40 research outputs found
Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers
BackgroundGenetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions. Methodology/Principal Findings Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPARγ, TSHR, GNAS and NRAS genes. Conclusion/Significance We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas
Death-associated protein 3 is overexpressed in human thyroid oncocytic tumours
Background: The human death-associated protein 3 (hDAP3) is a GTP-binding constituent of the small subunit of the mitochondrial ribosome with a pro-apoptotic function.Methods: A search through publicly available microarray data sets showed 337 genes potentially coregulated with the DAP3 gene. The promoter sequences of these 337 genes and 70 out of 85 mitochondrial ribosome genes were analysed in silico with the DAP3 gene promoter sequence. The mitochondrial role of DAP3 was also investigated in the thyroid tumours presenting various mitochondrial contents. Results: The study revealed nine transcription factors presenting enriched motifs for these gene promoters, five of which are implicated in cellular growth (ELK1, ELK4, RUNX1, HOX11-CTF1, TAL1-ternary complex factor 3) and four in mitochondrial biogenesis (nuclear respiratory factor-1 (NRF-1), GABPA, PPARG-RXRA and estrogen-related receptor alpha (ESRRA)). An independent microarray data set showed the overexpression of ELK1, RUNX1 and ESRRA in the thyroid oncocytic tumours. Exploring the thyroid tumours, we found that DAP3 mRNA and protein expression is upregulated in tumours presenting a mitochondrial biogenesis compared with the normal tissue. ELK1 and ESRRA were also showed upregulated with DAP3. Conclusion: ELK1 and ESRRA may be considered as potential regulators of the DAP3 gene expression. DAP3 may participate in mitochondrial maintenance and play a role in the balance between mitochondrial homoeostasis and tumourigenesis
Apple II PASCAL programs for molecular biologists.
A collection of PASCAL programs designed for the Apple II microcomputer is presented. These DNA sequence handling and analysis programs are interactive and may be used even by people with no computer experience. The package allows the user to enter a sequence from the keyboard, to modify it, to generate the reverse complement, to create new sequences from parts of other ones, to display or print sequences in various formats. Some analysis tasks are also performed: Translation, searches for restriction sites, for homology with subsequences, either perfect or with an adjustable match percentage. In addition, two programs are also included: The first one allows DNA data sequences generated with a BASIC program under the CP/M operating system to be used with these PASCAL programs. The second one is designed for the automatic assembly of DNA fragments sequences, obtained with the GILBERT-MAXAM or M13 techniques, into a complete sequence
Une sélection de 13 gènes et dix miRNA issus du transcriptome et du miRNome de 90 tumeurs thyroïdiennes et tissus sains pour la classification des tumeurs de potentiel de malignité incertain
DNA microarray and miRNA analyses reinforce the classification of follicular thyroid tumors
Context: Focusing on mitochondrial function and thyroid tumorigenesis, we used an integrative approach to identify relevant biomarkers for borderline thyroid lesions. Design: Using cDNA and microRNA (miRNA) microarrays and quantitative RT-PCR analysis (qPCR), we explored samples of various types of thyroid tumors including 25 benign follicular adenomas represented by macrofollicular variants of thyroid adenomas, 38 oncocytic variants of follicular thyroid tumors, 19 papillary thyroid carcinomas, and 10 tumors of uncertain malignant potential, together with 53 normal thyroid tissue samples. Results: Our transcriptomic analysis, which highlighted discrepancies between controls and tumor tissues, as well as between various tumor types, led to the identification of 13 genes, allowing discrimination between the thyroid adenomas, oncocytic variants of follicular thyroid tumors, and papillary thyroid carcinomas, whereas the tumors of uncertain malignant potential were found to overlap these classes. Five of these genes (TP53, HOXA9, RUNX1, MYD88, and CITED1), with a differential expression confirmed by qPCR analysis, are implicated in tumorigenesis, 4 in mitochondrial metabolism (MRPL14, MRPS2, MRPS28, and COX6A1), and 2 in thyroid metabolic pathways (CaMKIINalpha and TPO). The global miRNA analysis revealed 62 differential miRNAs, the expression level for 10 of these being confirmed by qPCR. The differential expression of the miRNAs was in accordance with the modulation of gene expression and the ontologies revealed by our transcriptomic analysis. Conclusions: These findings reinforce the classification of follicular thyroid tumors established by the World Health Organization, and our technique offers a novel molecular approach to refine the classification of thyroid tumors of uncertain malignant potential
