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Metadata

Name
Novel Lung Cancer-Related Genes Identified by a Non-Gapped Array-Comparative Genomic Hybridization Microarray in Asian and Caucasian Populations
Repository
Gene Expression Omnibus
Identifier
geo.series:GSE21276
Description
This study constructed a non-gapped bacterial artificial chromosomes (BAC) array containing 3604 BAC clones covering 18 lung cancer-related chromosome imbalance hotspot regions. Using this specialized array, DNA from tumor and normal tissues of 40 Asian and 20 Caucasian non-small cell lung cancer (NSLCL) patients was analyzed by array-comparative genomic hybridization (array-CGH). Block-wise normalization and a Bayes regression approach were used to refine the chromosomal imbalance regions identified by array-CGH. The array-CGH results then analyzed by MetaCore software to identify potential cancer-related genes. Finally, 273 genes showing significantly associated with molecular pathway, cancer biomarker, and gene ontology database, such as ZNF322A on 6p22.1, ARHGAP19 on 10q24.1, FRAT2 on 10q24.1, and PAFAH1B1 on 17p13.3 functioning in MAPK, Rho GTPase, and Wnt, and motility control pathways with frequent copy number gain were selected. This study mapped concisely the novel oncogenes or tumor suppressor genes in lung cancer and revealed insights of difference on chromosomal imbalance between lung cancer from Asian and Caucasian.
Data or Study Types
Genome variation profiling by genome tiling array
Source Organization
National Center for Biotechnology Information
Access Conditions
available
Year
2012
Access Hyperlink
http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GSE21276

Distributions

  • Encoding Format: Bioproject ; URL: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA126579
  • Encoding Format: TXT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE21276/matrix/
  • Encoding Format: MINiML ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE21276/miniml/
  • Encoding Format: SOFT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE21276/soft/
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.