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Systematic comparison of nonmelanoma skin cancer microarray datasets reveals lack of consensus genes
R Van Haren1, D Feldman, A A Sinha
1Division of Dermatology and Cutaneous Sciences, Center for Investigative Dermatology, 4179 Biomedical and Physical Sciences Building, College of Human Medicine, Michigan State University, East Lansing, MI 48823, USA.
Background:
DNA microarray technology has revealed vast numbers of gene expression alterations associated with human malignancies. Assigning validity and biological significance to these changes, however, remains a considerable hurdle. Recently, microarray analysis has been applied to the study of nonmelanoma skin cancer.
Objectives:
To compare experimental data rigorously in order to strengthen conclusions regarding the pathogenesis of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), and to evaluate systematically the experimental and statistical parameters that may impact the degree of consensus among differentially expressed genes (DEGs) between studies.
Methods:
We performed a systematic comparison of 10 studies that applied DNA microarray technology to study BCC/SCC.
Results:
A total of 1133 DEGs collectively reported across the studies were compared, and 64 DEG overlaps were found: 18 DEG overlaps in SCC vs. SCC study comparisons, 18 DEG overlaps in BCC vs. BCC study comparisons and 28 DEG overlaps in BCC vs. SCC study comparisons. We documented differences in several experimental methods that may account for the relative lack of consensus between studies, including sample type, tissue procurement/handling, microarray chip and statistical analysis. The two most dysregulated biological pathways across all studies involved genes with enzymatic and structural/adhesion functions.
Conclusions:
DEGs that were found to overlap across two or more studies and biological pathways with the largest representation of DEGs across studies may be particularly relevant to disease pathogenesis and serve as targets for future therapy. In future work, more consistent experimental methods across laboratories may improve the validity of reported DEGs and strengthen conclusions drawn from microarray data.
Insights
This study compared gene expression data from 10 nonmelanoma skin cancer studies. Overlapping differentially expressed genes (DEGs) and pathways were identified, highlighting potential therapeutic targets and the need for consistent experimental methods.
Area of Science:
- Genomics
- Oncology
- Biotechnology
Background:
- DNA microarray technology identifies gene expression alterations in human cancers.
- Validating and understanding the biological significance of these gene expression changes is challenging.
- Microarray analysis is increasingly used to study nonmelanoma skin cancers like basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).
Purpose of the Study:
- To rigorously compare experimental data from multiple microarray studies on BCC and SCC.
- To identify consensus differentially expressed genes (DEGs) relevant to BCC and SCC pathogenesis.
- To evaluate factors influencing consensus among DEGs, such as experimental and statistical parameters.
Main Methods:
- Systematic comparison of 10 studies utilizing DNA microarray technology for BCC/SCC research.
- Analysis of 1133 reported DEGs across studies to identify overlaps.
- Documentation of variations in experimental methods (sample type, tissue handling, microarray chips, statistical analysis).
Main Results:
- Identified 64 DEG overlaps across studies: 18 in SCC vs. SCC, 18 in BCC vs. BCC, and 28 in BCC vs. SCC comparisons.
- Observed significant differences in experimental methods potentially explaining the lack of consensus.
- Enzymatic and structural/adhesion genes were the most dysregulated pathways across studies.
Conclusions:
- Overlapping DEGs and highly represented biological pathways are crucial for understanding BCC/SCC pathogenesis and may offer therapeutic targets.
- Standardizing experimental methods across research laboratories is essential to improve DEG validity and strengthen conclusions from microarray data.
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