A mikro-RNS-ek szerepe az agyalapimirigy-daganatok tumorbiológiájában

Abstract: MicroRNAs (miRNAs) are short, single stranded RNA molecules which play regulatory roles through posttranscriptional regulation of their target genes. Based on our current knowledge, more than 30% of the human protein-coding genes are regulated by miRNAs, hence influencing basic cellular mechanisms including cell proliferation, differentiation and cell death. Differential miRNA expression pattern has been detected in many different types of tumors and, recently, several publications have referred to miRNAs as potential therapeutic targets. Through adjustment of miRNA levels by artificial miRNAs administration or miRNA inhibition, we can influence not only one target gene but also complex biological pathways. Pituitary adenoma is the second most frequent intracranial tumor. In spite of this, the molecular mechanism of the pituitary adenoma formation is not yet entirely revealed. Recently, more and more evidences have been found suggesting that miRNAs have an important role in pituitary adenoma pathogenesis. Here, we summarize the recent results related to this role and highlight the therapeutic potentials in pituitary adenomas. Orv Hetil. 2018; 159(7): 252?259

Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression.

BACKGROUND: Previously, drug-based synchronization procedures were used for characterizing the cell cycle dependent transcriptional program. However, these synchronization methods result in growth imbalance and alteration of the cell cycle machinery. DNA content-based fluorescence activated cell sorting (FACS) is able to sort the different cell cycle phases without perturbing the cell cycle. MiRNAs are key transcriptional regulators of the cell cycle, however, their expression dynamics during cell cycle has not been explored. METHODS: Following an optimized FACS, a complex initiative of high throughput platforms (microarray, Taqman Low Density Array, small RNA sequencing) were performed to study gene and miRNA expression profiles of cell cycle sorted human cells originating from different tissues. Validation of high throughput data was performed using quantitative real time PCR. Protein expression was detected by Western blot. Complex statistics and pathway analysis were also applied. RESULTS: Beyond confirming the previously described cell cycle transcriptional program, cell cycle dependently expressed genes showed a higher expression independently from the cell cycle phase and a lower amplitude of dynamic changes in cancer cells as compared to untransformed fibroblasts. Contrary to mRNA changes, miRNA expression was stable throughout the cell cycle. CONCLUSIONS: Cell cycle sorting is a synchronization-free method for the proper analysis of cell cycle dynamics. Altered dynamic expression of universal cell cycle genes in cancer cells reflects the transformed cell cycle machinery. Stable miRNA expression during cell cycle progression may suggest that dynamical miRNA-dependent regulation may be of less importance in short term regulations during the cell cycle

Limitations of high throughput methods for miRNA expression profiles in non-functioning pituitary adenomas

Microarray, RT-qPCR based arrays and next-generation-sequencing (NGS) are available high-throughput methods for miRNA profiling (miRNome). Analytical and biological performance of these methods were tested in identification of biologically relevant miRNAs in non-functioning pituitary adenomas (NFPA). miRNome of 4 normal pituitary (NP) and 8 NFPA samples was determined by these platforms and expression of 21 individual miRNAs was measured on 30 (20 NFPA and 10 NP) independent samples. Complex bioinformatics was used. 132 and 137 miRNAs were detected by all three platforms in NP and NFPA, respectively, of which 25 were differentially expressed (fold change > 2). The strongest correlation was observed between microarray and TaqMan-array, while the data obtained by NGS were the most discordant despite of various bioinformatics settings. As a technical validation we measured the expression of 21 selected miRNAs by individual RT-qPCR and we were able to validate 35.1%, 76.2% and 71.4% of the miRNAs revealed by SOLiD, TLDA and microarray result, respectively. We performed biological validation using an extended number of samples (20 NFPAs and 8 NPs). Technical and biological validation showed high correlation (p < 0.001; R = 0.96). Pathway and network analysis revealed several common pathways but no pathway showed the same activation score. Using the 25 platform-independent miRNAs developmental pathways were the top functional categories relevant for NFPA genesis. The difference among high-throughput platforms is of great importance and selection of screening method can influence experimental results. Validation by another platform is essential in order to avoid or to minimalize the platform specific errors

MicroRNA expression profiling in adrenal myelolipoma

Introduction: Adrenal myelolipoma (AML) is the second most common, and invariably benign primary adrenal neoplasm. Due to the variable proportion of fat and hematopoietic elements, and its often large size, it can cause differential diagnostic problems. Several reports confirmed the utility of microRNAs (miRNAs) in the diagnosis of tumors, but the miRNA expression in AML has not yet been investigated. Materials and methods: Next-generation sequencing (NGS) was performed on 30 formalin-fixed paraffin-embedded (FFPE) archived tissue [AML, adrenocortical adenoma (ACA) and adrenocortical carcinoma (ACC) 10 each] samples. Validation was performed by real-time RT-qPCR on a cohort containing 41 further FFPE samples (15 AML, 14 ACA and 12 ACC). Circulating miRNA counterparts of significantly differentially expressed tissue miRNAs were studied in altogether 33 plasma samples (ACA, ACC, AML 11 each). Results: By NGS, 256 significantly differentially expressed miRNAs were discovered, and 8 of these were chosen for validation. Significant overexpression of hsa-miR-451a, hsa-miR-486-5p, hsa-miR-363-3p and hsa-miR-150-5p was confirmed in AML relative to ACA and ACC. Hsa-miR-184, hsa-miR-483-5p and hsa-miR-183-5p were significantly overexpressed in ACC relative to ACA, but not to AML. Circulating hsa-miR-451a and hsa-miR-363-3p were significantly overexpressed in AML, whereas circulating hsa-miR-483-5p and hsa-miR-483-3p were only significantly overexpressed in ACC vs. ACA. Conclusions: We have found significantly differentially expressed miRNAs in AML and adrenocortical tumors. Circulating hsa-miR-451a might be a promising minimally invasive biomarker of AML. The lack of significantly different expression of hsa-miR-483-3p and hsa-miR-483-5p between AML and ACC might limit their applicability as diagnostic miRNA markers for ACC

Additional file 1: Table S1. of Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression

Characterization of cell cycle sorted cells and isolated RNA quantity in various cell types. Purity of cell cycle sort was determined by re-analyzing the sorted populations by FACS analysis: percentage was determined by the portion of cells residing in the gate previously designated for a certain cell cycle phase. Data of four replicate experiments. Data are given as × 1000 cells (sorted cells) and are shown as mean ± standard deviation. Table S2. Name and details of primers used for mRNA and miRNA expression qRT-PCR measurements. mRNA primers (Panel A, cat. No: 4331182) and miRNA primers (Panel B, cat. No: 4427975). All primers were from Applied Biosystems by Life Technologies. Table S3. Normalized expression of differently expressed genes between cell cycle phases in various cell types. Normalized expression of genes with fold change > 2 between cell cycle phases detected in HDFa cells (Panel A). Normalized expression of significantly differently expressed genes in cell cycle phases detected in NCI-H295R (Panel B) and HeLa (Panel C) cells. Note: For Panel B and C, genes are listed in the manner as shown in the heat map (Fig. 2, panel A and B, respectively). Table S4. List of genes shown on Venn diagram (Fig. 3, panel a). Genes are marked with “1” if being found cycling by either method (HDFa SORT, PF synchr, HeLa SORT, HeLa synchr). Gene IDs are Gene Symbols, if available or probe IDs. Table S5. List of HeLa cell cycle genes being present in enriched GO terms. Table 1 presents GO terms which are enriched in gene lists unique to HeLa SORT, HeLa synchronization experiments and the overlap beween HeLa SORT and synchronization lists. Gene symbols of genes being present in the gene lists and the enriched GO terms are shown here (HeLa SORT \ HeLa synchr - Panel A; HeLa synchr \ HeLa SORT – Panel B; HeLa SORT ∩ HeLa synchr – Panel C). (XLS 743 kb