Mohutný rozvoj molekulárně biologických metod v posledních deseti letech je charakterizován produkcí velkého množství dat. Nejinak je tomu i v případě technologie DNA mikročipů, která umožňuje v jednom experimentu sledovat expresi desítek tisíc genů najednou. Kvantum získaných experimentálních dat je však pro relevantní medicínské závěry nutné vhodně analyzovat a interpretovat. Tento článek je věnován statistickým metodám, které lze pro hodnocení dat získaných z DNA mikročipů použít. Tyto metody lze rozdělit do tří velkých skupin: shlukovací metody, metody pro identifikaci rozdílně exprimovaných genů a klasifikační metody. Shlukovací metody slouží k nalezení homogenních skupin pacientů s podobným expresním profilem nebo skupin genů s podobným chováním, metody pro identifikaci rozdílně exprimovan ých genů hledají geny specifické svojí aktivitou pro určitou biologickou tkáň, zatímco klasifikační metody slouží k nalezení diskriminačního pravidla pro přesnou diagnostiku nových pacientů do jedné z definovaných skupin.
Last decade led to massive progress in the molecular biology methods which was accompanied by the production of large amount of data. This is also the case of the gene expression microarray technology that makes it feasible to study thousands of genes simultaneously. However, for relevant medical inference there is the need for appropriate evaluation and interpretation of this large quantity of experimental data. This paper is dedicated to statistical methods that can be used for the evaluation of gene expression data. These methods can be split into three main categories: clustering methods, methods for identification of differentially expressed genes and classification techniques. Clustering methods can be used for finding of homogenous groups of patients or genes with similar expression profile, methods for identification of differentially expressed genes find genes specific for activity of certain biological tissue while classification techniques are used for setting up a discrimination rule for precise diagnostics of newly diagnosed patients to one of the previously defined classes.
- MeSH
- Discriminant Analysis MeSH
- Gene Expression genetics MeSH
- Financing, Organized MeSH
- Humans MeSH
- Oligonucleotide Array Sequence Analysis methods statistics & numerical data utilization MeSH
- Cluster Analysis MeSH
- Models, Statistical MeSH
- Statistics as Topic MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
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- MeSH
- Chromosome Aberrations * MeSH
- Cytogenetic Analysis MeSH
- In Situ Hybridization, Fluorescence MeSH
- Microarray Analysis MeSH
- Neoplasms pathology MeSH
- Terminology as Topic MeSH
- Publication type
- Monograph MeSH
- Conspectus
- Biochemie. Molekulární biologie. Biofyzika
- NML Fields
- molekulární biologie, molekulární medicína
S nástupem NGS a zvláště sekvenování transkriptomu došlo k nové specializaci microarray technologie a jejímu rozvoji k novým prakticky nenahraditelným nástrojům diagnostiky. Jakkoliv se objevují názory, že NGS zcela nahradí microarrays, trendy vyplývající z odborných článků, výzkumné a klinické praxe hovoří o výrazně rozdílném vývoji. NGS zejména v oblasti měření DNA exprese a transkriptomu skutečně přináší potenciál získání relativně kvalitativně bohatší informace, která se využívá zejména při hledání neznámých sekvencí, nicméně u analýz aplikovaných a známých sekvencí si microarrays naopak udržují dominantní postavení. Zcela unikátní platformou jsou tzv. VIPTM čipy, které přinesly možnost provádět populační studie s DNA až 10 tisíc jedinců v 10lokusech. Zároveň je opomíjen fakt, že současné sekvenační technologie mohou přinést informace pouze o DNA, na rozdíl od široké škály biomarkerových sond, které lze kotvit a tudíž analyzovat na substrátu microarrays, včetně proteinových biočipů, které zaznamenali velký boom především v poslední dekádě a těší se praktickému a klinickému využití.
With the advent of NGS and especially the transcriptome sequencing, a novel specialization of microarray technology has taken place and its development towards new practically indispensable diagnostic tools. Although it has been argued that NGS will eventually replace microarrays, the trends inferred from scientific articles, the research and clinical practice as well as the actual principal difference between these technologies, corroborate a significantly different development. In particular, in the measurement of DNA expression, NGS has brought the potential of obtaining a relatively richer qualitative information, which is used mainly for finding unknown sequences, however, microarrays conversely maintain dominance in the case of applied analyses and of known sequences. Furthermore, in the last decade, an entirely unique platform, called VIPTM microarrays, has been developed, which enabled to carry out cost -effectively population -based studies of DNA of up to 10,000 individuals in 10 loci. Moreover, another fact has been condoned, i.e. the current sequencing technologies can address only information related to DNA. Microarrays, to the contrary, permit immobilizing a wide spectrum of biomarker probes on the substrate and thus analyze a variety starting from DNA through proteins, small molecules, cells, tissues and others. As a consequence, the two technologies seem to be rather in a convenient complementarity than in any real competition.
The 16S-23S ribosomal internal transcribed spacer (ITS1) is often used as a subspecies or strain-specific molecular marker for various kinds of bacteria. However, the presence of different copies of ITS1 within a single genome has been reported. Such mosaicism may influence correct typing of many bacteria and therefore knowledge about exact configuration of this region in a particular genome is essential. In order to screen the variability of ITS1 among and within Pseudomonas syringae genomes, an oligonucleotide microarray targeting different configurations of ITS1 was developed. The microarray revealed seven distinct variants in 13 pathovars tested and detected mosaicism within the genomes of P. syringae pv. coronafaciens, pisi, syringae and tabaci. In addition, the findings presented here challenge the using of rRNA analysis for pathovar and strain determination.
- MeSH
- DNA, Bacterial genetics MeSH
- Phylogeny MeSH
- Genetic Variation MeSH
- DNA, Ribosomal Spacer genetics MeSH
- Molecular Sequence Data MeSH
- Plant Diseases microbiology MeSH
- Pseudomonas syringae classification genetics isolation & purification MeSH
- RNA, Ribosomal, 16S genetics MeSH
- RNA, Ribosomal, 23S genetics MeSH
- Plants microbiology MeSH
- Base Sequence MeSH
- Oligonucleotide Array Sequence Analysis methods MeSH
- Sequence Alignment MeSH
- Bacterial Typing Techniques MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Research Support, Non-U.S. Gov't MeSH
The urokinase-type plasminogen activator (uPA) and PA inhibitor 1 (PAI-1) play important roles in breast cancer metastasis through cell migration and invasion. They are clinically applicable prognostic and predictive markers. High levels of uPA and PAI-1 are associated with high risk of recurrence and adjuvant chemotherapy provides substantial benefit for this breast cancer population. The current sole validated method for quantifying uPA level in breast tumour tissue is ELISA assay. It requires 50–300 mg of fresh or frozen tissue, which is the main limitation for routine use. In this study, we evaluated the performances of customized antibody microarray to quantify uPA concentration from reduced extraction solution of breast tumour tissue and compared it with standard ELISA kit. We firstly optimized the elaboration of customized antibody microarray in order to sensitively detect and quantify uPA standard solutions. In the best conditions, we analysed uPA concentration in 16 cytosolic extracts from breast tumour tissue. Results showed that our customized antibody microarray could correctly quantify uPA concentration while consuming 100 times less volume of tumour tissue extraction solution than ELISA. Our antibody microarray is a powerful and promising tool for the miniaturization of the immunoassay quantification of uPA from breast tumour tissue extracts.
- MeSH
- Urokinase-Type Plasminogen Activator * analysis immunology adverse effects MeSH
- Biomarkers MeSH
- Tissue Array Analysis * methods instrumentation MeSH
- Enzyme-Linked Immunosorbent Assay MeSH
- Immunoassay methods MeSH
- Humans MeSH
- Neoplasm Metastasis MeSH
- Breast Neoplasms * diagnosis immunology MeSH
- Prognosis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
cDNA microarray technology is widely used in various biological and medical disciplines to determine gene expression profiles. Unfortunately, this technology requires a large quantity of input RNA. Since there is an increasing need for more precise analyses of defined cell subpopulations with low cell counts, working protocols using a minimal number of input cells are required. Optimal RNA isolation and its accurate amplification are crucial to the success of these protocols. The HL-60 cell line was used in the search for a suitable protocol that can be used for clinical samples of CD34+ haematopoietic cells obtained from bone marrow. The goal was to discover the best method for isolating and amplifying RNA from a small number of cells. Our evaluation of various methods and kits available in the market revealed that the combination of RNAqueous™ Kit for RNA isolation and the SenseAmp Plus Kit for one-round RNA amplification produced the best results. This article presents a verified protocol describing a reliable and reproducible method for obtaining enough input RNA for microarray experiments when the number of cells is limited.
miRNA profile of luminal breast cancer subtyptes in Slovak womenCieľ štúdie: Aberantná expresia krátkych, nekódujúcich molekúl RNA (miRNA) sa podieľa na vzniku, progresii a metastázovaní karcinómu prsníka. Úroveň expresie vybraných miRNA je úzko spojená nielen s imunohistochemickým profilom a histopatologickými parametrami, ale aj s klinickými výsledkami, prognózou a terapeutickou odpoveďou. Cieľom tohto výskumu bola analýza celého spektra miRNA metódou microarray a zadefinovanie panelu relevantných miRNA objasňujúcich biologickú charakteristiku luminálnych podtypov karcinómu prsníka. Typ štúdie: Prierezová, základný výskum. Názov a sídlo pracoviska: Martinské centrum pre biomedicínu, Jesseniova lekárska fakulta v Martine, Univerzita Komenského v Bratislave, Martin, Slovenská republika. Metodika: Sledovaný súbor pozostával zo 16 tkanív Luminal A/Luminal B karcinómu prsníka a 16 tkanív prsného tkaniva bez patologického nálezu. miRNA profil sa analyzoval microarray technológiou na sklíčku SurePrint G3 Human miRNA kit v.21, ktoré obsahuje 2549 miRNA. Výsledky sa hodnotili knižnicou AgiMicroRNA Bioconductor library v rámci balíka Limma. Výsledky: Analýza najnižšej hodnoty FDR p-value a najvyššej hodnoty logFC označila za najdôležitejšiu onkogénnu miRNA s vyššou expresiou v súbore nádorových tkanív v porovnaní s normálnymi tkanivami miR-182, nasledovala miR-21, miR-342-3p a miR-342-5p a miR6826. Medzi miRNA s nižšou expresiou dominovala miR-4324 a kluster miR-99a/let7c/miR-125b. Záver: Získané výsledky prispievajú k biologickej charakterizácií skupiny luminálnych typov karcinómu prsníka, ponúkajú východisko pre nadväzujúce projekty v kontexte objasnenia príslušných signálnych dráh mechanizmov karcinogenézy a sľubujú nové a inovatívne možnosti cielenej terapie zameranej na špecifické miRNA zapojené v dôležitých mechanizmoch karcinogenézy.
Objective: Aberrant expression of short, non-coding RNA molecules (miRNA) leads to breast cancer initiation, progression and metastasing. The miRNA expression level associates with imunohistochemical profile, histopathological parameters, clinical outcomes, prognoses and therapeutical response. The aim of this study was to analyse the whole spectrum of miRNA by microarray method and to define relevant miRNAs describing biological characteristics of luminal breast cancer subtypes. Design: Cross-sectional study, basic research. Setting: Biomedical center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia. Methods: We analysed 16 tissue samples of Luminal A/B breast cancer types and 16 breast tissue samples without pathological findings. The microarray technology by Agilent was used to analyse 2549 miRNAs by SurePrint G3 Human miRNA kit v.21. The results were assessed by AgiMicroRNA Bioconductor library within Limma pack. Results: The analyses of the lowest FDR p-value and the highest logFC value selected the oncomiR miR-182 as the most dominant with higher expression in cancer tissues than in normal tissues, followed by miR-21, miR342-3p/5p and miR-6826. The miR-4324 and cluster of miR-99a/let7c/miR-125b dominated in the group of miRNAs with lower expression in cancer tissues compared to normal tissues. Conclusion: The first results of this study complement biological characteristics of luminal breast cancer subptypes, represent basis for follow-up projects focused on the clarification of relevant signaling pathways and promise new and innovative breast cancer treatment based on the precise, tailored therapy by targeting specific miRNAs involved in the most important carcinogenesis mechanisms.
- MeSH
- Clinical Studies as Topic MeSH
- Humans MeSH
- MicroRNAs analysis MeSH
- Microarray Analysis methods MeSH
- Breast Neoplasms * genetics MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Invasive ductal and lobular carcinomas (IDC and ILC) are the most common histological types of breast cancer. Clinical follow-up data and metastatic patterns suggest that the development and progression of these tumors are different. The aim of our study was to identify gene expression profiles of IDC and ILC in relation to normal breast epithelial cells. METHODS: We examined 30 samples (normal ductal and lobular cells from 10 patients, IDC cells from 5 patients, ILC cells from 5 patients) microdissected from cryosections of ten mastectomy specimens from postmenopausal patients. Fifty nanograms of total RNA were amplified and labeled by PCR and in vitro transcription. Samples were analysed upon Affymetrix U133 Plus 2.0 Arrays. The expression of seven differentially expressed genes (CDH1, EMP1, DDR1, DVL1, KRT5, KRT6, KRT17) was verified by immunohistochemistry on tissue microarrays. Expression of ASPN mRNA was validated by in situ hybridization on frozen sections, and CTHRC1, ASPN and COL3A1 were tested by PCR. RESULTS: Using GCOS pairwise comparison algorithm and rank products we have identified 84 named genes common to ILC versus normal cell types, 74 named genes common to IDC versus normal cell types, 78 named genes differentially expressed between normal ductal and lobular cells, and 28 named genes between IDC and ILC. Genes distinguishing between IDC and ILC are involved in epithelial-mesenchymal transition, TGF-beta and Wnt signaling. These changes were present in both tumor types but appeared to be more prominent in ILC. Immunohistochemistry for several novel markers (EMP1, DVL1, DDR1) distinguished large sets of IDC from ILC. CONCLUSION: IDC and ILC can be differentiated both at the gene and protein levels. In this study we report two candidate genes, asporin (ASPN) and collagen triple helix repeat containing 1 (CTHRC1) which might be significant in breast carcinogenesis. Besides E-cadherin, the proteins validated on tissue microarrays (EMP1, DVL1, DDR1) may represent novel immunohistochemical markers helpful in distinguishing between IDC and ILC. Further studies with larger sets of patients are needed to verify the gene expression profiles of various histological types of breast cancer in order to determine molecular subclassifications, prognosis and the optimum treatment strategies.
- MeSH
- Biomarkers MeSH
- Tissue Array Analysis methods MeSH
- Carcinoma, Ductal, Breast genetics pathology MeSH
- Extracellular Matrix Proteins genetics MeSH
- Financing, Organized MeSH
- In Situ Hybridization MeSH
- Immunohistochemistry MeSH
- Cadherins genetics MeSH
- Collagen Type III genetics MeSH
- Lasers MeSH
- Humans MeSH
- Carcinoma, Lobular genetics pathology MeSH
- Microdissection methods MeSH
- Breast Neoplasms genetics pathology MeSH
- Breast metabolism MeSH
- Check Tag
- Humans MeSH
- Female MeSH
Background: Microarray technologies are used to measure the simultaneous expression of a certain set of thousands of genes based on ribonucleic acid (RNA) obtained from a biological sample. We are interested in several statistical analyses such as 1) finding differentially expressed genes between or among several experimental groups, 2) finding a small number of genes allowing for the correct classification of a sample in a certain group, and 3) finding relations among genes. Objectives: Gene expression data are high dimensional, and this fact complicates their analysis because we are able to perform only a few samples (e.g. the peripheral blood from a limited number of patients) for a certain set of thousands of genes. The main purpose of this paper is to present the shrinkage estimator and show its application in different statistical analyses. Methods: The shrinkage approach relates to the shift of a certain value of a classic estimator towards a certain value of a specified target estimator. More precisely, the shrinkage estimator is the weighted average of the classic estimator and the target estimator. Results: The benefit of the shrinkage estimator is that it improves the mean squared error (MSE) as compared to a classic estimator. The MSE combines the measure of an estimator’s bias away from its true unknown value and the measure of the estimator’s variability. The shrinkage estimator is a biased estimator but has a lower variability. Conclusions: The shrinkage estimator can be considered as a promising estimator for analyzing high dimensional gene expression data.
- MeSH
- Gene Expression * genetics MeSH
- Humans MeSH
- Microarray Analysis * methods statistics & numerical data MeSH
- RNA * genetics MeSH
- Models, Statistical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
Sdělení podává informace o novém přístupu k biochemickým analýzám. Dále shrnuje principy, použití, možné výhody či nevýhody nových multiplexních technologií. Většina prezentovaných metod je v současné době buď ve fázi vývoje, nebo je již používána v rámci výzkumných projektů. Pouze některé technologie postupně pronikají do laboratoří klinické biochemie.
The publication reports recent approach to biochemical analyses. There are summarized data about principles, usages, advantages or disadvantages of new multiplex technologies. Although, in the meantime, the most of presented methods are in development or they are used in research projects some technologies already expand to clinical biochemistry laboratories.
