Literature DB >> 27002761

Copy number variation in archival melanoma biopsies versus benign melanocytic lesions.

Ahmed Mahas1, Keerti Potluri1, Michael N Kent2,3, Sameep Naik3, Michael Markey1.   

Abstract

BACKGROUND: Skin melanocytes can give rise to benign and malignant neoplasms. Discrimination of an early melanoma from an unusual/atypical benign nevus can represent a significant challenge. However, previous studies have shown that in contrast to benign nevi, melanoma demonstrates pervasive chromosomal aberrations.
OBJECTIVE: This substantial difference between melanoma and benign nevi can be exploited to discriminate between melanoma and benign nevi.
METHODS: Array-comparative genomic hybridization (aCGH) is an approach that can be used on DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissues to assess the entire genome for the presence of changes in DNA copy number. In this study, high resolution, genome-wide single-nucleotide polymorphism (SNP) arrays were utilized to perform comprehensive and detailed analyses of recurrent copy number aberrations in 41 melanoma samples in comparison with 21 benign nevi.
RESULTS: We found statistically significant copy number gains and losses within melanoma samples. Some of the identified aberrations are previously implicated in melanoma. Moreover, novel regions of copy number alterations were identified, revealing new candidate genes potentially involved in melanoma pathogenesis.
CONCLUSIONS: Taken together, these findings can help improve melanoma diagnosis and introduce novel melanoma therapeutic targets.

Entities:  

Keywords:  Array comparative genomic hybridization; FFPE; melanoma

Mesh:

Year:  2016        PMID: 27002761     DOI: 10.3233/CBM-160600

Source DB:  PubMed          Journal:  Cancer Biomark        ISSN: 1574-0153            Impact factor:   4.388


  2 in total

1.  Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.

Authors:  Haleema Attique; Sajid Shah; Saima Jabeen; Fiaz Gul Khan; Ahmad Khan; Mohammed ELAffendi
Journal:  Comput Intell Neurosci       Date:  2022-06-09

2.  A Shallow Convolutional Learning Network for Classification of Cancers Based on Copy Number Variations.

Authors:  Ahmad AlShibli; Hassan Mathkour
Journal:  Sensors (Basel)       Date:  2019-09-27       Impact factor: 3.576

  2 in total

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