Molecular signatures of basal cell carcinoma susceptibility and pathogenesis: A genomic approach

Heller,Elizabeth  Rose; Gor,Ankit; Wang,Dan; Hu,Qiang; Lucchese,Alberta; Kanduc,Darja; Katdare,Meena; Liu,Song; Sinha,Animesh  A.

doi:10.3892/ijo.2012.1725

Molecular signatures of basal cell carcinoma susceptibility and pathogenesis: A genomic approach

Authors:
- Elizabeth Rose Heller
- Ankit Gor
- Dan Wang
- Qiang Hu
- Alberta Lucchese
- Darja Kanduc
- Meena Katdare
- Song Liu
- Animesh A. Sinha
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Affiliations: Department of Dermatology, State University of New York at Buffalo and Roswell Park Cancer Institute, Buffalo, NY, USA, Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, NY, USA, Department of Odontostomatological, Orthodontics, and Surgical Disciplines, Faculty of Medicine, Second University of Naples (SUN), Naples, Italy, Department of Biochemistry and Molecular Biology, University of Bari, Italy, Department of Surgery, Carcinogenesis and Chemoprevention Laboratory, Weill Medical College of Cornell University, New York, NY, USA
Published online on: November 30, 2012 https://doi.org/10.3892/ijo.2012.1725
Pages: 583-596

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Abstract

Gene expression profiling can be useful for phenotypic classification, investigation of functional pathways, and to facilitate the search for disease risk genes through the integration of transcriptional data with available genomic information. To enhance our understanding of the genetic and molecular basis of basal cell carcinoma (BCC) we performed global gene expression analysis to generate a disease-associated transcriptional profile. A gene signature composed of 331 differentially expressed genes (DEGs) was generated from comparing 4 lesional and 4 site-matched control samples using Affymetrix Human Genome U95A microarrays. Hierarchical clustering based on the obtained gene signature separated the samples into their corresponding phenotype. Pathway analysis identified several significantly overrepresented pathways including PPAR-γ signaling, TGF-β signaling and lipid metabolism, as well as confirmed the importance of SHH and p53 in the pathogenesis of BCC. Comparison of our microarray data with previous microarray studies revealed 13 DEGs overlapping in 3 studies. Several of these overlapping genes function in lipid metabolism or are components of the extracellular matrix, suggesting the importance of these and related pathways in BCC pathogenesis. BCC-associated DEGs were mapped to previously reported BCC susceptibility loci including 1p36, 1q42, 5p13.3, 5p15 and 12q11-13. Our analysis also revealed transcriptional ‘hot spots’ on chromosome 5 which help to confirm (5p13 and 5p15) and suggest novel (5q11.2-14.3, 5q22.1-23.3 and 5q31-35.3) disease susceptibility loci/regions. Integrating microarray analyses with reported genetic information helps to confirm and suggest novel disease susceptibility loci/regions. Identification of these specific genomic and/or transcriptional targets may lead to novel diagnostic and therapeutic modalities.

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