Background: Recent studies have identified thousands of sense-antisense gene pairs across different genomes by computational mapping of cDNA sequences. These studies have shown that approximately 25% of all transcriptional units in the human and mouse genomes are involved in cis-sense-antisense pairs. However, the number of known sense-antisense pairs remains limited because currently available cDNA sequences represent only a fraction of the total number of transcripts comprising the transcriptome of each cell type. Methods. To discover novel antisense transcripts encoded in the antisense strand of important genes, such as cancer-related genes, we conducted expression analyses of antisense transcripts using our custom microarray platform along with 2376 probes designed specifically to detect the potential antisense transcripts of 501 well-known genes suitable for cancer research. Results: Using colon cancer tissue and normal tissue surrounding the cancer tissue obtained from 6 patients, we found that antisense transcripts without poly(A) tails are expressed from approximately 80% of these well-known genes. This observation is consistent with our previous finding that many antisense transcripts expressed in a cell are poly(A)-. We also identified 101 and 71 antisense probes displaying a high level of expression specifically in normal and cancer tissues respectively. Conclusion: Our microarray analysis identified novel antisense transcripts with expression profiles specific to cancer tissue, some of which might play a role in the regulatory networks underlying oncogenesis and thus are potential targets for further experimental validation. Our microarray data are available at http://www.brc.riken.go.jp/ncrna2007/viewer-Saito-01/index.html. © 2011 Saito et al; licensee BioMed Central Ltd.
CITATION STYLE
Saito, R., Kohno, K., Okada, Y., Osada, Y., Numata, K., Kohama, C., … Kiyosawa, H. (2011). Comprehensive expressional analyses of antisense transcripts in colon cancer tissues using artificial antisense probes. BMC Medical Genomics, 4. https://doi.org/10.1186/1755-8794-4-42
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