The increased methylation of the signature CpGs was also present in normal-appearing tissues collected from cancer patients, but not in samples obtained from healthy donors. This suggests the involvement of DNA methylation in a “field defect”. Field defect (or field cancerization) refers clinically to the existence of pre-neoplastic alterations in cells of a tissue that are associated with local recurrences. From a molecular point of view, this phenomenon has been explained by genetic abnormalities in patients with familial cancers. However, our data propose a contribution of epigenetic alterations to the field defect in sporadic cancers. GHSR methylation could thus possibly act as a marker of treatment success and recurrence.
...
Figure to the left: To visualise differences in the degree of methylation in breast samples, correspondence analysis (CA) was used. In the projection plot, each sample is depicted as a coloured square and CpG sites that exhibited the most significant differential methylation levels are represented as black dots. All co-localise with the cancer samples at the right side of the plot, indicating that the highest methylation level is found in cancer. In contrast, the healthy samples are located to the left, in the opposite direction off the centroid, indicating that the CpGs are at the lowest level of methylation in these samples. Likewise, based on the localisation of normal-appearing tissues of cancer patients and benign samples along the horizontal axis (first principal component; i.e., the direction along which the samples show the largest variation), it can be seen that an intermediate methylation load existed in these samples

PCR amplification of bisulfite-treated DNA is a processing step that is common to many currently used methods of quantitative methylation analysis. Preferential amplification of unmethylated alleles – known as PCR-bias – may significantly affect the accuracy of quantification. To date, reported processes for avoiding PCR-bias relied on an optimisation of PCR conditions. Although shown to be effective for particular genes, the implementation is time-consuming and labour-intensive, especially if multiple loci are analysed, thus the usefulness remains contradictory.
...
We developed an effective method of correcting biased methylation data. As opposed to refining experimental conditions, we approached the correction of amplification bias by accepting its occurrence during experimentation but adjusting the initial amplification result by a comparison to calibration data and the application of regression curves for deriving correction factors. As few as three calibration samples (0, 50, 100% methylation) are sufficient to define methylation degrees accurately. Two types of regression – hyperbolic and cubic polynomial – were checked. The correction process based on cubic polynomial regression was found to be superior overall.
...
The method is applicable irrespective of the locus that is interrogated or the number of sites analysed. Based on curve-fitting, the method is not influenced by the type of bias – preferential recovery of methylated or unmethylated alleles – and works equally well for both. Furthermore, any bias that was additionally introduced by the analysis procedure, for example a sequencing readout, could be compensated by the very process. The method is also automatable for high-throughput analyses.

Figure legend: The degree of bias introduced by PCR-amplification is shown for three gene promoters.The apparent degree of methylation observed after amplification (y axis) was plotted as a function of the actual methylation (x axis). The red line indicates the regression curve fitted to the data. Without correction (left) there was partly a substantial deviation between real and observed methylation, as indicated by the distance between the red line and the expected diagonal. The deviation basically disappeared upon correction (right).


Kapsner et al. (2021) Int. J. Cancer 149, 1150-1165.