A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping

Selection of novel molecular markers is an important goal of cancer genomics studies. The aim of our analysis was to apply the multivariate bioinformatical tools to rank the genes – potential markers of papillary thyroid cancer (PTC) according to their diagnostic usefulness. We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC. We analyzed a 180-array dataset (90 HG-U95A and 90 HG-U133A oligonucleotide arrays), which included a collection of 57 PTCs, 61 benign thyroid tumors, and 62 apparently normal tissues. Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently in the classifiers (bootstrap-based feature ranking, BBFR); 2) ranking the samples, and thus detecting cases that were most difficult to classify (bootstrap-based outlier detection). The accuracy of PTC diagnosis was 98.5% for a 20-gene classifier, its 95% confidence interval (CI) was 95.9–100%, with the lower limit of CI exceeding 95% already for five genes. Only 5 of 180 samples (2.8%) were misclassified in more than 10% of bootstrap iterations. We specified 43 genes which are most suitable as molecular markers of PTC, among them some well-known PTC markers (MET, fibronectin 1, dipeptidylpeptidase 4, or adenosine A1 receptor) and potential new ones (UDP-galactose-4-epimerase, cadherin 16, gap junction protein 3, sushi, nidogen, and EGF-like domains 1, inhibitor of DNA binding 3, RUNX1, leiomodin 1, F-box protein 9, and tripartite motif-containing 58). The highest ranking gene, metallophosphoesterase domain-containing protein 2, achieved 96.7% of the maximum BBFR score

Analysis options for high-throughput sequencing in miRNA expression profiling

Background: Recently high-throughput sequencing (HTS) using next generation sequencing techniques became useful in digital gene expression profiling. Our study introduces analysis options for HTS data based on mapping to miRBase or counting and grouping of identical sequence reads. Those approaches allow a hypothesis free detection of miRNA differential expression. Methods: We compare our results to microarray and qPCR data from one set of RNA samples. We use Illumina platforms for microarray analysis and miRNA sequencing of 20 samples from benign follicular thyroid adenoma and malignant follicular thyroid carcinoma. Furthermore, we use three strategies for HTS data analysis to evaluate miRNA biomarkers for malignant versus benign follicular thyroid tumors. Results: High correlation of qPCR and HTS data was observed for the proposed analysis methods. However, qPCR is limited in the differential detection of miRNA isoforms. Moreover, we illustrate a much broader dynamic range of HTS compared to microarrays for small RNA studies. Finally, our data confirm hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p and both hsa-miR-144-3p and hsa-miR-144-5p as potential follicular thyroid cancer biomarkers. Conclusions: Compared to microarrays HTS provides a global profile of miRNA expression with higher specificity and in more detail. Summarizing of HTS reads as isoform groups (analysis pipeline B) or according to functional criteria (seed analysis pipeline C), which better correlates to results of qPCR are promising new options for HTS analysis. Finally, data opens future miRNA research perspectives for HTS and indicates that qPCR might be limited in validating HTS data in detail.:Background; Methods; Results; Discussion; Conclusion

Gene expression profile of medullary thyroid carcinoma - preliminary results

Wstęp: Rak rdzeniasty tarczycy (MTC, medullary thyroid carcinoma) jest nowotworem wywodzącym się z okołopęcherzykowych komórek C tarczycy. W około 20-30% przypadków rak ma charakter dziedziczny, a jego wystąpienie związane jest z mutacją germinalną w genie RET. Celem pracy była analiza profilu ekspresji genów charakterystycznego dla raka rdzeniastego tarczycy przy zastosowaniu mikromacierzy oligonukleotydowych wysokiej gęstości (HG U133A, Affymetrix) oraz porównanie profilu ekspresji pomiędzy postacią dziedziczną i sporadyczną tego nowotworu. Materiał i metody: Analizie poddano 24 próbki tkankowe, w tym 12 próbek MTC oraz 12 odpowiadających im tkanek zdrowych. Połowę grupy stanowiły przypadki dziedziczne (iMTC), a połowę sporadyczne (sMTC). Wyniki: Różnica w ekspresji genów pomiędzy tkanką zdrową a tkanką raka rdzeniastego była bardzo wyraźna i wynikała nie tylko z istnienia procesu nowotworowego, lecz również z odmiennego pochodzenia komórkowego. W analizie rozkładu na wartości osobliwe (SVD, singular value decomposition) dwie pierwsze składowe główne obrazowały różnicę guz/tkanka zdrowa, trzecia była związana przynajmniej częściowo z odpowiedzią immunologiczną. Głębsza analiza drugiej składowej głównej za pomocą testu ANOVA pozwoliła wyodrębnić dwie podgrupy w obrębie guzów nowotworowych, podział ten nie był jednak związany z różnicą iMTC/sMTC. Dopiero zastosowanie analizy wariancji w grupie genów wyodrębnionych za pomocą techniki maszyn podpierających (SVM, support vector machine) pozwoliło wskazać grupę genów różnicujących. Do genów o podwyższonej ekspresji w raku sporadycznym należy oksydaza monoaminowa B (MAOB, monoamine oxidase B) oraz receptor kwasu gamma-aminomasłowego (GABRR1, gamma-aminobutyric-acid receptor rho-1). W raku dziedzicznym podwyższoną ekspresję wykazywały: receptor opioidowego czynnika wzrostowego (OGFR, opioid growth factor receptor) i synaptotagmina V (SYT5, synaptotagmin V), zaangażowane w regulację cyklu komórkowego. Wnioski: Uzyskane dane nie pozwalają na wyodrębnienie istotnych różnic w profilu ekspresji genów pomiędzy sporadycznym i dziedzicznym rakiem rdzeniastym tarczycy, co przemawia za wspólnym torem transformacji nowotworowej.Introduction: Medullary thyroid carcinoma occurs both as a sporadic and a familial disease. Inherited MTC (iMTC) patients usually exhibit better prognosis than patients with sporadic form of MTC (sMTC), however, in both subtypes the outcome is unpredictable. No molecular markers contributing to the prognosis or predicting the type of therapy have been introduced to clinical practice until now. The aim of this study was to analyze gene expression pattern of MTC by high density oligonucleotide microarray. Material and methods: 24 samples were studied: 12 MTC and 12 corresponding normal tissues, (Affymetrix HG-U 133A). Among MTC patients there were half inherited cases and half sporadic ones. Results: First, the differences between MTC and thyroid tissue were analyzed by Singular Value Decomposition (SVD) which indicated three main modes determining the variability of gene expression profile: the first two were related to the tumor/normal tissue difference and the third one was related to the immune response. The characteristic expression pattern, beside of numerous changes within cancer- related genes, included many up-regulated genes specific for thyroid C cells. Further analysis of the second component revealed two subgroups of MTC, but the subdivision was not related to the iMTC/sMTC difference. Recursive Feature Replacement (RFR) confirmed the very similar expression profile in both forms of MTC. With subsequent ANOVA analysis some genes with differential expression could be specified, among them monoamine oxidase B (MAOB) and gamma-aminobutyric acid receptor (GABRR1) which were consistently up-regulated in sMTC. In contrary, some genes involved in regulation of cell proliferation: opioid growth factor receptor(OGFR) and synaptotagmin V (SYT 5) were up-regulated in iMTC. Conclusions: The obtained data indicate a very similar gene expression pattern in inherited and sporadic MTC. Minor differences in their molecular profile require further analysis

Bayesian experts in exploring reaction kinetics of transcription circuits

Motivation Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics

Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation

<p>Abstract</p> <p>Background</p> <p>Several DNA microarray based expression signatures for the different clinically relevant thyroid tumor entities have been described over the past few years. However, reproducibility of these signatures is generally low, mainly due to study biases, small sample sizes and the highly multivariate nature of microarrays. While there are new technologies available for a more accurate high throughput expression analysis, we show that there is still a lot of information to be gained from data deposited in public microarray databases. In this study we were aiming (1) to identify potential markers for papillary thyroid carcinomas through meta analysis of public microarray data and (2) to confirm these markers in an independent dataset using an independent technology.</p> <p>Methods</p> <p>We adopted a meta analysis approach for four publicly available microarray datasets on papillary thyroid carcinoma (PTC) nodules versus nodular goitre (NG) from N2-frozen tissue. The methodology included merging of datasets, bias removal using distance weighted discrimination (DWD), feature selection/inference statistics, classification/crossvalidation and gene set enrichment analysis (GSEA). External Validation was performed on an independent dataset using an independent technology, quantitative RT-PCR (RT-qPCR) in our laboratory.</p> <p>Results</p> <p>From meta analysis we identified one gene (SERPINA1) which identifies papillary thyroid carcinoma against benign nodules with 99% accuracy (n = 99, sensitivity = 0.98, specificity = 1, PPV = 1, NPV = 0.98). In the independent validation data, which included not only PTC and NG, but all major histological thyroid entities plus a few variants, SERPINA1 was again markedly up regulated (36-fold, p = 1:3*10^-10) in PTC and identification of papillary carcinoma was possible with 93% accuracy (n = 82, sensitivity = 1, specificity = 0.90, PPV = 0.76, NPV = 1). We also show that the extracellular matrix pathway is strongly activated in the meta analysis data, suggesting an important role of tumor-stroma interaction in the carcinogenesis of papillary thyroid carcinoma.</p> <p>Conclusions</p> <p>We show that valuable new information can be gained from meta analysis of existing microarray data deposited in public repositories. While single microarray studies rarely exhibit a sample number which allows robust feature selection, this can be achieved by combining published data using DWD. This approach is not only efficient, but also very cost-effective. Independent validation shows the validity of the results from this meta analysis and confirms SERPINA1 as a potent mRNA marker for PTC in a total (meta analysis plus validation) of 181 samples.</p

Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder involving variations in the transcriptome of many genes. AD does not affect all brain regions simultaneously. Identifying the differences among the affected regions may shed more light onto the disease progression. We developed a novel method involving the differential topology of gene coexpression networks to understand the association among affected regions and disease severity.</p> <p>Methods</p> <p>We analysed microarray data of four regions - entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC) and middle temporal gyrus (MTG) from AD affected and normal subjects. A coexpression network was built for each region and the topological overlap between them was examined. Genes with zero topological overlap between two region-specific networks were used to characterise the differences between the two regions.</p> <p>Results and conclusion</p> <p>Results indicate that MTG shows early AD pathology compared to the other regions. We postulate that if the MTG gets affected later in the disease, post-mortem analyses of individuals with end-stage AD will show signs of early AD in the MTG, while the EC, HIP and PCC will have severe pathology. Such knowledge is useful for data collection in clinical studies where sample selection is a limiting factor as well as highlighting the underlying biology of disease progression.</p

Mathematical models for immunology:current state of the art and future research directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years