title: Concurrent analysis of copy number variation and gene expression: application in paired non-smoking female lung cancer patients.
abstract: Recent studies indicate that both genomic alterations and transcriptional dysregulation influence the disease progresses. This study proposes a method identifying pathways by integrating copy numbers (CN), gene expressions (GE) and their correlations. A lung cancer patients dataset with both normal and tumor tissues is utilized to evaluate the performance of the proposed method. To further appraise the predicting abilities of those pathways, these patients are classified by support vector machines. Based on the classification results, pathways integrating CN, GE and their correlations is more informative and biologically meaningful and perform better than pathways obtained by only CN or only GE.
authors.affiliation: Department of Statistics and Informatics Science, Providence University, 200, Chung Chi Rd., Taichun, Taiwan. pcchen3@pu.edu.tw
authors.forename: Pei-Chun
authors.initials: PC
authors.lastname: Chen
authors.forename: Tzu-Pin
authors.initials: TP
authors.lastname: Lu
authors.forename: Jung-Chih
authors.initials: JC
authors.lastname: Chang
authors.forename: Liang-Chuan
authors.initials: LC
authors.lastname: Lai
authors.forename: Mong-Hsun
authors.initials: MH
authors.lastname: Tsai
authors.forename: Chuhsing Kate
authors.initials: CK
authors.lastname: Hsiao
authors.forename: Eric Y
authors.initials: EY
authors.lastname: Chuang
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics; D056915:DNA Copy Number Variations; D005260:Female; D015870:Gene Expression; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D012907:Smoking / Q000235:genetics; D060388:Support Vector Machine; D014158:Transcription, Genetic
chemical_list: D014408:Biomarkers, Tumor