The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
23 May 2019
Historique:
received: 15 04 2019
revised: 09 05 2019
accepted: 20 05 2019
entrez: 26 5 2019
pubmed: 28 5 2019
medline: 15 11 2019
Statut: epublish

Résumé

Breast Cancer is one of the world's most notorious diseases affecting two million women in 2018 worldwide. It is a highly heterogeneous disease, making it difficult to treat. However, its linear progression makes it a candidate for early screening programs, and the earlier its detection the higher the chance of recovery. However, one key hurdle for breast cancer screening is the fact that most screening techniques are expensive, time-consuming, and cumbersome, making them impractical for use in several parts of the world. One current trend in breast cancer detection has pointed to a possible solution, the use of salivary breast cancer biomarkers. Saliva is an attractive medium for diagnosis because it is readily available in large quantities, easy to obtain at low cost, and contains all the biomarkers present in blood, albeit in lower quantities. Affinity sensors are devices that detect molecules through their interactions with biological recognition molecules. Their low cost, high sensitivity, and selectivity, as well as rapid detection time make them an attractive alternative to traditional means of detection. In this review article, we discuss the current status of breast cancer diagnosis, its salivary biomarkers, as well as the current trends in the development of affinity sensors for their detection.

Identifiants

pubmed: 31126047
pii: s19102373
doi: 10.3390/s19102373
pmc: PMC6566681
pii:
doi:

Substances chimiques

Autoantibodies 0
Biomarkers, Tumor 0
MicroRNAs 0
Mucins 0

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : UK-Lebanon Tech Hub
ID : Primispot
Organisme : Horizon 2020 Framework Programme
ID : 768686

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Auteurs

Imad Abrao Nemeir (I)

Faculty of Sciences, Holy Spirit University of Kaslik, 446 Jounieh, Mount Lebanon, Lebanon. imad.abraonemeir@isa-lyon.fr.
Institut des Sciences Analytiques, Université de Lyon, Claude Bernard Lyon 1, UMR 5280, CNRS - 5, rue de la Doua, 69100 Villeurbanne, France. imad.abraonemeir@isa-lyon.fr.

Joseph Saab (J)

Faculty of Sciences, Holy Spirit University of Kaslik, 446 Jounieh, Mount Lebanon, Lebanon. josephsaab@usek.edu.lb.

Walid Hleihel (W)

Faculty of Sciences, Holy Spirit University of Kaslik, 446 Jounieh, Mount Lebanon, Lebanon. walidhleihel@usek.edu.lb.

Abdelhamid Errachid (A)

Institut des Sciences Analytiques, Université de Lyon, Claude Bernard Lyon 1, UMR 5280, CNRS - 5, rue de la Doua, 69100 Villeurbanne, France. abdelhamid.errachid-el-salhi@univ-lyon1.fr.

Nicole Jafferzic-Renault (N)

Institut des Sciences Analytiques, Université de Lyon, Claude Bernard Lyon 1, UMR 5280, CNRS - 5, rue de la Doua, 69100 Villeurbanne, France. nicole.jaffrezic@univ-lyon1.fr.

Nadia Zine (N)

Institut des Sciences Analytiques, Université de Lyon, Claude Bernard Lyon 1, UMR 5280, CNRS - 5, rue de la Doua, 69100 Villeurbanne, France. imad-nmeir@hotmail.com.

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Classifications MeSH