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- Jurcevic, Sanja, et al.
(författare)
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Verification of microRNA expression in human endometrial adenocarcinoma
- 2016
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Ingår i: BMC Cancer. - : BioMed Central (BMC). - 1471-2407. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: MicroRNAs are small non-coding RNAs that have been implicated in tumor initiation and progression. In a previous study we identified 138 miRNAs as differentially expressed in endometrial adenocarcinoma compared to normal tissues. One of these miRNAs was miRNA-34a, which regulates several genes involved in the Notch pathway, which is frequently altered in endometrial cancer. The aims of this study were to verify the differential expression of a subset of miRNAs and to scrutinize the regulatory role of mir-34a on the target genes NOTCH1 and DLL1. Methods: Twenty-five miRNAs that were previously identified as differentially expressed were subjected to further analysis using qPCR. To investigate the regulation of NOTCH1 and DLL1 by mir-34a, we designed gain- and loss-of-function experiments in Ishikawa and HEK293 cell lines by transfection with a synthetic mir-34a mimic and a mir-34a inhibitor. Results: Of the 25 validated miRNAs, seven were down-regulated and 18 were up-regulated compared to normal endometrium, which was fully consistent with our previous findings. In addition, the up-regulation of mir-34a led to a significant decrease in mRNA levels of NOTCH1 and DLL1, while down-regulation led to a significant increase in mRNA levels of these two genes. Conclusions: We verified both up-regulated and down-regulated miRNAs in the tumor samples, indicating various roles of microRNAs during tumor development. Mir-34a functions as a regulator by decreasing the expression of NOTCH1 and DLL1. Our study is the first to identify a correlation between mir-34a and its target genes NOTCH1 and DLL1 in endometrial adenocarcinoma.
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| 2. |
- Ulfenborg, Benjamin, 1985-
(författare)
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Bioinformatics tools for discovery and evaluation of biomarkers : Applications in clinical assessment of cancer
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. A biomarker is a biological molecule found in tissues or body fluids and can be used to predict or assess disease states. The aim of this thesis is to develop bioinformatics tools for discovery and evaluation of novel biomarkers from high-throughput datasets.MicroRNAs (miRNAs) are short non-coding RNAs that function as negative regulators of gene expression. Dysregulation of miRNAs in cancer is frequently reported, making them interesting as biomarker candidates. GenoScan was developed for genome-wide discovery of miRNA-coding genes, as a first step in the identification of novel mi-RNA biomarkers.High-throughput technologies such as microarrays allow researchers to measure the expression of thousands of genes or miRNAs simultaneously. The Decision Trunk Classifier (DTC) algorithm has been developed to screen datasets from these experiments for biomarker candidates. When applied to a miRNA expression dataset for endometrial cancer (EC) samples vs. controls, a two-marker model with 98 % accuracy was generated. These miRNAs (hsa-miR-183-5p and hsa-miRPlus-C1070) are promising as biomarkers for EC screening.The miREC database was developed to store gene and miRNA data from curated expression profiling studies of EC, as well as gene-miRNA regulatory connections. Using gene-miRNA interaction networks from miREC, the roles of miRNAs in cancer hallmark acquisition can be clarified. To further support exploratory analysis of expression data, DTC was extended with partial least squares regression models. The resulting PLS-DTC algorithm can be used to gain deeper insights into the perturbation of biological processes and pathways.
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| 5. |
- Ulfenborg, Benjamin, et al.
(författare)
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Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
- 2015
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Ingår i: International Journal of Data Mining and Bioinformatics. - Geneva, Switzerland : InderScience Publishers. - 1748-5681 .- 1748-5673. ; 13:4, s. 338-359
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Tidskriftsartikel (refereegranskat)abstract
- In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensemble-based and one using logistic regression were benchmarked along with 15 published methods. In addition, the sequence-folding step is addressed by investigating the impact of secondary structure prediction methods and the choice of input sequence length on prediction performance. Both the accuracy of secondary structure predictions and the miRNA prediction are evaluated. In the benchmark of hairpin classification methods, the regression model achieved highest classification accuracy. Of the structure prediction methods evaluated, ContextFold achieved the highest agreement between predicted and experimentally determined structures. However, both the choice of secondary structure prediction method and input sequence length had limited impact on hairpin classification performance.
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| 6. |
- Ulfenborg, Benjamin, et al.
(författare)
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miREC : a database of miRNAs involved in the development of endometrial cancer
- 2015
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Ingår i: BMC Research Notes. - : BioMed Central. - 1756-0500. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundEndometrial cancer (EC) is the most frequently diagnosed gynecological malignancy and the fourth most common cancer diagnosis overall among women. As with many other forms of cancer, it has been shown that certain miRNAs are differentially expressed in EC and these miRNAs are believed to play important roles as regulators of processes involved in the development of the disease. With the rapidly growing number of studies of miRNA expression in EC, there is a need to organize the data, combine the findings from experimental studies of EC with information from various miRNA databases, and make the integrated information easily accessible for the EC research community.FindingsThe miREC database is an organized collection of data and information about miRNAs shown to be differentially expressed in EC. The database can be used to map connections between miRNAs and their target genes in order to identify specific miRNAs that are potentially important for the development of EC. The aim of the miREC database is to integrate all available information about miRNAs and target genes involved in the development of endometrial cancer, and to provide a comprehensive, up-to-date, and easily accessible source of knowledge regarding the role of miRNAs in the development of EC. Database URL: http://www.mirecdb.orgwebcite.ConclusionsSeveral databases have been published that store information about all miRNA targets that have been predicted or experimentally verified to date. It would be a time-consuming task to navigate between these different data sources and literature to gather information about a specific disease, such as endometrial cancer. The miREC database is a specialized data repository that, in addition to miRNA target information, keeps track of the differential expression of genes and miRNAs potentially involved in endometrial cancer development. By providing flexible search functions it becomes easy to search for EC-associated genes and miRNAs from different starting points, such as differential expression and genomic loci (based on genomic aberrations).
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| 7. |
- Jurcevic, Sanja, 1971-, et al.
(författare)
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MicroRNA expression in human endometrial adenocarcinoma
- 2014
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Ingår i: Cancer Cell International. - London : BioMed Central (BMC). - 1475-2867. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: MicroRNAs are small non-coding RNAs that play crucial roles in the pathogenesis of different cancer types. The aim of this study was to identify miRNAs that are differentially expressed in endometrial adenocarcinoma compared to healthy endometrium. These miRNAs can potentially be used to develop a panel for classification and prognosis in order to better predict the progression of the disease and facilitate the choice of treatment strategy.METHODS: Formalin fixed paraffin embedded endometrial tissue samples were collected from the Örebro university hospital. QPCR was used to quantify the expression levels of 742 miRNAs in 30 malignant and 20 normal endometrium samples. After normalization of the qPCR data, miRNAs differing significantly in expression between normal and cancer samples were identified, and hierarchical clustering analysis was used to identify groups of miRNAs with coordinated expression profiles.RESULTS: In comparisons between endometrial adenocarcinoma and normal endometrium samples 138 miRNAs were found to be significantly differentially expressed (p < 0.001) among which 112 miRNAs have not been previous reported for endometrial adenocarcinoma.CONCLUSION: Our study shows that several miRNAs are differentially expressed in endometrial adenocarcinoma. These identified miRNA hold great potential as target for classification and prognosis of this disease. Further analysis of the differentially expressed miRNA and their target genes will help to derive new biomarkers that can be used for classification and prognosis of endometrial adenocarcinoma.
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| 9. |
- Ulfenborg, Benjamin, et al.
(författare)
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GenoScan : Genomic Scanner for Putative miRNA Precursors
- 2014
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Ingår i: Bioinformatics Research and Applications. - Cham : Springer International Publishing Switzerland. - 9783319081700 - 9783319081717 ; , s. 266-277
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Konferensbidrag (refereegranskat)abstract
- The significance of miRNAs has been clarified over the last decade as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes. Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or nonmiRNAs. In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.
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| 10. |
- Carlsson, Jessica, 1984-, et al.
(författare)
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Differences in microRNA expression during tumor development in the transition and peripheral zones of the prostate
- 2013
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Ingår i: BMC Cancer. - London, United Kingdom : BioMed Central (BMC). - 1471-2407. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: The prostate is divided into three glandular zones, the peripheral zone (PZ), the transition zone (TZ), and the central zone. Most prostate tumors arise in the peripheral zone (70-75%) and in the transition zone (20-25%) while only 10% arise in the central zone. The aim of this study was to investigate if differences in miRNA expression could be a possible explanation for the difference in propensity of tumors in the zones of the prostate. Methods: Patients with prostate cancer were included in the study if they had a tumor with Gleason grade 3 in the PZ, the TZ, or both (n=16). Normal prostate tissue was collected from men undergoing cystoprostatectomy (n=20). The expression of 667 unique miRNAs was investigated using TaqMan low density arrays for miRNAs. Student's t-test was used in order to identify differentially expressed miRNAs, followed by hierarchical clustering and principal component analysis (PCA) to study the separation of the tissues. The ADtree algorithm was used to identify markers for classification of tissues and a cross-validation procedure was used to test the generality of the identified miRNA-based classifiers. Results: The t-tests revealed that the major differences in miRNA expression are found between normal and malignant tissues. Hierarchical clustering and PCA based on differentially expressed miRNAs between normal and malignant tissues showed perfect separation between samples, while the corresponding analyses based on differentially expressed miRNAs between the two zones showed several misplaced samples. A classification and cross-validation procedure confirmed these results and several potential miRNA markers were identified. Conclusions: The results of this study indicate that the major differences in the transcription program are those arising during tumor development, rather than during normal tissue development. In addition, tumors arising in the TZ have more unique differentially expressed miRNAs compared to the PZ. The results also indicate that separate miRNA expression signatures for diagnosis might be needed for tumors arising in the different zones. MicroRNA signatures that are specific for PZ and TZ tumors could also lead to more accurate prognoses, since tumors arising in the PZ tend to be more aggressive than tumors arising in the TZ.
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