Computational dissection of tissue contamination for identification of colon cancer-specific expression profiles.
Microarray profiles of bulk tumor tissues reflect gene expression corresponding to malignant cells as well as to many different types of contaminating normal cells. In this report, we assess the feasibility of querying baseline multitissue transcriptome databases to dissect disease-specific genes. Using colon cancer as a model tumor, we show that the application of Boolean operators (AND, OR, BUTNOT) for database searches leads to genes with expression patterns of interest. The BUTNOT operator for example allows the assignment of "expression signatures" to normal tissue specimens. These expression signatures were then used to computationally identify contaminating cells within conventionally dissected tissue specimens. The combination of several logic operators together with an expression database based on multiple human tissue specimens can resolve the problem of tissue contamination, revealing novel cancer-specific gene expression. Several markers, previously not known to be colon cancer associated, are provided.
Türeci
Ozlem
O
III. Medizinische Klinik und Poliklinik, Johannes Gutenberg Universität Mainz, D-55131 Mainz, Germany. Tureci@mail.uni-mainz.de
Ding
Jiayi
J
Hilton
Holly
H
Bian
Hongjin
H
Ohkawa
Hitomi
H
Braxenthaler
Michael
M
Seitz
Gerhard
G
Raddrizzani
Laura
L
Friess
Helmut
H
Buchler
Markus
M
Sahin
Ugur
U
Hammer
Juergen
J
D003106:Colon / Q000033:anatomy & histology / Q000378:metabolism; D003110:Colonic Neoplasms / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D014158:Transcription, Genetic