Methylation pattern analysis in prostate cancer as a new potential diagnostic approach

Roberta Gunelli1, Massimo Fiori1, Teo Zenico1, Giorgia Gurioli2, Samanta Salvi2, Filippo Martignano2, Flavia Foca3, Matteo Costantini4, Umberto Salomone1, Cristiano Salaris1, Daniele Calistri2, Valentina Casadio2
  • 1 Ospedale Morgagni Pierantoni, Divisione di Urologia (Forlì)
  • 2 Laboratorio di Bioscienze, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS (Meldola)
  • 3 Unità di Biostatistica e Studi Clinici, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS (Meldola)
  • 4 Ospedale Morgagni Pierantoni, Divisione di Patologia (Forlì)

Objective

Epigenetic modifications, such as DNA methylation in CpG islands, are correlated to cancer development suggesting that these events could be early phenomena (1). For this reason, DNA methylation could be a potential biomarker for prostate cancer early diagnosis. Moreover, every tumor type has a specific methylation pattern and, when compared with healthy tissues, it could be useful for the diagnosis, prognosis and treatment of the disease (2). Characterizing aberrant DNA methylation changes associated with prostate carcinogenesis may identify a tumor-specific methylation pattern useful for prostate cancer early diagnosis (3).

Materials and Methods

The objective of the study was to assess the methylation status of 40 tumor suppressor genes using methylation specific-multiplex ligation probe amplification (MS-MLPA) assay and we used MSP as a confirmatory methodology to analyze the methylation status of the 5 genes resulted methylated with statistical significance: GSTP1, RARB, RASSF1A, SCGB3A1 and CCND2 (4,5). We analyzed two sets of paraffin-embedded tissues. In the training set 89 samples were collected distinguished in: 40 prostate cancer tissues, 26 healthy prostatic tissues adjacent to the tumor and 23 healthy non prostatic tissues, such as seminal vesicles and vesical neck. In the validation set 40 prostate cancer tissues and their healthy prostate tissues adjacent to the tumor were collected. Hierarchical cluster analysis (Ward method) was performed to determine a methylation pattern.

Results

In training set, the hierarchical cluster analysis identified highly methylated genes (GSTP1, RARB, RASSF1, SCGB3A1, CCND2, APC, ID4) in tumor samples respect to other tissues. In the same way, the validation set cluster analysis confirmed the same genes with different methylation status. In particular, five genes (GSTP1, RARB, RASSF1, SCGB3A1, CCND2) were significantly different methylated between the tissues (p < 0.001). These genes had area under ROC curve varying from 0.89 to 0.95 and diagnostic accuracy from 80% to 90%.
Correlation analyses between methylation status of the 40 tumor suppressor genes and clinical-pathological features such as Gleason score, tumor size, PSA levels and age at diagnosis were performed but none of the genes analyzed was significantly correlated with clinical-pathological features.

Discussions

The current need is to find which patients with suspected PCa and an initial negative biopsy have to be selected for further biopsy.
A MS-MLPA approach was adopted to find out a panel of tumor suppressor genes able to distinguish prostate cancer tissues from healthy ones (4, 5, 6). We observed that 5 genes (GSTP1, RARB, RASSF1, SCGB3A1 and CCND2) were highly specific for statistically discriminating prostate cancer tissues from healthy prostatic tissues adjacent to the tumor and to date SCGB3A1 and CCND2, that in our study had a statistically higher methylation, were not hypothesized as potential biomarkers in early diagnosis of prostate cancer (7). These findings suggest that maybe an early methylation phenomenon occurs in healthy prostate tissue inducing a consequent cancer transformation and, as suggested by some recently published studies (8, 9), it could be important to identify prostate cancer among negative core biopsies, thus avoiding unnecessary repeat biopsies.

Conclusion

In our study the methylation status of GSTP1, RARB, RASSF1, SCGB3A1, CCND2 genes was highly specific for statistically discriminating prostate cancer tissues from healthy prostate tissues adjacent to the tumor, in particular SCGB3A1 and CCND2 are novel potential biomarkers in early diagnosis prostate cancer and useful avoiding unnecessary repeat biopsies.
The main limitations of our study are the modest case series and the data found in tissue but our preliminary results regarding the use of methylation status of several genes as biomarkers for early diagnosis are encouraging and we are aware of the need to continue the study with the research performed on biological fluids (10, 11, 12).

References

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