| Title |
Folate and choline metabolism gene variants in relation to ovarian cancer risk in the Polish population
|
|---|---|
| Published in |
Molecular Biology Reports, December 2011
|
| DOI | 10.1007/s11033-011-1359-0 |
| Pubmed ID | |
| Authors |
Piotr Pawlik, Adrianna Mostowska, Margarita Lianeri, Stefan Sajdak, Helena Kędzia, Paweł P. Jagodzinski |
| Abstract |
Data indicates that genetic factors alone do not account for ovarian tumorigenesis, suggesting that epigenetic status additionally affects this process. Therefore, we assessed the possible contribution of polymorphic variants of genes that may affect DNA methylation to the risk of ovarian cancer incidence in the Polish population. Using PCR-RFLP and HRM analyses, we studied the distribution of BHMT (rs3733890), MTHFD1 (rs2236225), MTHFR (rs1801133), MTR (rs1805087), MTRR (rs1801394) and TCN2 (rs1801198) genotypes and alleles in patients with ovarian cancer (n = 136) and controls (n = 160). Moreover, using DNA and methylation-specific PCR (MSP) we also determined the methylation of the Cadherin 13 (CDH13) promoter in cancerous tissue from these patients. We did not observe a significant association between all studied gene variants and the incidence of ovarian cancer. The lowest P (trend) = 0.1226 was observed for the MTHFR Ala222Val polymorphism. Moreover, the lowest P = 0.0772 was found in the comparison of MTHFR Ala/Ala versus Val/Val and Val/Ala genotypes in patients and control groups. The multifactor dimensionality reduction analysis also did not indicate a significant interactive genetic effect on ovarian cancer incidence for all analyzed SNPs. However, we observed frequent methylation of the CDH13 promoter in approximately 21% (29/136) patients with ovarian carcinomas. Our results might suggest that the selected polymorphic gene variants may not contribute to ovarian cancer incidence. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 32% |
| Student > Master | 4 | 21% |
| Researcher | 3 | 16% |
| Student > Bachelor | 1 | 5% |
| Librarian | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 37% |
| Biochemistry, Genetics and Molecular Biology | 5 | 26% |
| Medicine and Dentistry | 2 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 5% |
| Chemistry | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 3 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 December 2011.
All research outputs
#18,303,139
of 22,660,862 outputs
Outputs from Molecular Biology Reports
#1,593
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Outputs of similar age
#195,540
of 243,104 outputs
Outputs of similar age from Molecular Biology Reports
#26
of 41 outputs
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