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Epitranscriptome biomarkers successfully distinguish early and late-stage prostate cancer

Written by Lauren Robertson, Science Writer

As one of the leading causes of cancer-related deaths for men in the US and Europe, determining the stage of prostate cancer (PCa) is crucial to ensure higher survival rates and reduce the chances of overtreatment.

A new study, published in Genomics, has attempted to facilitate this tricky process by identifying molecular markers capable of differentiating localized PCa from locally advanced PCa.

The epitranscriptome of prostate cancer

DNA methylation is known to play a role in regulating gene expression – specifically, any occurrence in the promoter regions of the genome can prevent the binding of transcription factors and lead to downregulation of transcription. In the case of PCa, hypermethylation has been linked to a higher rate of incidence, but it is still unclear which regions of the genome, and which specific genes, are causing this hypermethylation. In addition, hypermethylated CpG sites appear to be more prone to mutations and tumour-derived factors have been shown to influence the metabolic profile of periprostatic adipose tissue (a metabolically active tissue surrounding the prostate).

This means that identifying the epitranscriptomic biomarkers associated with gene expression and DNA methylation could help differentiate localised prostate cancer (LPC) from locally advanced prostate cancer (LAPC) – ultimately leading to better treatment outcomes.

Searching for the signature

To date, only a few studies have compared the different stages of cancer in the same PCa patients. To achieve their aim of uncovering new biomarkers for PCa, the research team profiled the gene expression and DNA methylation of 10 patients’ periprostatic (PP) adipose tissue – 4 with LPC and 6 with LAPC.

By combining and comparing the gene expression and DNA methylation profiles of each gene and its CpG site in the promoter region, the team were able to construct a “signature” of 30 genes that could reliably differentiate the two stages of PCa. These genes were found by looking at the gene-CpG pairs with statistically significant anti-correlation – in other words, where a down-regulated gene showed hypermethylation in the promoter region.

From their analysis of 20,028 genes, they identified 56 that showed this particular anti-correlation pattern. A further 30 genes were found to show all three features (anti-correlation, down-regulation, and hyper-methylation) simultaneously, meaning they had a high chance of being affected by mutations. They were also shown to be involved in a wide range of cell processes, including DNA replication and cell regulation. The team had found their signature.


The team went on to show that 6 genes within the 30-gene signature showed significantly different mutational burdens between the LPC and LAPC patients. Classification models based on the 30 genes were used to predict PCa patients’ metastasis and progression risk and proved that this signature could potentially work well as a prognostic biomarker for PCa.

In the future, more work will need to be done on a larger cohort to fully validate the findings, and the influence of confounding variables such as diet will need to be accounted for. Besides these limitations, this study may act as a useful framework for future work looking to identify novel candidate biomarkers that can distinguish primary from advanced prostate cancer – meaning patients get the correct treatment for particular stages of disease.