Concurrent Analysis between Copy Number Variation and Gene Expression of Female Non-Smoking Lung Cancer in Taiwan
Date Issued
2011
Date
2011
Author(s)
Chang, Jung-Chih
Abstract
To identify genes with genomic alterations and/or transcriptional dysregulation, a concurrent analyzing method was developed to integrate data form copy number (CN) and gene expression (GE). This study contains three major parts: determine the correlation between CN and GE, perform pathway analysis by Gene Set Enrichment Analysis (GSEA), and to summarize all the pathways enriched by CN, GE, and correlation between CN and GE using a scoring method. Two datasets were analyzed to evaluate the performance of the method. The first dataset was from 44 female non-smoking lung cancer patients, which contain both paired normal and tumor tissues. The other dataset was retrieved from the Gene Expression Omnibus: GSE19539 ovarian cancer samples with two subtypes, endometrioid and serous. Both the datasets have CN and GE microarray data from the same individual. Copy number was analyzed by Affymetrix SNP 6.0 array in the both datasets. Gene expression profiles were analyzed by Affymetrix U133plus 2.0 array in the first dataset and Affymetrix 1.0 ST array in the second one. To further explore those identified pathways, Support Vector Machine (SVM) was used for classification. The classification results had higher prediction sensitivity and specificity compared with traditional analysis methods. In addition, using integration of data from both DNA and RNA levels is much biological meaningful, and may reveal much information about disease-causing genes and their regulation mechanisms. In summary, the results indicated that concurrent analyses may help to identify potential biomarkers with lower false positive rates.
Subjects
copy number
gene expression
concurrent analysis
gene set analysis
SDGs
Type
thesis
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