In silico identification and microarray analysis of human mucin-like genes

Mucins, which are highly O-glycosylated proteins, are involved in a variety of diseases. Most of the known mucins show little sequence conservation. To identify novel candidate mucins that might be helpful for diagnosis and therapy of diseases, bioinformatics approaches other than homology searches are required. We have developed two bioinformatics approaches that focus on either the amino acid compositions or the repeated sequences in regions carrying 0-linked glycans. Both approaches identify candidate mucins efficiently from the human reference sequence database. The former and latter approaches identified three and two novel mucin-like genes, respectively. We used Affymetrix GeneChip oligonucleotide microarrays to analyze the mucins identified from the database and found that three human mucin genes differed significantly in expression between normal tissues/cells and cancer cell lines. A combination of our bioinformatics approaches and the oligonucleotide microarray method will be useful for the efficient identification of candidate human mucins. Further study of the mucins in cancer tissues will facilitate the direct evaluation of their potential for cancer diagnosis and therapy