Epigenome-wide analysis of common warts reveals aberrant promoter methylation

Abstract

Epigenetic alteration of host DNA is a common occurrence in both low- and high-risk human papillomavirus (HPV) infection. Although changes in promoter methylation have been widely studied in HPV-associated cancers, they have not been the subject of much investigation in HPV-induced warts, which are a temporary manifestation of HPV infection. The present study sought to examine the differences in promoter methylation between warts and normal skin. To achieve this, DNA was extracted from 24 paired wart and normal skin samples and inputted into the Infinium MethylationEPIC BeadChip microarray. Differential methylation analysis revealed a clear pattern of hyper- and hypomethylation in warts compared to normal skin, and the most differentially methylated promoters were found within the EIF3EP2, CYSLTR1, C10orf99, KRT6B, LAMA4, and H3F3B genes as well as the C9orf30 pseudogene. Moreover, pathway analysis showed that the H3F3A, CDKN1A, and MAPK13 genes were the most common regulators among the most differentially methylated promoters. Since the tissue samples were excised from active warts, however, this differential methylation could either be a cellular response to HPV infection or an HPV-driven process to establish the wart and/or promote disease progression. Conclusively, it is apparent that HPV infection alters the methylation status of certain genes to possibly initiate the formation of a wart and maintain its presence.

Keywords: HPV; epigenetics; methylation; promoter; wart.

Figure 1

Distribution of CpG sites per promoter.

Figure 2

Distribution of CpG sites across promoters. The relative coordinates of 0 and 1 correspond to the start and end coordinates of promoters. Coordinates smaller than 0 and greater than 1 denote flanking regions normalized by region length.

Figure 3

Heatmap showing the hierarchical clustering of samples displaying only the top-ranking 1000 most variable promoters with the highest variance across all samples. Clustering utilized complete linkage and Manhattan distance. The top x-axis shows the normal skin (NS) and wart (W) samples, while the bottom x-axis shows the patient identification number. Values of 0 (red color) and 1 (purple color) indicate decreased and increased methylation, respectively.

Figure 4

Two-dimensional scatterplot illustrating sample positions after the application of Kruskal's non-metric multidimensional scaling based on the matrix of average methylation and Manhattan distance.

Figure 5

Two-dimensional scatterplot showing sample positions after principal component analysis.

Figure 6

Two-dimensional scatterplot of the top-ranking 1000 DM promoters. The mean.mean β values of normal skin (NS) and warts (W) are shown on the x-axis and y-axis, respectively. The methylation β values range from 0 (unmethylated) to 1 (methylated).

Figure 7

Volcano plot of the promoter differential methylation quantified by the log₂ of the quotient in mean.mean methylation and adjusted combined p-value between warts (W) and normal skin (NS). The color scale represents the combined ranking.

Figure 8

Word cloud illustrating the significant biological processes (BP) associated with the top-ranking 500 hypermethylated promoters.

Figure 9

Word cloud illustrating the significant molecular functions (MF) associated with the top-ranking 500 hypermethylated promoters.

Figure 10

Word cloud illustrating the significant biological processes (BP) associated with the top-ranking 500 hypomethylated promoters.

Figure 11

Word cloud illustrating the significant molecular functions (MF) associated with the top-ranking 500 hypomethylated promoters.

Figure 12

Pathway signaling network generated from the top-ranking 1000 DM promoters.

Figure 13

Pathway signaling network generated from the top-ranking 100 DM promoters.