Literature DB >> 32415889

Biomarkers in melanoma and non-melanoma skin cancer prevention and risk stratification.

Megan H Trager1, Larisa J Geskin1, Faramarz H Samie1, Liang Liu2.   

Abstract

The rates of melanoma and non-melanoma skin cancer (NMSC) have been increasing over the last twenty years in the United States, and this has been attributed to increased ultraviolet radiation exposure (UVR). Given these rising rates, preventative measures have become increasingly important to reduce the incidence and promote early detection of these cancers. Skin cancer prevention remains a challenging task to accomplish mainly due to the lack of reliable and sensitive methods to provide objective risk information that can educate and motivate individuals to avoid sunburn. Currently, minimal erythema dose (MED) is used as a marker of UVR. However, it is not an ideal marker because significant cancer-related molecular damage can occur after UVR exposure that cannot be detected by MED. Thus, over the recent years there has been significant interest in development of biomarkers indicative of exposure to UVR to improve early detection of cutaneous malignancies. Here, we will discuss emerging biomarkers for melanoma and NMSC that can help with risk stratification and targeted prevention and treatment.
© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  biomarker; melanoma; non-melanoma skin cancer; prevention; ultraviolet radiation

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Year:  2020        PMID: 32415889     DOI: 10.1111/exd.14114

Source DB:  PubMed          Journal:  Exp Dermatol        ISSN: 0906-6705            Impact factor:   3.960


  3 in total

1.  Paraoxonase-2 Silencing Enhances Sensitivity of A375 Melanoma Cells to Treatment with Cisplatin.

Authors:  Roberto Campagna; Tiziana Bacchetti; Eleonora Salvolini; Valentina Pozzi; Elisa Molinelli; Valerio Brisigotti; Davide Sartini; Anna Campanati; Gianna Ferretti; Annamaria Offidani; Monica Emanuelli
Journal:  Antioxidants (Basel)       Date:  2020-12-07

2.  A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.

Authors:  Mehak Arshad; Muhammad Attique Khan; Usman Tariq; Ammar Armghan; Fayadh Alenezi; Muhammad Younus Javed; Shabnam Mohamed Aslam; Seifedine Kadry
Journal:  Comput Intell Neurosci       Date:  2021-12-06

Review 3.  Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets.

Authors:  Elena-Georgiana Dobre; Carolina Constantin; Monica Neagu
Journal:  J Pers Med       Date:  2022-07-13
  3 in total

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