Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Aug 30;14(1):249.
doi: 10.1186/s12967-016-1014-6.

Methylation pattern analysis in prostate cancer tissue: identification of biomarkers using an MS-MLPA approach

Giorgia Gurioli  1 , Samanta Salvi  1 , Filippo Martignano  1 , Flavia Foca  2 , Roberta Gunelli  3 , Matteo Costantini  4 , Giacomo Cicchetti  5 , Ugo De Giorgi  6 , Persio Dello Sbarba  7 , Daniele Calistri  1 , Valentina Casadio  8
Affiliations

Methylation pattern analysis in prostate cancer tissue: identification of biomarkers using an MS-MLPA approach

Giorgia Gurioli et al. J Transl Med. .

Abstract

Background: Epigenetic silencing mediated by CpG island methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with prostate carcinogenesis could potentially identify a tumour-specific methylation pattern, facilitating the early diagnosis of prostate cancer. The objective of the study was to assess the methylation status of 40 tumour suppressor genes in prostate cancer and healthy prostatic tissues.

Methods: We used methylation specific-multiplex ligation probe amplification (MS-MLPA) assay in two independent case series (training and validation set). The training set comprised samples of prostate cancer tissue (n = 40), healthy prostatic tissue adjacent to the tumor (n = 26), and healthy non prostatic tissue (n = 23), for a total of 89 DNA samples; the validation set was composed of 40 prostate cancer tissue samples and their adjacent healthy prostatic tissue, for a total of 80 DNA samples. Methylation specific-polymerase chain reaction (MSP) was used to confirm the results obtained in the validation set.

Results: We identified five highly methylated genes in prostate cancer: GSTP1, RARB, RASSF1, SCGB3A1, CCND2 (P < 0.0001), with an area under the ROC curve varying between 0.89 (95 % CI 0.82-0.97) and 0.95 (95 % CI 0.90-1.00). Diagnostic accuracy ranged from 80 % (95 % CI 70-88) to 90 % (95 % CI 81-96). Moreover, a concordance rate ranging from 83 % (95 % CI 72-90) to 89 % (95 % CI 80-95) was observed between MS-MLPA and MSP.

Conclusions: Our preliminary results highlighted that hypermethylation of GSTP1, RARB, RASSF1, SCGB3A1 and CCND2 was highly tumour-specific in prostate cancer tissue.

Keywords: Early diagnosis; MS-MLPA; Methylation pattern; Prostate cancer.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Correlation between different percentages of methylated DNA input (LNCaP cell line, X axis) and methylation percentage results obtained using the MS-MLPA technique (Y axis). Results for CCND2, RUNX3, SCGB3A1, RARB, APC, CASP8, CD44, RASSF1 and GSTP1 are reported with corresponding R2 results
Fig. 2
Fig. 2
Hierarchical cluster analysis of methylation status of 40 tumour suppressor genes (training set): the blue colour indicates an absence of methylation in the genes, whereas red indicates high methylation; shades of colour indicate intermediate methylation status. The 40 genes are shown along the bottom, while the samples are represented in the column on the right
Fig. 3
Fig. 3
Hierarchical cluster analysis of methylation status of 40 tumour suppressor genes (validation set): the blue colour indicates an absence of methylation in the genes, whereas red indicates high methylation; shades of colour indicate intermediate methylation status. The 40 genes are shown along the bottom, while the samples are represented in the column on the right
Fig. 4
Fig. 4
ROC curve analysis of the five genes highly specific in discriminating prostate cancer from healthy tissue: a GSTP1, b RARB, c RASSF1, d SCGB3A1, and e CCND2
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Tao ZQ, Shi AM, Wang KX, Zhang WD. Epidemiology of prostate cancer: current status. Eur Rev Med Pharmacol Sci. 2015;19:805–812. - PubMed
    1. Heijnsdijk EA, der Kinderen A, Wever EM, Draisma G, Roobol MJ, de Koning HJ. Overdetection, overtreatment and costs in prostate-specific antigen screening for prostate cancer. Br J Cancer. 2009;101:1833–1838. doi: 10.1038/sj.bjc.6605422. - DOI - PMC - PubMed
    1. Heijnsdijk EA, de Carvalho TM, Auvinen A, Zappa M, Nelen V, Kwiatkowski M, et al. Cost-effectiveness of prostate cancer screening: a simulation study based on ERSPC data. J Natl Cancer Inst. 2014;107:366. doi: 10.1093/jnci/dju366. - DOI - PMC - PubMed
    1. Richiardi L, Fiano V, Vizzini L, De Marco L, Delsedime L, Akre O, et al. Promoter methylation in APC, RUNX3, and GSTP1 and mortality in prostate cancer patients. J Clin Oncol. 2009;27:3161–3168. doi: 10.1200/JCO.2008.18.2485. - DOI - PubMed
    1. Djavan B, Zlotta AR, Remzi M, Ghawidel K, Bursa B, Hruby S, et al. Total and transition zone prostate volume and age: how do they affect the utility of PSA-based diagnostic parameters for early prostate cancer detection? Urology. 1999;54:846–852. doi: 10.1016/S0090-4295(99)00329-5. - DOI - PubMed

Substances