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. 2020 Jul 8;13:100. doi: 10.1186/s12920-020-00745-6

Genome-wide identification of methylated CpG sites in nongenital cutaneous warts

Laith N AL-Eitan 1,2,✉, Mansour A Alghamdi 3,4, Amneh H Tarkhan 1, Firas A Al-Qarqaz 5,6
PMCID: PMC7346436  PMID: 32641122

Abstract

Background

Low-risk HPV infection has not been the subject of epigenetic investigation. The present study was carried out in order to investigate the methylation status of CpG sites in non-genital cutaneous warts.

Methods

Genomic DNA was extracted from 24 paired epidermal samples of warts and normal skin. DNA samples were bisulfite converted and underwent genome-wide methylation profiling using the Infinium MethylationEPIC BeadChip Kit.

Results

From a total of 844,234 CpG sites, 56,960 and 43,040 CpG sites were found to be hypo- and hypermethylated, respectively, in non-genital cutaneous warts. The most differentially methylated CpG sites in warts were located within the C10orf26, FAM83H-AS1, ZNF644, LINC00702, GSAP, STAT5A, HDAC4, NCALD, and EXOC4 genes.

Conclusion

Non-genital cutaneous warts exhibit a unique CpG methylation signature.

Keywords: HPV, Warts, DNA methylation, CpG, Epigenetics

Background

CpG sites are parts of DNA that consist of a cytosine nucleotide linked to a guanine nucleotide by a phosphate group, and they are often found as a part of CpG islands, the latter of which are areas of high CpG frequencies [1]. From an epigenetic perspective, CpGs are of particular importance due to the fact that DNA methylation in mammals occurs primarily in a CpG context [2]. In mammalian genomes, the majority of CpG sites are methylated, while those in CpG islands are generally hypomethylated [3]. Due to the high mutability of methylcytosine, methylated CpG sites are under-represented in the human genome [4]. Aberrant CpG methylation patterns increase susceptibility to various diseases, including cancer, but such changes can also be induced during host-pathogen interactions [5, 6].

Host gene dysregulation is a common component of viral infection, and such changes are often generated via epigenetic exploitation of the host genome [7]. In order to evade the antiviral immune response, DNA viruses induce aberrant methylation of immune-related genes in the host [8]. One such example is the human papillomavirus (HPV), a DNA virus that alters host methylation patterns as a part of its life cycle and replication mechanisms within keratinocytes [9]. To date, more than 200 HPV genotypes have been characterized, most of which are low-risk and often manifest in the form of benign cutaneous or genital lesions known as warts [10]. However, a small group of HPV types are considered to be high risk, as they are a causative agent for several different types of squamous cell carcinomas [11].

High-risk HPV infection affects cervical cancer progression by increasing levels of DNA methylation, although methylation patterns were heterogenous among different neoplastic grades [12–14]. Hypomethylation of a CpG site in the MAL gene was reported to be potentially associated with persistent cervical infection with high-risk HPV [15]. Moreover, HPV-positive head-and-neck squamous cell carcinomas exhibited a novel methylation signature in which hypomethylated CpG islands were functionally correlated with gene expression [16]. In fact, HPV-induced epigenetic changes are a major contributing factor to the stability of malignant head-and-neck squamous cell carcinoma [17]. Similarly, CpG loci were differentially methylated in HPV-positive anal squamous neoplasia, and significant differential methylation was observed between in-situ and invasive samples [18].

Unlike its high-risk counterpart, low-risk HPV infection has not been the subject of epigenetic analysis in the context of non-genital cutaneous warts, the latter of which constitutes an extremely common skin disease that is benign and self-limiting in the majority of cases [19]. The most prevalent type of non-genital cutaneous wart is the common wart, which usually manifests on the hands and feet as a firm, hyperkeratotic papule with an irregular surface [20]. The extensive transformation that an HPV-infected keratinocyte undergoes to form a wart suggests that a similar change in methylation patterns must occur. Subsequently, the aim of the current study is to identify the genome-wide methylation status of CpG sites in warts as compared to normal skin.

Methods

Patient recruitment

Twelve patients were recruited at the dermatological clinic in King Abdullah University Hospital in the north of Jordan. The Institutional Review Board (IRB) at Jordan University of Science and Technology (JUST) granted ethical approval to conduct the present study. The inclusion criteria for participants comprised the following characteristics: being male, being free from autoimmune disease, presenting with common warts, not having received prior treatment for their warts, and having given written informed consent. Shave biopsies were performed by a resident dermatologist in order to excise paired normal skin and wart samples from each patient, which were then stored at − 20 °C until subsequent processing.

Extraction of genomic DNA and bisulfite conversion

RNA-free genomic DNA was extracted by means of the QIAamp DNA Mini Kit (Qiagen, Germany) and shipped to the Australian Genome Research Facility (AGRF) on dry ice. Upon arriving to the AGRF, further quality control analysis was performed for each sample using the QuantiFluor® dsDNA System (Promega, USA) and 0.8% agarose gel electrophoresis to determine their purity and integrity, respectively. After obtaining assurance of their quality, the EZ DNA Methylation kit (Zymo Research, USA) was employed for the bisulfite conversion of normalized samples.

Genome-wide methylation profiling and data processing

The Infinium MethylationEPIC BeadChip Kit (Illumina, USA) was utilized in order to interrogate over 850,000 methylation sites. The MethylationEPIC array contains 866,895 probes that target 863,904 CpG sites, 2932 CpH sites, and 59 rs sites. The raw intensity data generated by the array was analyzed using RnBeads, a computational R package [21].

Differential methylation analysis

To calculate the extent of differential methylation (DM) for each CpG site, limma was used to determine three ranks: the beta difference in methylation means between warts (W) and normal skin (NS), the log2 of the quotient in methylation, and the DM p-value [21]. Limma was also utilized to compute p-values on CpG sites [22]. Multiple testing was corrected for by setting the false discovery rate (FDR) at 5% with the Benjamini-Hochberg procedure. Using these three ranks, a combined rank was formulated in which increased DM at a particular CpG site resulted in a smaller rank [21]. The combined rank was used to sort DM CpG sites in ascending order, and the top-ranking 100,000 sites were selected for further analysis.

Enrichment, pathway, and signaling analysis

Gene ontology (GO) term enrichment analysis as well as KEGG and Reactome pathway analysis of the top 100 CpG sites were carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (https://david.ncifcrf.gov/). GO terms revolved around three criteria (biological process (BP), cellular component (CC), and molecular function (MF)), and the cut-off threshold was fixed at p-value ≤0.05. After selecting the top-ranked 100 DM CpG sites, the Signaling Network Open Resource 2.0 (SIGNOR) was used to analyze the signaling networks of associated genes [23].

Results

Sample clustering

Based on the DM values of the top-ranking 1000 loci, an expected clustering pattern can be observed between the NS and W samples (Fig. 1). Using multidimensional scaling (MDS) and principal component analysis (PCA), strong signals in sample methylation values were examined (Fig. 2a and b).

Fig. 1.

Fig. 1

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Heatmap showing the hierarchal clustering of the top 1000 most variable loci across all 24 samples. Clustering used average linkage and Manhattan distance. Patient identification numbers are shown on the x-axis. W and NS stand for wart and normal skin, respectively

Fig. 2.

Fig. 2

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Scatter plots showing the coordinates of the wart (W) and normal skin (NS) samples (a) after performing Kruskal’s multi-dimensional scaling based on the matrix of the average methylation levels and Euclidean distance and (b) on the first and second principal components. A clear difference between the W and NS samples can be seen in both plots

Processing and filtering of data

17,371 probes were removed due to their overlap with SNPs (Fig. 3a). A further 2,310 probes were filtered out using the Greedycut algorithm in RnBeads. Additional filtering eliminated 2,980 probes with specific contexts (Fig. 3b). In total, 22,661 probes were removed and 844,234 probes were retained. Both probes and samples were subject to the full RnBeads package pipeline, which entailed quality control, preprocessing, batch effects testing, and normalization (Fig. 4). The complete processed methylation data for the CpG sites can be found in Supplementary File.

Fig. 3.

Fig. 3

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Contrasting the density distributions of methylation levels (β) after (a) removal of SNP-enriched probes and filtration by Greedycut and (b) removal of context-specific probes

Fig. 4.

Fig. 4

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Density distributions of methylation levels (β) were normalized using Dasen’s method. The figure compares the β values before and after correction

Differential methylation of CpG sites

Of the top-ranking 100,000 CpG sites in terms of DM, 56,960 sites were hypomethylated and 43,040 sites were hypermethylated in W compared to NS, with a mean beta difference greater than 0.055 and less than − 0.055 (p-value < 0.032; adjusted p-value < 0.032) (Fig. 5). The beta difference for the hypomethylated and hypermethylated sites ranged from − 0.055 to 0.56 and 0.55 to 0.56, respectively. Similarly, the log2 of the quotient in methylation between W and NS ranged from − 2.47 to 2.9 (Fig. 6). The highest concentration of DM sites was seen on chromosomes 1 and 2 (Fig. 7). The top-ranking100 CpG sites, i.e. the most DM, are listed in Table 1.

Fig. 5.

Fig. 5

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Scatter plots for the (a) top-ranking 1000 and (b) top-ranking 100,000 differentially methylated CpG sites. For each plot, the mean β values of normal skin (mean.beta. NS) are on the x-axis, while the mean β values of warts (mean.beta. W) are on the y-axis. Methylation levels (β) varied between 0 (unmethylated) and 1 (fully methylated). Blue points represent variable differentially methylated sites

Fig. 6.

Fig. 6

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Volcano plot of the top-ranking 1000 differentially methylated sites. Differential methylation was measured by the log2 of the mean quotient in methylation (mean.quot.log2) and the mean fold difference (mean.diff) between warts (W) and normal skin (NS). Data points less than 0 represent relative hypomethylation, while those more than 0 represent relative hypermethylation. The intensity of each data point correlates with the combined rank score as shown on the color scale to the right

Fig. 7.

Fig. 7

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Chromosomal distribution of the top 100 differentially methylated CpG sites in warts compared to normal skin

Table 1.

The 100 CpG sites with the lowest combined rank scores

CpG Chromosome Gene Methylation region CpG Island Mean β value (NS) Mean β value (W) Mean β value diff (W-NS) mean.quot. (log2) P-value False discovery rate Combined rank score Methylation pattern
cg09671951 10 C10orf26 Body 0.1129 0.5848 0.4719 2.2753 6.82E-16 5.09E-11 48 Hypermethylation
cg27071672 8 FAM83H- AS1 Body S_Shelf 0.1290 0.5765 0.4475 2.0772 1.74E-14 1.99E-10 102 Hypermethylation
cg07385604 1 ZNF644 TSS1500 S_Shore 0.1281 0.5720 0.4440 2.0756 9.33E-16 5.09E-11 110 Hypermethylation
cg12432168 10 LINC00702 Body 0.1558 0.6389 0.4832 1.9690 6.83E-15 1.31E-10 151 Hypermethylation
cg06305962 7 GSAP Body 0.1249 0.5457 0.4208 2.0421 1.49E-14 1.91E-10 183 Hypermethylation
cg00071017 2 0.6112 0.1537 −0.4575 −1.9241 7.83E-16 5.09E-11 186 Hypomethylation
cg16530881 17 0.1080 0.5208 0.4127 2.1688 1.99E-13 7.07E-10 236 Hypermethylation
cg08246644 17 STAT5A

TSS1500;5’UTR;TS

S200

 N_Shore 0.1009 0.5098 0.4089 2.2286 2.29E-15 7.97E-11 245 Hypermethylation
cg05171197 2 HDAC4 Body 0.1973 0.7523 0.5550 1.8785 1.65E-13 6.32E-10 247 Hypermethylation
cg16516970 8 NCALD 5’UTR 0.1567 0.6028 0.4461 1.8783 2.74E-14 2.4E-10 248 Hypermethylation
cg03432603 7 EXOC4 Body 0.6423 0.1335 −0.5088 −2.1842 2.14E-13 7.24E-10 249 Hypomethylation
cg01890417 1 ZNF644 TSS1500 S_Shore 0.1519 0.5773 0.4254 1.8592 2.75E-14 2.4E-10 274 Hypermethylation
cg00194325 2 TANC1 Body 0.1719 0.6446 0.4727 1.8473 5.54E-16 5.09E-11 290 Hypermethylation
cg25894955 9 ABCA1 Body 0.5371 0.1351 −0.4021 −1.9151 2.81E-14 2.4E-10 295 Hypomethylation
cg10560060 13 GJB2 5’UTR N_Shelf 0.6623 0.1799 −0.4824 − 1.8238 2.4E-13 7.66E-10 329 Hypomethylation
cg10144055 2 0.1350 0.5324 0.3974 1.9032 7.42E-15 1.34E-10 336 Hypermethylation
cg19342952 13 GJB2 5’UTR N_Shore 0.6449 0.1770 −0.4679 −1.8080 5.82E-14 3.81E-10 347 Hypomethylation
cg15612257 2 N_Shore 0.1547 0.5655 0.4108 1.8048 2.56E-15 7.97E-11 359 Hypermethylation
cg07863022 17 SEPT9;

5’UTR;Body;TSS15

00

 0.1681 0.6076 0.4395 1.7937 3.99E-15 9.85E-11 375 Hypermethylation
cg02745009 3 ARHGAP3 1 Body S_Shore 0.1718 0.6135 0.4417 1.7783 2.9E-13 8.24E-10 407 Hypermethylation
cg15782771 5 0.7396 0.2096 −0.5299 −1.7709 3.84E-14 3.03E-10 428 Hypomethylation
cg04272613 14 DAAM1 5’UTR 0.1508 0.5378 0.3869 1.7680 2.74E-15 7.97E-11 445 Hypermethylation
cg10017626 2 N_Shore 0.0988 0.4854 0.3866 2.1870 2.02E-13 7.07E-10 449 Hypermethylation
cg18248499 11 ROBO4 TSS1500 0.5057 0.1193 −0.3865 −1.9961 3.43E-13 9.24E-10 451 Hypomethylation
cg10841463 14 0.1646 0.5798 0.4153 1.7566 7.01E-17 1.69E-11 457 Hypermethylation
cg19497037 11 0.5188 0.1328 −0.3860 −1.8891 7.48E-13 1.37E-09 459 Hypomethylation
cg13800897 2 0.5754 0.1613 −0.4141 −1.7727 8.99E-13 1.55E-09 490 Hypomethylation
cg13632752 8 0.5831 0.1474 −0.4357 −1.9140 9.15E-13 1.56E-09 494 Hypomethylation
cg27277339 15 MYO5C Body 0.1561 0.5455 0.3894 1.7417 9.65E-14 4.88E-10 496 Hypermethylation
cg19158326 22 GRAMD4 Body 0.0980 0.4793 0.3813 2.1796 3.91E-15 9.85E-11 514 Hypermethylation
cg20400915 17 STAT5A

TSS1500;5’UTR;TS

S200

 N_Shore 0.0555 0.4492 0.3937 2.8086 1.02E-12 1.66E-09 519 Hypermethylation
cg20392201 1 FAM129A Body 0.1263 0.5848 0.4585 2.1258 1.04E-12 1.69E-09 521 Hypermethylation
cg21879102 12 CIT Body N_Shore 0.1946 0.6605 0.4659 1.7127 2.61E-13 7.91E-10 549 Hypermethylation
cg14384093 9 C9orf5 Body N_Shelf 0.1256 0.5097 0.3841 1.9381 1.25E-12 1.9E-09 557 Hypermethylation
cg18813270 2

HS1BP3-

IT1

 TSS1500 0.6868 0.1911 −0.4957 −1.7929 1.3E-12 1.95E-09 564 Hypomethylation
cg19449565 2 HDAC4 Body 0.1691 0.6536 0.4845 1.8898 1.33E-12 1.96E-09 570 Hypermethylation
cg09187774 10 0.6165 0.1627 −0.4538 −1.8593 1.34E-12 1.98E-09 572 Hypomethylation
cg07980148 4 S_Shelf 0.6475 0.1624 −0.4852 −1.9317 1.36E-12 1.99E-09 573 Hypomethylation
cg03304533 11 0.6668 0.1977 −0.4690 −1.7040 3.09E-13 8.67E-10 576 Hypomethylation
cg08569613 17 STAT5A

TSS1500;5’UTR;TS

S200

 N_Shore 0.0692 0.4453 0.3761 2.5226 6.22E-15 1.25E-10 578 Hypermethylation
cg06848849 1

ARHGEF10

L

 Body 0.1451 0.5204 0.3753 1.7737 2.84E-14 2.4E-10 591 Hypermethylation
cg17164954 6 ARID1B Body S_Shelf 0.1656 0.5604 0.3948 1.6997 6.39E-13 1.24E-09 591 Hypermethylation
cg13733684 15 ZNF106 TSS200;Body 0.1724 0.5807 0.4083 1.6954 1.72E-14 1.99E-10 603 Hypermethylation
cg05669832 2 PRKD3 TSS1500 0.2068 0.6911 0.4843 1.6934 2.72E-13 8.02E-10 611 Hypermethylation
cg06382539 12 BHLHE41 Body N_Shore 0.1759 0.5882 0.4123 1.6864 1.58E-12 2.14E-09 629 Hypermethylation
cg16303737 20 0.5411 0.1618 −0.3793 −1.6819 7.37E-13 1.36E-09 642 Hypomethylation
cg27335585 5

LOC101929

710

 Body 0.7606 0.2298 −0.5308 − 1.6840 1.78E-12 2.31E-09 652 Hypomethylation
cg09185727 6 0.5467 0.1642 −0.3825 −1.6763 2.73E-13 8.02E-10 652 Hypomethylation
cg15350314 3

LOC101928

992

 Body 0.1552 0.5574 0.4021 1.7797 1.85E-12 2.36E-09 658 Hypermethylation
cg11508674 14 FOXN3 Body 0.1648 0.6344 0.4696 1.8820 2.02E-12 2.49E-09 683 Hypermethylation
cg06610988 18 SETBP1 5’UTR S_Shore 0.1684 0.5546 0.3862 1.6622 3.94E-14 3.06E-10 684 Hypermethylation
cg18492160 15 0.5276 0.1311 −0.3965 −1.9299 2.03E-12 2.49E-09 690 Hypomethylation
cg02921273 20 0.0980 0.4645 0.3664 2.1349 3.95E-14 3.06E-10 699 Hypermethylation
cg14167109 11 MAML2 Body 0.1594 0.5381 0.3787 1.6939 2.13E-12 2.55E-09 703 Hypermethylation
cg06373653 12 CD163L1 Body 0.4932 0.1277 −0.3656 −1.8699 2.3E-13 7.47E-10 709 Hypomethylation
cg09403144 18 SETBP1 Body 0.1549 0.5202 0.3653 1.6847 3.68E-14 2.96E-10 714 Hypermethylation
cg06746371 6 DCBLD1 Body 0.7344 0.2249 −0.5095 −1.6641 2.31E-12 2.68E-09 727 Hypomethylation
cg14002969 20 PTPRA 5’UTR 0.4985 0.1342 −0.3644 −1.8187 5.04E-13 1.11E-09 727 Hypomethylation
cg07076915 16 PKD1 Body N_Shelf 0.2112 0.6851 0.4739 1.6517 2.48E-14 2.32E-10 728 Hypermethylation
cg27341747 6 0.2010 0.6524 0.4514 1.6503 5.21E-14 3.61E-10 732 Hypermethylation
cg20964957 4 0.5612 0.1185 −0.4428 −2.1527 2.39E-12 2.74E-09 736 Hypomethylation
cg19917507 18 ALPK2 Body 0.5863 0.1813 −0.4050 −1.6401 4.78E-13 1.08E-09 757 Hypomethylation
cg00925616 1 Island 0.0781 0.5172 0.4392 2.5818 2.61E-12 2.89E-09 762 Hypermethylation
cg13515269 12 BHLHE41 3’UTR N_Shore 0.2078 0.6886 0.4809 1.6818 2.71E-12 2.96E-09 772 Hypermethylation
cg18638180 21 C21orf70 Body S_Shore 0.1734 0.6318 0.4584 1.8073 2.93E-12 3.13E-09 791 Hypermethylation
cg17967134 17 MPRIP Body 0.1283 0.4884 0.3601 1.8495 1.19E-12 1.83E-09 804 Hypermethylation
cg06373648 6 SYNGAP1 Body 0.1564 0.5160 0.3596 1.6604 4.57E-13 1.06E-09 818 Hypermethylation
cg14825152 1 0.1422 0.5010 0.3588 1.7475 4.83E-13 1.09E-09 828 Hypermethylation
cg08966889 6 TRAM2 Body N_Shore 0.1747 0.5588 0.3840 1.6224 1.16E-12 1.81E-09 828 Hypermethylation
cg09443467 5 TENM2 Body 0.5807 0.1623 −0.4185 −1.7779 3.44E-12 3.49E-09 833 Hypomethylation
cg17758398 18 0.6251 0.1850 −0.4401 −1.7035 3.48E-12 3.51E-09 836 Hypomethylation
cg01821452 12 0.2138 0.6779 0.4641 1.6198 1.44E-12 2.06E-09 840 Hypermethylation
cg19663114 3 MED12L Body 0.7670 0.2279 −0.5390 −1.7073 3.64E-12 3.6E-09 853 Hypomethylation
cg10624729 1 FAM73A Body 0.1847 0.5864 0.4017 1.6152 1.53E-13 6.05E-10 857 Hypermethylation
cg26586287 11 0.6087 0.1625 −0.4463 −1.8430 3.74E-12 3.67E-09 859 Hypomethylation
cg23983887 1 VPS13D Body 0.1546 0.5113 0.3567 1.6629 1.65E-12 2.21E-09 866 Hypermethylation
cg08921063 6 WASF1 5’UTR 0.4750 0.1185 −0.3565 −1.9164 2.02E-12 2.49E-09 871 Hypomethylation
cg14359656 17 SPAG9 Body 0.5856 0.1477 −0.4380 −1.9176 3.98E-12 3.81E-09 883 Hypomethylation
cg26754187 3 0.5241 0.1368 −0.3873 −1.8634 4E-12 3.81E-09 885 Hypomethylation
cg10126884 4 0.4827 0.1254 −0.3573 −1.8635 4.05E-12 3.85E-09 888 Hypomethylation
cg13355857 16 0.6967 0.1872 −0.5096 −1.8418 4.06E-12 3.85E-09 889 Hypomethylation
cg13568540 7 PKD1L1 Body 0.6599 0.1847 −0.4752 −1.7828 4.22E-12 3.95E-09 901 Hypomethylation
cg08611640 1 VPS13D Body;Body 0.1109 0.4654 0.3546 1.9757 7.7E-15 1.34E-10 912 Hypermethylation
cg25322618 2 RAPGEF4 TSS200;Body 0.2041 0.6388 0.4347 1.5994 1.22E-13 5.62E-10 913 Hypermethylation
cg16669099 6 0.1801 0.5652 0.3851 1.5971 3.77E-12 3.69E-09 919 Hypermethylation
cg19712663 6 SLC22A23 Body 0.1017 0.4711 0.3694 2.1069 4.47E-12 4.07E-09 927 Hypermethylation
cg13720639 14 SIPA1L1 Body 0.1299 0.4946 0.3646 1.8502 4.5E-12 4.08E-09 929 Hypermethylation
cg04394003 12 C12orf75 TSS1500 N_Shore 0.1172 0.4703 0.3531 1.9170 3.46E-12 3.51E-09 931 Hypermethylation
cg17356718 2 HDAC4 Body 0.1435 0.5270 0.3835 1.8066 4.51E-12 4.08E-09 931 Hypermethylation
cg26639076 2 RIF1 3’UTR 0.1710 0.5360 0.3650 1.5930 7.11E-14 4.2E-10 936 Hypermethylation
cg07969739 10 BTAF1 Body 0.5137 0.1346 −0.3791 −1.8564 4.74E-12 4.17E-09 958 Hypomethylation
cg26125625 3 SLC12A8 Body Island 0.1074 0.4587 0.3513 1.9968 2.24E-12 2.64E-09 965 Hypermethylation
cg18251218 1 0.0952 0.4461 0.3510 2.1169 1.17E-16 1.69E-11 967 Hypermethylation
cg23909079 10 GRID1 Body 0.6723 0.2146 −0.4577 −1.6031 4.92E-12 4.25E-09 977 Hypomethylation
cg24117274 1 RAP1GAP Body N_Shelf 0.1260 0.4766 0.3505 1.8387 7.37E-14 4.29E-10 979 Hypermethylation
cg09262171 16 ADCY9 Body 0.1896 0.5865 0.3970 1.5796 3.41E-14 2.78E-10 992 Hypermethylation
cg14600452 10 0.6088 0.1865 −0.4223 −1.6550 5.44E-12 4.53E-09 1014 Hypomethylation
cg24088496 11 MAML2 Body 0.1856 0.5727 0.3871 1.5747 1.73E-13 6.44E-10 1016 Hypermethylation
cg06968781 1 GMEB1 5’UTR 0.5323 0.1666 −0.3657 −1.6189 5.65E-12 4.63E-09 1030 Hypomethylation
cg03133881 1 MAST2 Body 0.5066 0.1589 −0.3477 −1.6128 5.41E-12 4.52E-09 1035 Hypomethylation
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Functional enrichment analysis

GO enrichment analyses of the genes associated with the top 100 DM CpG sites were performed using the DAVID webtool. Table 2 shows the most significant GO terms (p-value ≤0.05). Associated genes were mainly enriched for “SH3 domain binding”, “actin binding”, and “GTPase activator activity” on the MF level, “regulation of GTPase activity” and “positive regulation of GTPase” on the BP level, and “postsynaptic membrane” on the CC level. The most significant KEGG and Reactome pathway terms with a p-value ≤0.05 are presented. The genes were mainly enriched in the Rap1 signaling and VxPx cargo-targeting to cilium pathways (Table 3).

Table 2.

GO enrichment analyses revealed significant (p-value ≤0.05) GO terms and associated enriched genes in the biological process (BP), cellular component (CC), and molecular function (MF) categories

Category Term P-value Genes
MF GO:0017124 ~ SH3 domain binding 0.004 ARHGAP31, ZNF106, SYNGAP1, CIT
MF GO:0003779 ~ actin binding 0.006 NCALD, WASF1, DAAM1, MPRIP, MYO5C
MF GO:0005096 ~ GTPase activator activity 0.006 ARHGAP31, RAP1GAP, SIPA1L1, SYNGAP1, ARHGEF10L
BP GO:0043087 ~ regulation of GTPase activity 0.014 RAP1GAP, SIPA1L1, SYNGAP1
BP GO:0043547 ~ positive regulation of GTPase activity 0.019 ARHGAP31, RAP1GAP, PTPRA, RAPGEF4, SYNGAP1, ARHGEF10L
CC GO:0045211 ~ postsynaptic membrane 0.019 SIPA1L1, TENM2, TANC1, GRID1
BP GO:0016337 ~ single organismal cell-cell adhesion 0.031 TENM2, PKD1, PKD1L1
BP GO:0050982 ~ detection of mechanical stimulus 0.038 PKD1, PKD1L1
MF GO:0017016 ~ Ras GTPase binding 0.039 RAP1GAP, RAPGEF4
BP GO:0010832 ~ negative regulation of myotube differentiation 0.043 HDAC4, BHLHE41
BP GO:0018105 ~ peptidyl-serine phosphorylation 0.046 MAST2, PKD1, PRKD3
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Table 3.

The most significantly enriched KEGG and Reactome pathway terms of the genes associated with the top-ranking 100 DM CpG sites

Category Term P-value Genes
KEGG_PATHWAY hsa04015:Rap1 signaling pathway 0.001 RAP1GAP, ADCY9, SIPA1L1, RAPGEF4, PRKD3
REACTOME_PATHWAY R-HSA-5620916:VxPx cargo-targeting to cilium 0.045 EXOC4, PKD1
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Signaling network analysis

Analysis of the genes associated with the top 100 DM CpG sites showed that five genes were found to be common regulators with a minimum of 20 connectivities each. These genes are the PRKD1, HDAC4, and STAT5A genes (Fig. 8).

Fig. 8.

Fig. 8

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Pathway signalling network of the common gene regulators associated with the top-ranking 100 CpG sites. Three genes (PRKD1, HDAC4, and STAT5A) have a minimum of 20 connectivities

Discussion

In the present study, the genome-wide methylation profile of CpG sites was demonstrated for the first time in non-genital cutaneous warts. Out of the 844,234 CpG sites that were investigated, 56,960 and 43,040 CpG sites were found to be hypomethylated and hypermethylated, respectively, in warts. The combined rank scoring method revealed the top 100 most differentially methylated CpG sites, which lay within the C10orf26, FAM83H-AS1, ZNF644, LINC00702, GSAP, STAT5A, HDAC4, NCALD, and EXOC4 genes, among others.

cg09671951 was found to be the most hypermethylated CpG site in warts, and it is located within the C10orf26 gene, which is also known as the outcome predictor in acute leukemia 1 (OPAL1) gene. The C10orf26 gene has been associated with response to treatment in children with acute lymphoblastic leukemia, and it has also been implicated as a modulator of schizophrenia symptoms and disease progression [24–26]. The second most hypermethylated CpG site, cg27071672, lies within the FAM83H-AS1 gene, which codes for the FAM83H antisense RNA 1 (head to head). FAM83H-AS1 dysregulation has been associated with carcinogenesis in breast, colorectal, and lung cancer [27–29]. Two of the most hypermethylated CpG sites, cg07385604 and cg01890417, were located within the ZNF644 gene, which encodes the zinc finger protein 644. ZNF644 is associated with transcriptional repression as a part of the G9a/GLP complex, and mutations in this gene are responsible for a monogenic form of myopia [30, 31].

cg12432168, located with the LINC00702 gene, and cg06305962, located within the GSAP gene, were the fourth and fifth most hypermethylated CpG sites, respectively. The long intergenic non-protein coding RNA 702 (LINC00702), like other long non-coding RNAs, functions in genetic and epigenetic regulation, and its upregulation has been reported in endometrial cancer as well as malignant meningioma [32, 33]. However, the γ-secretase activating protein (GSAP) has mostly been reported in the context of Alzheimer’s disease pathology [34, 35]. Comparatively little is known about functions of the LINC00702 and GSAP genes outside of a disease context.

In contrast, three of the most hypermethylated CpG sites (cg08246644, cg20400915, and cg08569613) were located within the signal transducer and activator of transcription 5A (STAT5A) gene, the latter of which has been extensively studied and elucidated. STAT5A has an essential function in lactogenic and mammopoietic signaling and development in adults, and its expression is upregulated by the tumor protein p53 [36, 37]. Aberrant STAT5A expression has been reported in a number of different cancers, including breast, colon, head and neck, and prostate cancer as well as leukemia [38–42]. Of particular interest is the association of STAT5A dysregulation with head and neck squamous carcinoma, which is a type of cancer that can be caused by high-risk HPV infection [43, 44]. Although low-risk HPV types lack the carcinogenic potential of their high-risk counterparts, it is intriguing that both the benign and cancerous manifestations of HPV infection exhibit aberrant STAT5A expression.

A further three of the most hypermethylated CpG sites (cg05171197, cg19449565, and cg17356718) were found within the histone deacetylase 4 (HDAC4) gene that functions in the condensation of chromatin and repression of transcription via deacetylation [45]. The survival and growth of multiple myeloma is regulated by the HDAC4-RelB-p52 complex, and the disruption of the latter blocks the growth of these cells [46]. Moreover, HDAC4 degradation by certain chemotherapeutic agents results in the apoptosis of head-and-neck cancer cells that are resistant to TRAIL, while miR-22-driven HDAC4 repression helped to resensitize fulvestrant-resistant breast cancer cells [47, 48]. Likewise, eptoposide resistance in human A549 lung cancer cells was conferred by STAT1-HDAC4 upregulation, and HDAC4 inhibition has been reported to induce apoptosis in non-small cell lung cancer PC-9 cells [49, 50].

HDAC4 has been previously implicated in viral replication as well as the host’s antiviral response [51]. For example, HIV-1 DNA integration is facilitated by the involvement of HDAC4 in the post-integration repair process [52]. Moreover, infection with the influenza A virus has been reported to cause airway remodeling in asthmatic individuals via the indirect dysregulation of HDAC4 [53]. HDAC4 is also a critical regulator of antiviral response, and its overexpression hinders the host immune response by suppressing type 1 interferon production [54]. Furthermore, STAT-HDAC4 signaling was reported to induce epithelial-mesenchymal transition, a malignant tumor feature that is also exhibited by keratinocytes during tissue repair [55–57]. High-risk HPV infection can similarly result in malignancy by inducing this transition in epithelial and keratinocyte cells [58–60].

With regard to functional enrichment analysis of the top-ranking 100 DM CpG sites, the most significantly enriched genes in warts were associated with SH3 domain binding, namely the Rho GTPase activating protein 31 (ARHGAP31), zinc finger protein 106 (ZNF106), synaptic Ras GTPase-activating protein 1 (SYNGAP1), and citron Rho-interacting serine/threonine kinase (CIT) genes. Despite the fact that the SH3 domain plays a role in a range of different fundamental cellular processes, not much is known about the aforementioned genes in the context of skin pathology or HPV infection [61].

In contrast, pathway analysis revealed that the Rap1 signaling pathway was the most significantly enriched term, which included the RAP1 GTPase activating protein (RAP1GAP), adenylyl cyclase type 9 (ADCY9), signal-induced proliferation-associated 1 like protein 1 (SIPA1L1), Rap guanine nucleotide exchange factor (GEF) 4 (RAPGEF4), and protein kinase D3 (PRKD3) genes. RAP1GAP downregulation via promoter hypermethylation was reported to promote the cell proliferation, survival, and migration of melanoma cells [62]. Moreover, sequence analysis of the high-risk HPV 16 E6-binding protein showed that it had the highest degree of homology with the mammalian Rap1GAP protein [63]. In addition, PRKD3 has been previously reported to have an important role in promoting the growth and progression of invasive breast cancer [64].

Signaling network analysis of the top-ranking 100 CpG sites identified three common regulators: the protein kinase D1 (PRKD1), histone deacetylase 4 (HDAC4), and signal transducer and activator of transcription 5A (STAT5A) genes. The PRKD1 gene plays an integral role in anti-differentiative and proliferative keratinocyte processes, and its aberrant expression has been suggested to have a putative tumorigenic function in the skin [65, 66]. Similarly, the STAT5A gene has been reported to play a major role in the keratinocyte differentiation process [67]. In the context of HPV infection, STAT5A was found to promote HPV viral replication, and STAT-5 isoforms have been indicated to contribute to the progression of HPV-associated cervical cancer [68, 69].

Conclusions

The current study reported a number of novel CpG sites that were differentially methylated in non-genital cutaneous warts compared to normal skin. Such differences in methylation status could be responsible for the HPV-induced wart formation process. The identification of methylation status for the most differentially methylated CpG sites may prove beneficial towards the understanding of the epigenetic factors associated with non-genital cutaneous warts. One limitation of the present study is the relatively small sample size, which may result in sub-optimal statistical power for the genome-wide methylation analysis. Future research is required to validate the results on a larger scale.

Supplementary information

12920_2020_745_MOESM1_ESM.csv (236.4MB, csv)

Additional file 1.Supplementary file. Complete processed methylation data for CpG sites.

Acknowledgements

The authors are grateful to all the participants of this study for their invaluable contribution. The authors also would like to express their gratitude to King Khalid University, Saudi Arabia, for providing administrative and technical support.

Abbreviations

AGRF

Australian Genome Research Facility

BP

Biological process

CC

Cellular component

CpG

5′-C-phosphate-G-3′

DAVID

Database for Annotation, Visualization, and Integrated Discovery

DM

Differentially methylated

DNA

Deoxyribonucleic acid

GO

Gene ontology

HPV

Human papillomavirus

IRB

Institutional Review Board

JUST

Jordan University of Science and Technology

MDS

Multi-dimensional scaling

MF

Molecular function

NS

Normal skin

PCA

Principal component analysis

SIGNOR

Signaling Network Open Resource 2.0

W

Wart

Authors’ contributions

LNA-E designed the method study and supervised the study. LNA-E, AHT and FAA-Q helped in samples and clinical data collection. LNA-E, AHT, MAA and FAA-Q lead the implementation of the method and performed the data analysis. LNA-E, AHT and MAA helped with the interpretation and description of the results and drafted the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Deanship of Research at Jordan University of Science and Technology under grant number (Ref # 177/2017).

Availability of data and materials

The data generated over the course of the present study are available from the corresponding author upon request. However, the complete processed methylation data for the CpG sites is available as a Supplementary file.

Ethics approval and consent to participate

Ethical approval was obtained from the IRB committee at Jordan University of Science and Technology (Ref. # 19/105/2017). All participants gave written informed consent before taking part in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12920-020-00745-6.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12920_2020_745_MOESM1_ESM.csv (236.4MB, csv)

Additional file 1.Supplementary file. Complete processed methylation data for CpG sites.

Data Availability Statement

The data generated over the course of the present study are available from the corresponding author upon request. However, the complete processed methylation data for the CpG sites is available as a Supplementary file.

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