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Ståhl, Fredrik
Publications (10 of 60) Show all publications
Karlsson, S., Ståhl, F. & Larsson, D. (2013). Molecular diagnostic markers in endometrial carcinoma: an overview. Journal of Oncopathology, 1(2), 145-150
Open this publication in new window or tab >>Molecular diagnostic markers in endometrial carcinoma: an overview
2013 (English)In: Journal of Oncopathology, ISSN 2052-5931, Vol. 1, no 2, p. 145-150Article in journal (Refereed) Published
Abstract [en]

Endometrial, ovarian, and cervical cancers are three of the most common malignancies of the female reproductive organs and the most common cause of gynecological cancer deaths in the Western world. Approximately 80% or more of endometrial cancers are low-grade, estrogen-dependent, endometrioid adenocarcinoma (type I), whereas 20% are high-grade endometrial carcinomas (type II) associated with poor prognosis. Although endometrial cancer is usually diagnosed at an early stage, still almost 20% of the patients present with advanced disease. Thus, there is a need for highly sensitive markers that can distinguish between high- and low-risk endometrial carcinoma. To date, however, there are no validated molecular markers for endometrial cancer. Recent genomic and proteomic-based anaes show great promise for the discovery of new and more useful biomarkers. In this review, we will discuss the currently reported biomarkers that hold potential as diagnostic tools for endometrial cancer.

Place, publisher, year, edition, pages
Optimal Clinical Ltd., 2013
Keywords
endometrial carcinoma, Medicin
National Category
Cancer and Oncology
Research subject
Integrated Caring Science
Identifiers
urn:nbn:se:hb:diva-1619 (URN)2320/12696 (Local ID)2320/12696 (Archive number)2320/12696 (OAI)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-02-20Bibliographically approved
Larsson, D., Adele, J., Bergsten, N., Ståhl, F. & Karlsson, S. (2012). Membrane Initiated Effects of 1?,25-Dihydroxyvitamin D3 in Prostate Cancer Cells: Effects on AP1 and CREB Mediated Transcription. In: Stevo Najman (Ed.), Current Frontiers and Perspectives in Cell Biology: (pp. 153-162). InTech
Open this publication in new window or tab >>Membrane Initiated Effects of 1?,25-Dihydroxyvitamin D3 in Prostate Cancer Cells: Effects on AP1 and CREB Mediated Transcription
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2012 (English)In: Current Frontiers and Perspectives in Cell Biology / [ed] Stevo Najman, InTech , 2012, p. 153-162Chapter in book (Other academic)
Abstract [sv]

Vitamin D effekter på progression av cancer i prostata

Place, publisher, year, edition, pages
InTech, 2012
Keywords
prostatacancer, vitamin D, Medicinsk genetik
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:hb:diva-5122 (URN)2320/11900 (Local ID)978-953-51-0544-2 (ISBN)2320/11900 (Archive number)2320/11900 (OAI)
Available from: 2015-12-17 Created: 2015-12-17 Last updated: 2018-01-10
Andersson, L. & Ståhl, F. (2010). Distribution of candidate genes for experimentally induced arthritis in rats. BMC Genomics, 11(146)
Open this publication in new window or tab >>Distribution of candidate genes for experimentally induced arthritis in rats
2010 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 11, no 146Article in journal (Refereed) Published
Abstract [en]

Background: Rat models are frequently used to link genomic regions to experimentally induced arthritis in quantitative trait locus (QTL) analyses. To facilitate the search for candidate genes within such regions, we have previously developed an application (CGC) that uses weighted keywords to rank genes based on their descriptive text. In this study, CGC is used for analyzing the localization of candidate genes from two viewpoints: distribution over the rat genome and functional connections between arthritis QTLs. Methods: To investigate if candidate genes identified by CGC are more likely to be found inside QTLs, we ranked 2403 genes genome wide in rat. The number of genes within different ranges of CGC scores localized inside and outside QTLs was then calculated. Furthermore, we investigated if candidate genes within certain QTLs share similar functions, and if these functions could be connected to genes within other QTLs. Based on references between genes in OMIM, we created connections between genes in QTLs identified in two distinct rat crosses. In this way, QTL pairs with one QTL from each cross that share an unexpectedly high number of gene connections were identified. The genes that were found to connect a pair of QTLs were then functionally analysed using a publicly available classification tool. Results: Out of the 2403 genes ranked by the CGC application, 1160 were localized within QTL regions. No difference was observed between highly and lowly rated genes. Hence, highly rated candidate genes for arthritis seem to be distributed randomly inside and outside QTLs. Furthermore, we found five pairs of QTLs that shared a significantly high number of interconnected genes. When functionally analyzed, most genes connecting two QTLs could be included in a single functional cluster. Thus, the functional connections between these genes could very well be involved in the development of an arthritis phenotype. Conclusions: From the genome wide CGC search, we conclude that candidate genes for arthritis in rat are randomly distributed between QTL and non-QTL regions. We do however find certain pairs of QTLs that share a large number of functionally connected candidate genes, suggesting that these QTLs contain a number of genes involved in similar functions contributing to the arthritis phenotype.

Place, publisher, year, edition, pages
BioMed Central Ltd., 2010
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hb:diva-3069 (URN)10.1186/1471-2164-11-146 (DOI)2320/7611 (Local ID)2320/7611 (Archive number)2320/7611 (OAI)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-12-01Bibliographically approved
Andersson, L., Petersen, G. & Ståhl, F. (2009). Ranking candidate genes in rat models of type 2 diabetes. Theoretical Biology Medical Modelling, 6(12)
Open this publication in new window or tab >>Ranking candidate genes in rat models of type 2 diabetes
2009 (English)In: Theoretical Biology Medical Modelling, ISSN 1742-4682, E-ISSN 1742-4682, Vol. 6, no 12Article in journal (Refereed) Published
Abstract [en]

Background Rat models are frequently used to find genomic regions that contribute to complex diseases, so called quantitative trait loci (QTLs). In general, the genomic regions found to be associated with a quantitative trait are rather large, covering hundreds of genes. To help selecting appropriate candidate genes from QTLs associated with type 2 diabetes models in rat, we have developed a web tool called Candidate Gene Capture (CGC), specifically adopted for this disorder. Methods CGC combines diabetes-related genomic regions in rat with rat/human homology data, textual descriptions of gene effects and an array of 789 keywords. Each keyword is assigned values that reflect its co-occurrence with 24 different reference terms describing sub-phenotypes of type 2 diabetes (for example "insulin resistance"). The genes are then ranked based on the occurrences of keywords in the describing texts. Results CGC includes QTLs from type 2 diabetes models in rat. When comparing gene rankings from CGC based on one sub-phenotype, with manual gene ratings for four QTLs, very similar results were obtained. In total, 24 different sub-phenotypes are available as reference terms in the application and based on differences in gene ranking, they fall into separate clusters. Conclusion The very good agreement between the CGC gene ranking and the manual rating confirms that CGC is as a reliable tool for interpreting textual information. This, together with the possibility to select many different sub-phenotypes, makes CGC a versatile tool for finding candidate genes. CGC is publicly available at http://ratmap.org/CGC.

Place, publisher, year, edition, pages
BioMed Central Ltd., 2009
Keywords
rats, genomics, Genetics medicine
National Category
Medical Genetics
Identifiers
urn:nbn:se:hb:diva-2771 (URN)10.1186/1742-4682-6-12 (DOI)2320/6039 (Local ID)2320/6039 (Archive number)2320/6039 (OAI)
Note

Sponsorship:

Swedish Medical Research Council, the Nilsson-Ehle Foundation, the Sven and Lilly Lawski Foundation, the Erik Philip-Sorensen Foundation, the Wilhelm and Martina Lundgren Research Foundation, and the SWEGENE Foundation.

Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-01-10Bibliographically approved
Petersen, G. & Ståhl, F. (2008). RGST: Rat Gene Symbol Tracker, a database for defining official rat gene symbols. BMC Genomics, 9(29)
Open this publication in new window or tab >>RGST: Rat Gene Symbol Tracker, a database for defining official rat gene symbols
2008 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 9, no 29Article in journal (Refereed) Published
Abstract [en]

Background The names of genes are central in describing their function and relationship. However, gene symbols are often a subject of controversy. In addition, the discovery of mammalian genes is now so rapid that a proper use of gene symbol nomenclature rules tends to be overlooked. This is currently the situation in the rat and there is a need for a cohesive and unifying overview of all rat gene symbols in use. Based on the experiences in rat gene symbol curation that we have gained from running the "Ratmap" rat genome database, we have now developed a database that unifies different rat gene naming attempts with the accepted rat gene symbol nomenclature rules. Description This paper presents a newly developed database known as RGST (Rat Gene Symbol Tracker). The database contains rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI-Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity. Conclusion This database fulfils the important need of unifying rat gene symbols into an automatic and cohesive nomenclature system. The RGST database is available directly from the RatMap home page: http://ratmap.org.

Place, publisher, year, edition, pages
BioMed Central Ltd., 2008
Keywords
rats, genomics, Genetics
National Category
Medical Genetics Natural Sciences
Identifiers
urn:nbn:se:hb:diva-2772 (URN)10.1186/1471-2164-9-29 (DOI)2320/6040 (Local ID)2320/6040 (Archive number)2320/6040 (OAI)
Note

Sponsorship:

the Swedish Medical Research Council, the Nilsson-Ehle Foundation, the Sven and Lilly Lawski Foundation, the Erik Philip-Sorensen Foundation, the Wilhelm and Martina Lundgren Research Foundation, and the SWEGENE Foundation.

Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-01-10Bibliographically approved
Andersson, L. & Ståhl, F. (2007). Candidate Gene Capture (CGC): a Tool for Selecting Potentially. In: : . Paper presented at The 9th Functional Genomics Conference: Synthetic Biology, Göteborg,.
Open this publication in new window or tab >>Candidate Gene Capture (CGC): a Tool for Selecting Potentially
2007 (English)Conference paper, Poster (with or without abstract) (Other academic)
Keywords
Genetics
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hb:diva-5827 (URN)2320/3112 (Local ID)2320/3112 (Archive number)2320/3112 (OAI)
Conference
The 9th Functional Genomics Conference: Synthetic Biology, Göteborg,
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2016-11-18Bibliographically approved
Andersson, L., Petersen, G., Johnson, P. & Ståhl, F. (2005). A web tool for finding gene candidates associated with experimentally induced arthritis in rat. Arthritis Research & Therapy, 7(3), R485-R492
Open this publication in new window or tab >>A web tool for finding gene candidates associated with experimentally induced arthritis in rat
2005 (English)In: Arthritis Research & Therapy, ISSN 1478-6354, E-ISSN 1478-6362, Vol. 7, no 3, p. R485-R492Article in journal (Refereed) Published
Abstract [en]

Rat models are frequently used for finding genes contributing to the arthritis phenotype. In most studies, however, limitations in the number of animals result in a low resolution. As a result, the linkage between the autoimmune experimental arthritis phenotype and the genomic region, that is, the quantitative trait locus, can cover several hundred genes. The purpose of this work was to facilitate the search for candidate genes in such regions by introducing a web tool called Candidate Gene Capture (CGC) that takes advantage of free text data on gene function. The CGC tool was developed by combining genomic regions in the rat, associated with the autoimmune experimental arthritis phenotype, with rat/human gene homology data, and with descriptions of phenotypic gene effects and selected keywords. Each keyword was assigned a value, which was used for ranking genes based on their description of phenotypic gene effects. The application was implemented as a web-based tool and made public at http://ratmap.org/cgc. The CGC application ranks gene candidates for 37 rat genomic regions associated with autoimmune experimental arthritis phenotypes. To evaluate the CGC tool, the gene ranking in four regions was compared with an independent manual evaluation. In these sample tests, there was a full agreement between the manual ranking and the CGC ranking for the four highest-ranked genes in each test, except for one single gene. This indicates that the CGC tool creates a ranking very similar to that made by human inspection. The exceptional gene, which was ranked as a gene candidate by the CGC tool but not in the manual evaluation, was found to be closely associated with rheumatoid arthritis in additional literature studies. Genes ranked by the CGC tools as less likely gene candidates, as well as genes ranked low, were generally rated in a similar manner to those done manually. Thus, to find genes contributing to experimentally induced arthritis, we consider the CGC application to be a helpful tool in facilitating the evaluation of large amounts of textual information.

Place, publisher, year, edition, pages
BioMed Central Ltd., 2005
National Category
Mathematics
Identifiers
urn:nbn:se:hb:diva-3068 (URN)10.1186/ar1700 (DOI)2320/7610 (Local ID)2320/7610 (Archive number)2320/7610 (OAI)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-12-01Bibliographically approved
Petersen, G., Johnson, P., Andersson, L., Klinga-Levan, K., Gomez-Fabre, P. & Ståhl, F. (2005). RatMap: rat genome tools and data. Nucleic Acids Research - Database Issue, 33(1 (suppl)), 492-494
Open this publication in new window or tab >>RatMap: rat genome tools and data
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2005 (English)In: Nucleic Acids Research - Database Issue, ISSN 0305-1048, Vol. 33, no 1 (suppl), p. 492-494Article in journal (Refereed) Published
Abstract [en]

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB–Genetics at Göteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hb:diva-3062 (URN)10.1093/nar/gki125 (DOI)2320/7594 (Local ID)2320/7594 (Archive number)2320/7594 (OAI)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-02-13Bibliographically approved
Andersson, L., Petersen, G., Johnson, P. & Ståhl, F. (2004). Finding genes contributing to the arthirits phenotype by comparing rat and human genome data. Health Informatics Journal, 10(1), 71-75
Open this publication in new window or tab >>Finding genes contributing to the arthirits phenotype by comparing rat and human genome data
2004 (English)In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 10, no 1, p. 71-75Article in journal (Refereed) Published
Abstract [en]

Published quantitative trait locus (QTL) data, as well as all known rat genes and DNA markers, have since 1993 been collected and made easily accessible at the rat genome database, RatMap. The objective of the present study is to fully integrate available data concerning rat models with human genome information. The final goal of this process is to make results from any rat model experiment directly applicable to humans. The overall goal of this work is to create an automatic system which, for any given rat chromosomal region associated with a QTL, will characterize both mapped rat genes and all putative homologous human genes in that region. This article reports the use of the web application to find human gene candidates contributing to an arthritis phenotype.

Place, publisher, year, edition, pages
Sage Publications Ltd., 2004
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hb:diva-3061 (URN)10.1177/1460458204040670 (DOI)2320/7593 (Local ID)2320/7593 (Archive number)2320/7593 (OAI)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2017-11-05Bibliographically approved
Behboudi, A., Roshani, L., Kost-Alimova, M., Sjöstrand, E., Montelius-Alatalo, K., Röhme, D., . . . Ståhl, F. (2002). Detailed Chromosomal and Radiation Hybrid Mapping in the Proximal Part of the Rat Chromosome 10 and gene order Comparison with Mouse and Human. Mammalian Genome, 13(6), 302-309
Open this publication in new window or tab >>Detailed Chromosomal and Radiation Hybrid Mapping in the Proximal Part of the Rat Chromosome 10 and gene order Comparison with Mouse and Human
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2002 (English)In: Mammalian Genome, ISSN 0938-8990, E-ISSN 1432-1777, Vol. 13, no 6, p. 302-309Article in journal (Refereed) Published
Abstract [en]

The rat provides valuable and sometimes unique models of human complex diseases. To fully exploit the rat models in biomedical research, it is important to have access to detailed knowledge of the rat genome organization as well as its relation to the human genome. Rat Chromosome 10 (RNO10) harbors several important cancer-related genes. Deletions in the proximal part of RNO10 were repeatedly found in a rat model for endometrial cancer. To identify functional and positional candidate genes in the affected region, we used radiation hybrid (RH) mapping and single- and dual-color fluorescence in situ hybridization (FISH) techniques to construct a detailed chromosomal map of the proximal part of RNO10. The regional localization of 14 genes, most of them cancer-related (Grin2a, Gspt1, Crebbp, Gfer, Tsc2, Tpsb1, Il9r, Il4, Irf1, Csf2, Sparc, Tp53, Thra1, Gh1), and of five microsatellite markers (D10Mit10, D10Rat42, D10Rat50, D10Rat72, and D10Rat165) was determined on RNO10. For a fifteenth gene, Ppm1b, which had previously been assigned to RNO10, the map position was corrected to RNO6q12-q13.

Place, publisher, year, edition, pages
Springer New York LLC, 2002
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hb:diva-7506 (URN)10.1007/s00335-001-2153-4 (DOI)2320/7583 (Local ID)2320/7583 (Archive number)2320/7583 (OAI)
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-12-01Bibliographically approved

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