Comprehensive Assessment of Copy Number Alterations Uncovers Recurrent AIFM3 and DLK1 Copy Gain in Medullary Thyroid Carcinoma.
Aline Neves AraujoCléber Pinto CamachoThais Biude MendesSusan Chow LindseyLais MoraesMarta MiyazawaRosana DelceloRenata PellegrinoDiego Robles MazzottiRui Monteiro de Barros MacielJanete Maria CeruttiPublished in: Cancers (2021)
Medullary thyroid carcinoma (MTC) is a malignant tumor originating from thyroid C-cells that can occur either in sporadic (70-80%) or hereditary (20-30%) form. In this study we aimed to identify recurrent copy number alterations (CNA) that might be related to the pathogenesis or progression of MTC. We used Affymetrix SNP array 6.0 on MTC and paired-blood samples to identify CNA using PennCNV and Genotyping Console software. The algorithms identified recurrent copy number gains in chromosomes 15q, 10q, 14q and 22q in MTC, whereas 4q cumulated losses. Coding genes were identified within CNA regions. The quantitative PCR analysis performed in an independent series of MTCs (n = 51) confirmed focal recurrent copy number gains encompassing the DLK1 (14q32.2) and AIFM3 (22q11.21) genes. Immunohistochemistry confirmed AIFM3 and DLK1 expression in MTC cases, while no expression was found in normal thyroid tissues and few MTC samples were found with normal copy numbers. The functional relevance of CNA was also assessed by in silico analysis. CNA status correlated with protein expression (DLK1, p = 0.01), tumor size (DLK1, p = 0.04) and AJCC staging (AIFM3p = 0.01 and DLK1p = 0.05). These data provide a novel insight into MTC biology, and suggest a common CNA landscape, regardless of if it is sporadic or hereditary MTC.
Keyphrases
- copy number
- genome wide
- mitochondrial dna
- dna methylation
- poor prognosis
- high resolution
- gene expression
- long non coding rna
- induced apoptosis
- machine learning
- high throughput
- lymph node
- oxidative stress
- transcription factor
- cell proliferation
- mass spectrometry
- pet ct
- data analysis
- single cell
- deep learning
- amyotrophic lateral sclerosis
- endoplasmic reticulum stress
- artificial intelligence
- high density