title: Identification of novel universal housekeeping genes by statistical analysis of microarray data.
abstract: Housekeeping genes are widely used as internal controls in a variety of study types, including real time RT-PCR, microarrays, Northern analysis and RNase protection assays. However, even commonly used housekeeping genes may vary in stability depending on the cell type or disease being studied. Thus, it is necessary to identify additional housekeeping-type genes that show sample-independent stability. Here, we used statistical analysis to examine a large human microarray database, seeking genes that were stably expressed in various tissues, disease states and cell lines. We further selected genes that were expressed at different levels, because reference and target genes should be present in similar copy numbers to achieve reliable quantitative results. Real time RT-PCR amplification of three newly identified reference genes, CGI-119, CTBP1 and GOLGAl, alongside three well-known housekeeping genes, B2M, GAPD, and TUBB, confirmed that the newly identified genes were more stably expressed in individual samples with similar ranges. These results collectively suggest that statistical analysis of microarray data can be used to identify new candidate housekeeping genes showing consistent expression across tissues and diseases. Our analysis identified three novel candidate housekeeping genes (CGI-119, GOLGA1, and CTBP1) that could prove useful for normalization across a variety of RNA-based techniques.
authors: Department of Chemistry, Dongguk University, Seoul 100-715, Korea. skim@dongguk.edu
authors: Seram
authors: S
authors: Lee
authors: Minjoung
authors: M
authors: Jo
authors: Jungeun
authors: J
authors: Lee
authors: Sang Seok
authors: SS
authors: Koh
authors: Soyoun
authors: S
authors: Kim
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D020043:Genes, Essential / Q000235:genetics*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D020133:Reverse Transcriptase Polymerase Chain Reaction