developed against numerous cancer targets and among them, many tumor cell-membrane protein biomarkers. The identification of aptamers targeting cell-surface proteins has mainly been performed by two different strategies: protein- and cell-based SELEX, when the targets used for selection were proteins and cells, respectively. This review aims to update the literature on aptamers targeting tumor cell surface protein biomarkers, highlighting potentials, pitfalls of protein- and cell-based selection processes and applications of such selected molecules. Aptamers as promising agents for diagnosis and therapeutic approaches in oncology are documented, as well as aptamers in clinical development.

Indexed as

aptamercell-surface biomarkerSELEX

Identifiers

PMID28635657
PMCPMC5483888
OpenAlexW2721117256

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.