ABSTRACT
Using cDNA microarray assays we have observed a clear difference in the gene expression pattern between bone marrow stromal cells obtained from healthy children (CT) and from pediatric patients with either myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) associated with MDS (MDS-AML). The global gene function profiling analysis indicated that in the pediatric MDS microenvironment the disease stages may be characterized mainly by underexpression of genes associated with biological processes such as transport. Furthermore, a subset of downregulated genes related to endocytosis and protein secretion was able to discriminate MDS from MDS-AML.
Subject(s)
Bone Marrow/metabolism , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic/physiology , Myelodysplastic Syndromes/genetics , Bone Marrow/pathology , Child , Child, Preschool , Female , Humans , Infant , Leukemia, Myeloid, Acute/genetics , Leukemia, Myeloid, Acute/pathology , Male , Myelodysplastic Syndromes/pathology , Neoplasm Staging , Oligonucleotide Array Sequence Analysis/methods , RNA, Messenger/metabolism , Reverse Transcriptase Polymerase Chain Reaction/methods , Stromal Cells/metabolism , Stromal Cells/pathologyABSTRACT
BACKGROUND: Nodules of the thyroid gland are observed frequently in patients who undergo ultrasound studies. The majority of these nodules are benign, corresponding to goiters or adenomas, and only a small fraction corresponds to carcinomas. Among thyroid tumors, the diagnosis of follicular adenocarcinomas by preoperative fine-needle aspiration biopsy is a major challenge, because it requires inspection of the entire capsule to differentiate it from adenoma. Consequently, large numbers of patients undergo unnecessary thyroidectomy. METHODS: Using data from gene expression analysis, the authors applied Fisher linear discriminant analysis and searched for expression signatures of individual samples of adenomas and follicular carcinomas that could be used as molecular classifiers for the precise classification of malignant and nonmalignant lesions. RESULTS: Fourteen trios of genes were described that fulfilled the criteria for the correct classification of 100% of samples. The robustness of these trios was verified by using leave-1-out cross-validation and bootstrap analyses. The results demonstrated that, by combining trios, better classifiers could be generated that correctly classified >92% of samples. CONCLUSIONS: The strategy of classifiers based on individual signatures was a useful strategy for distinguishing between samples with very similar expression profiles.
