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Klinga-Levan, Karin
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Publications (10 of 29) Show all publications
Jurcevic, S., Klinga-Levan, K., Olsson, B. & Ejeskär, K. (2016). Verification of microRNA expression in human endometrial adenocarcinoma. BMC Cancer, 16(1), Article ID 261.
Open this publication in new window or tab >>Verification of microRNA expression in human endometrial adenocarcinoma
2016 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 16, no 1, article id 261Article in journal (Refereed) Published
Place, publisher, year, edition, pages
BioMed Central, 2016
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:his:diva-10890 (URN)10.1186/s12885-016-2296-z (DOI)000373329900001 ()27039384 (PubMedID)2-s2.0-84962003924 (Scopus ID)
Available from: 2015-05-05 Created: 2015-05-05 Last updated: 2017-12-04Bibliographically approved
Fagerlind, M., Stålhammar, H., Olsson, B. & Klinga-Levan, K. (2015). Expression of miRNAs in Bull Spermatozoa Correlates with Fertility Rates. Reproduction in domestic animals, 50(4), 587-594
Open this publication in new window or tab >>Expression of miRNAs in Bull Spermatozoa Correlates with Fertility Rates
2015 (English)In: Reproduction in domestic animals, ISSN 0936-6768, E-ISSN 1439-0531, Vol. 50, no 4, p. 587-594Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Blackwell Verlag, 2015
National Category
Bioinformatics and Systems Biology Biochemistry and Molecular Biology Genetics and Breeding
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-11552 (URN)10.1111/rda.12531 (DOI)000357976200009 ()2-s2.0-84936986482 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2015-09-24 Created: 2015-09-24 Last updated: 2017-11-27Bibliographically approved
Ulfenborg, B., Klinga-Levan, K. & Olsson, B. (2015). Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression. International Journal of Data Mining and Bioinformatics, 13(4), 338-359
Open this publication in new window or tab >>Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression
2015 (English)In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5681, Vol. 13, no 4, p. 338-359Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
InderScience Publishers, 2015
National Category
Bioinformatics and Systems Biology
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-11759 (URN)10.1504/IJDMB.2015.072755 (DOI)000366135400002 ()26547983 (PubMedID)2-s2.0-84946741012 (Scopus ID)
Available from: 2015-12-15 Created: 2015-12-15 Last updated: 2017-11-27Bibliographically approved
Ulfenborg, B., Jurcevic, S., Lindelöf, A., Klinga-Levan, K. & Olsson, B. (2015). miREC: a database of miRNAs involved in the development of endometrial cancer. BMC Research Notes, 8(1), Article ID 104.
Open this publication in new window or tab >>miREC: a database of miRNAs involved in the development of endometrial cancer
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2015 (English)In: BMC Research Notes, ISSN 1756-0500, E-ISSN 1756-0500, Vol. 8, no 1, article id 104Article in journal (Refereed) Published
Abstract [en]

Background

Endometrial 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.

Findings

The 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.

Conclusions

Several 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).

Place, publisher, year, edition, pages
BioMed Central, 2015
Keyword
Endometrial cancer, MicroRNA, Database
National Category
Cancer and Oncology
Research subject
Medical sciences
Identifiers
urn:nbn:se:his:diva-10891 (URN)10.1186/s13104-015-1052-9 (DOI)25889518 (PubMedID)2-s2.0-84940717539 (Scopus ID)
Available from: 2015-05-05 Created: 2015-05-05 Last updated: 2017-12-04Bibliographically approved
Ulfenborg, B., Klinga-Levan, K. & Olsson, B. (2014). GenoScan: Genomic Scanner for Putative miRNA Precursors. In: Mitra Basu, Yi Pan, Jianxin Wang (Ed.), Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014. Proceedings. Paper presented at 10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014 (pp. 266-277). Springer
Open this publication in new window or tab >>GenoScan: Genomic Scanner for Putative miRNA Precursors
2014 (English)In: Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014. Proceedings / [ed] Mitra Basu, Yi Pan, Jianxin Wang, Springer, 2014, p. 266-277Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2014
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8492
Keyword
miRNA discovery, machine learning, hairpin classification
National Category
Bioinformatics and Systems Biology
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-10450 (URN)10.1007/978-3-319-08171-7_24 (DOI)2-s2.0-84906337524 (Scopus ID)978-3-319-08170-0 (ISBN)978-3-319-08171-7 (ISBN)
Conference
10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014
Available from: 2014-12-18 Created: 2014-12-18 Last updated: 2017-11-27Bibliographically approved
Jurcevic, S., Olsson, B. & Klinga-Levan, K. (2014). MicroRNA expression in human endometrial adenocarcinoma. Cancer Cell International, 14(1), Article ID 88.
Open this publication in new window or tab >>MicroRNA expression in human endometrial adenocarcinoma
2014 (English)In: Cancer Cell International, ISSN 1475-2867, E-ISSN 1475-2867, Vol. 14, no 1, article id 88Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
London: BioMed Central, 2014
Keyword
Endometrial adenocarcinoma, MicroRNA, Quantitative polymerase chain reaction
National Category
Cancer and Oncology
Research subject
Medical sciences
Identifiers
urn:nbn:se:his:diva-10451 (URN)10.1186/s12935-014-0088-6 (DOI)000346199300001 ()25419182 (PubMedID)
Funder
Knowledge Foundation, 2009/091
Available from: 2014-12-18 Created: 2014-12-18 Last updated: 2017-12-05Bibliographically approved
Ulfenborg, B., Klinga-Levan, K. & Olsson, B. (2013). Classification of tumor samples from expression data using decision trunks. Cancer Informatics, 12, 53-66
Open this publication in new window or tab >>Classification of tumor samples from expression data using decision trunks
2013 (English)In: Cancer Informatics, ISSN 1176-9351, E-ISSN 1176-9351, Vol. 12, p. 53-66Article in journal (Refereed) Published
Abstract [en]

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks," since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2-3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices. © the author(s), publisher and licensee Libertas Academica Ltd.

Place, publisher, year, edition, pages
Libertas Academica Ltd., 2013
Keyword
Biomarkers, Classification, Gene expression, Machine learning, accuracy, article, classification algorithm, controlled study, decision making, decision tree, intermethod comparison, learning algorithm
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-8394 (URN)10.4137/CIN.S10356 (DOI)23467331 (PubMedID)2-s2.0-84874202131 (Scopus ID)
Available from: 2013-08-12 Created: 2013-08-12 Last updated: 2017-12-06
Carlsson, J., Helenius, G., Karlsson, M. G., Andrén, O., Klinga-Levan, K. & Olsson, B. (2013). Differences in microRNA expression during tumor development in the transition and peripheral zones of the prostate. BMC Cancer, 13, Article ID 362.
Open this publication in new window or tab >>Differences in microRNA expression during tumor development in the transition and peripheral zones of the prostate
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2013 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 13, article id 362Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
BioMed Central, 2013
Keyword
MiRNA expression, Prostate cancer, Prostate zones, microRNA, microRNA 127 3p, microrna 154, microRNA 15a, microRNA 15b, microRNA 181c, microRNA 216b, microRNA 22, microRNA 27b, microRNA 337 3p, microrna 379, microRNA 424, microRNA 433, microrna 494, microRNA 495, microRNA 543, phosphatidylinositol 3, 4, 5 trisphosphate 3 phosphatase, transforming growth factor beta, unclassified drug, adult, aged, article, cancer grading, cell cycle arrest, cell cycle regulation, central zone, clinical article, controlled study, gene expression, gene function, gene targeting, human, human tissue, male, pathogenesis, peripheral zone, prostate, tissue differentiation, transition zone
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-8710 (URN)10.1186/1471-2407-13-362 (DOI)000322598200001 ()23890084 (PubMedID)2-s2.0-84880941948 (Scopus ID)
Available from: 2014-01-02 Created: 2014-01-02 Last updated: 2017-12-06Bibliographically approved
Falck, E. & Klinga-Levan, K. (2013). Expression patterns of PHF5A/Phf5a and GJa1/Gja1 in rat and human endometrial cancer. Cancer Cell International, 13(1), Article ID 43.
Open this publication in new window or tab >>Expression patterns of PHF5A/Phf5a and GJa1/Gja1 in rat and human endometrial cancer
2013 (English)In: Cancer Cell International, ISSN 1475-2867, E-ISSN 1475-2867, Vol. 13, no 1, article id 43Article in journal (Refereed) Published
Abstract [en]

Endometrial adenocarcinoma is the most frequently diagnosed cancer of the female genital tract in the western world. Studies of complex diseases can be difficult to perform on human tumor samples due to the high genetic heterogeneity in human. The use of rat models is preferable since rat has similarities in pathogenesis and histopathological properties to that of human.

A genomic region including the highly conserved Phf5a gene associated to development of EAC has previously been identified in an association study. PHF5A has been suggested to acts as a transcription factor or cofactor in the up regulation of expression of Gja1 gene in the presence of estrogen. It has earlier been shown that the Phf5a gene is down regulated in rat EAC derived cell lines by means of expression microarrays.

We analyzed the expression of Phf5a and Gja1 by qPCR, and potential relations between the two genes in EAC tumors and non-malignant cell lines derived from the BDII rat model. In addition, the expression pattern of these genes was compared in rat and human EAC tumor samples.

Changes in expression for Phf5a/PHF5A were found in tumors from both rat and human even though the observed pattern was not completely consistent between the two species. By separating rat EAC cell lines according to the genetic background, a significant lower expression of Phf5a in one of the two cross backgrounds was revealed, but not for the other. In contrast to other studies, Phf5a/PHF5A regulation of Gja1/GJA1 was not revealed in this study.

Place, publisher, year, edition, pages
BioMed Central, 2013
Keyword
BDII, Endometrial cancer, Genetic background, Phf5a, Gja1
National Category
Medical and Health Sciences
Research subject
Medical sciences
Identifiers
urn:nbn:se:his:diva-7420 (URN)10.1186/1475-2867-13-43 (DOI)000319389700001 ()23675859 (PubMedID)2-s2.0-84878075429 (Scopus ID)
Available from: 2013-03-18 Created: 2013-03-18 Last updated: 2017-12-06Bibliographically approved
Jurcevic, S., Olsson, B. & Klinga-Levan, K. (2013). Validation of Suitable Endogenous Control Genes for Quantitative PCR Analysis of microRNA gene expression in a rat model of endometrial cancer. Cancer Cell International, 13, Article ID 45.
Open this publication in new window or tab >>Validation of Suitable Endogenous Control Genes for Quantitative PCR Analysis of microRNA gene expression in a rat model of endometrial cancer
2013 (English)In: Cancer Cell International, ISSN 1475-2867, E-ISSN 1475-2867, Vol. 13, article id 45Article in journal (Refereed) Published
Abstract [en]

Background

MicroRNAs are small RNA molecules that negatively regulate gene expression by translational inhibition or mRNA cleavage. The discovery that abnormal expression of particular miRNAs contributes to human disease, including cancer, has spurred growing interest in analysing expression profiles of these molecules. Quantitative polymerase chain reaction is frequently used for quantification of miRNA expression due to its sensitivity and specificity. To minimize experimental error in this system an appropriate endogenous control gene must be chosen. An ideal endogenous control gene should be expressed at a constant level across all samples and its expression stability should be unaffected by the experimental procedure.

Results

The expression and validation of candidate control genes (4.5S RNA(H) A, Y1, 4.5S RNA(H) B, snoRNA, U87 and U6) was examined in 21 rat cell lines to establish the most suitable endogenous control for miRNA analysis in a rat model of cancer. The stability of these genes was analysed using geNorm and NormFinder algorithms. U87 and snoRNA were identified as the most stable control genes, while Y1 was least stable.

Conclusion

This study identified the control gene that is most suitable for normalizing the miRNA expression data in rat. That reference gene will be useful when miRNAs expression are analyzed in order to find new miRNA markers for endometrial cancer in rat.

Place, publisher, year, edition, pages
BioMed Central, 2013
Keyword
Endogenous control genes, microRNA, Endometrial cancer
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
urn:nbn:se:his:diva-8384 (URN)10.1186/1475-2867-13-45 (DOI)000319504800001 ()23680393 (PubMedID)2-s2.0-84878248686 (Scopus ID)
Available from: 2013-08-12 Created: 2013-08-09 Last updated: 2017-12-06Bibliographically approved
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