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Published on: October 31, 2016
Discovery of DNA methylation markers in cervical cancer using relaxation ranking
Maté Ongenaert1, G Bea A Wisman, Haukeline H Volders
1Laboratory for Bioinformatics and Computational Genomics (BioBix), Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Belgium. mate.ongenaert@ugent.be
Background:
To discover cancer specific DNA methylation markers, large-scale screening methods are widely used. The pharmacological unmasking expression microarray approach is an elegant method to enrich for genes that are silenced and re-expressed during functional reversal of DNA methylation upon treatment with demethylation agents. However, such experiments are performed in in vitro (cancer) cell lines, mostly with poor relevance when extrapolating to primary cancers. To overcome this problem, we incorporated data from primary cancer samples in the experimental design. A strategy to combine and rank data from these different data sources is essential to minimize the experimental work in the validation steps.
Aim:
To apply a new relaxation ranking algorithm to enrich DNA methylation markers in cervical cancer.
Results:
The application of a new sorting methodology allowed us to sort high-throughput microarray data from both cervical cancer cell lines and primary cervical cancer samples. The performance of the sorting was analyzed in silico. Pathway and gene ontology analysis was performed on the top-selection and gives a strong indication that the ranking methodology is able to enrich towards genes that might be methylated. Terms like regulation of progression through cell cycle, positive regulation of programmed cell death as well as organ development and embryonic development are overrepresented. Combined with the highly enriched number of imprinted and X-chromosome located genes, and increased prevalence of known methylation markers selected from cervical (the highest-ranking known gene is CCNA1) as well as from other cancer types, the use of the ranking algorithm seems to be powerful in enriching towards methylated genes.Verification of the DNA methylation state of the 10 highest-ranking genes revealed that 7/9 (78%) gene promoters showed DNA methylation in cervical carcinomas. Of these 7 genes, 3 (SST, HTRA3 and NPTX1) are not methylated in normal cervix tissue.
Conclusion:
The application of this new relaxation ranking methodology allowed us to significantly enrich towards methylation genes in cancer. This enrichment is both shown in silico and by experimental validation, and revealed novel methylation markers as proof-of-concept that might be useful in early cancer detection in cervical scrapings.
Insights
A new ranking algorithm effectively identifies DNA methylation markers for cervical cancer by analyzing cell line and primary sample data. This method enriches for methylated genes, revealing potential biomarkers for early cancer detection.
Area of Science:
- * Molecular Biology
- * Cancer Genomics
- * Bioinformatics
Background:
- * Large-scale screening for cancer-specific DNA methylation markers is crucial.
- * Pharmacological unmasking expression microarray enriches for silenced genes but often lacks relevance to primary cancers.
- * Integrating primary cancer sample data with cell line data is essential for robust marker discovery.
Purpose of the Study:
- * To develop and apply a novel relaxation ranking algorithm for identifying DNA methylation markers in cervical cancer.
- * To improve the enrichment of candidate methylation markers by combining in vitro and in vivo data.
- * To validate the algorithm's efficacy in identifying biologically relevant methylation markers.
Main Methods:
- * Applied a new relaxation ranking algorithm to sort high-throughput microarray data from cervical cancer cell lines and primary samples.
- * Performed in silico analysis to assess the sorting methodology's performance.
- * Conducted pathway and gene ontology analysis on top-ranked genes.
- * Experimentally verified the DNA methylation status of top-ranked candidate genes in cervical carcinomas and normal tissues.
Main Results:
- * The ranking algorithm successfully sorted microarray data from both cell lines and primary cervical cancer samples.
- * Pathway analysis indicated enrichment towards genes involved in cell cycle regulation, programmed cell death, and development.
- * Verification revealed that 78% of the top 10 ranked gene promoters were methylated in cervical carcinomas, with 3 novel markers identified.
- * Known methylation markers, including CCNA1, were highly ranked, supporting the algorithm's power.
Conclusions:
- * The novel relaxation ranking methodology significantly enriches for cancer-associated methylation markers.
- * The approach demonstrates efficacy both in silico and through experimental validation.
- * Identified novel methylation markers show promise for early detection of cervical cancer.
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