ABSTRACT
Here, we describe the identification of three human genes with altered expression in thyroid diseases. One of them corresponds to insulin-like growth factor binding protein 5 (IGFBP5), which has already been described as over expressed in other cancers and, for the first time, is identified as overexpressed in thyroid tumors. The other genes, named 44 and 199, are ESTs with yet unknown function and were mapped on human chromosomes seven and four, respectively. We determined by RT-PCR the expression level of these genes in ten samples of disease-free thyroid, ten of goiter, nine of papillary carcinoma, ten of adenoma and seven of follicular carcinoma and the significance of observed differences was statistically determined. IGFBP-5 and gene 44 were significantly overexpressed in papillary carcinoma when compared to normal and goiter. Genes 44 and 199 were differentially expressed in follicular carcinoma and adenoma when compared to normal thyroid tissue.
Subject(s)
Adenocarcinoma, Follicular/genetics , Adenoma/genetics , Carcinoma, Papillary/genetics , Expressed Sequence Tags , Goiter/genetics , Insulin-Like Growth Factor Binding Protein 5/genetics , Thyroid Neoplasms/genetics , Adenocarcinoma, Follicular/metabolism , Adenocarcinoma, Follicular/pathology , Adenoma/metabolism , Adenoma/pathology , Blotting, Southern , Carcinoma, Papillary/metabolism , Carcinoma, Papillary/pathology , Chromosomes, Human, Pair 4/genetics , Chromosomes, Human, Pair 7/genetics , DNA Primers/chemistry , Diagnosis, Differential , Gene Expression Profiling , Goiter/metabolism , Goiter/pathology , Humans , Insulin-Like Growth Factor Binding Protein 5/metabolism , RNA, Messenger/analysis , Reverse Transcriptase Polymerase Chain Reaction , Thyroid Gland/metabolism , Thyroid Neoplasms/metabolism , Thyroid Neoplasms/pathologyABSTRACT
Using cDNA fragments from the FAPESP/lICR Cancer Genome Project, we constructed a cDNA array having 4512 elements and determined gene expression in six normal and six tumor gastric tissues. Using t-statistics, we identified 80 cDNAs whose expression in normal and tumor samples differed more than 3.5 sample standard deviations. Using Self-Organizing Map, the expression profile of these cDNAs allowed perfect separation of malignant and non-malignant samples. Using the supervised learning procedure Support Vector Machine, we identified trios of cDNAs that could be used to classify samples as normal or tumor, based on single-array analysis. Finally, we identified genes with altered linear correlation when their expression in normal and tumor samples were compared. Further investigation concerning the function of these genes could contribute to the understanding of gastric carcinogenesis and may prove useful in molecular diagnostics.
