Int J Med Sci 2020; 17(2):191-206. doi:10.7150/ijms.39261

Research Paper

Epigenome-wide analysis of common warts reveals aberrant promoter methylation

Laith N. AL-Eitan1,2 Corresponding address, Mansour A. Alghamdi3, Amneh H. Tarkhan1, Firas A. Al-Qarqaz4,5

1. Department of Applied Biological Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
2. Department of Biotechnology and Genetic Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
3. Department of Anatomy, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia
4. Department of Internal Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan
5. Division of Dermatology, Department of Internal Medicine, King Abdullah University Hospital, Jordan University of Science and Technology, Irbid 22110, Jordan

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
AL-Eitan LN, Alghamdi MA, Tarkhan AH, Al-Qarqaz FA. Epigenome-wide analysis of common warts reveals aberrant promoter methylation. Int J Med Sci 2020; 17(2):191-206. doi:10.7150/ijms.39261. Available from http://www.medsci.org/v17p0191.htm

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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: wart, HPV, methylation, promoter, epigenetics

Introduction

Epigenetics is the study of heritable changes in gene expression that are not caused by changes to the DNA sequence itself, but by covalent modifications such as DNA methylation (DNA-M) [1]. Mammalian DNA-M, which primarily involves the addition of a methyl group to a cytosine base in a CpG dinucleotide, results in increased gene expression when it occurs at higher levels within the gene's body instead of its promoter region [2]. On a similar note, promoter methylation is of particular epigenetic importance because the vast majority of those located upstream of a gene contain a CpG island, the latter of which is a region with a high concentration of CpG sites [3]. In contrast to the hypermethylated CpG sites scattered throughout the human genome, CpG islands are not methylated, and the methylation of CpG islands initiates remodeling mechanisms that ultimately result in gene silencing [4, 5].

The methylation status of promoters is integral to maintaining normal expression levels of the genes they regulate. In fact, promoter hypermethylation is a key part of cancer development and progression, as it results in the silencing of tumor suppressor gene expression [6]. In addition, host promoter hypermethylation has also been implicated in infections by both oncogenic and non-oncogenic viruses such as the human papillomaviruses (HPV) [7]. HPV comprises a family of double-stranded DNA viruses that exclusively infect the basal epithelium of the skin and mucosa [8]. The majority of HPV infections are asymptomatic and resolve without the need for medical intervention but, depending on the individual and the HPV type, can also result in a number of malignancies and dermatological diseases [9]. One such condition is the wart, which arises due to the benign proliferation of HPV-infected epithelial keratinocytes [10]. The most prevalent type of wart is the common wart, which accounts for nearly 70% of all cutaneous warts encountered in clinical settings [11]. As a result of their benign nature, common warts are subject to a much lesser degree of scrutiny than other HPV-associated diseases.

The impermanent nature of cutaneous warts strongly suggests that epigenetic changes are involved in the mechanism of wart formation and their eventual disappearance. However, a paucity of information exists regarding the methylation status of cutaneous warts, especially in the context of the promotor regions. Therefore, the primary objective of the current study was to provide an exploratory survey of the genome-wide changes in promoter methylation patterns in cutaneous warts compared to healthy skin.

Materials and Methods

Study participants

Ethical approval to conduct this study was obtained from Jordan University of Science and Technology's (JUST) Institutional Review Board (IRB). Twelve Arab males presenting with common warts were recruited from the general population after providing written informed consent. Shave biopsies of common warts and adjacent normal skin were performed, allowing paired tissue samples (wart and normal skin) to be obtained from each participant.

Whole genome bisulfite sequencing

A QIAamp DNA Mini Kit (Qiagen, Germany) was used to perform DNA extraction, and optional RNase A digestion was incorporated. DNA purity and integrity were determined by means of the BioTek PowerWave XS2 Spectrophotometer (BioTek Instruments, Inc., USA) and agarose gel electrophoresis, respectively. Genomic DNA that fulfilled our standards for quality and quantity were shipped on dry ice to the Australian Genome Research Facility (AGRF) in Melbourne, where the quality was further ascertained by the QuantiFluor® dsDNA System (Promega, USA). The Zymo EZ DNA Methylation Kit (Zymo Research, USA) was utilized in order to perform bisulfite conversion on the 24 samples. Lastly, the samples were inputted into the Infinium MethylationEPIC BeadChip microarray (Illumina, USA) for a genome-wide interrogation of over 850,000 CpG sites.

Data processing

RnBeads, a computational R package, was adapted to process and analyze the raw intensity data (IDAT files) from the BeadChip [12]. Quality control, preprocessing, batch effects adjustment, and normalization were carried out on all probes and samples according to the RnBeads package pipeline.

Differential methylation and statistical analysis

The mean of the mean β (mean.mean β) values of all the interrogated CpG sites in each promoter were computed. The distribution of CpG sites per promoter is shown in Figure 1, while Figure 2 depicts the distribution of CpG sites across promoters. DM for each promoter was calculated using the following three measures: the mean.mean β difference between warts (W) and normal skin (NS), the log2 of the mean quotient in β means across all CpG sites in a promoter, and the adjusted combined p-value of all CpG sites in the promoter using a limma statistical test [12, 13]. Furthermore, these three measures were used to create a combined ranking, in which promoters that exhibit more DM are assigned a lower combined rank [12]. Promoters were sorted from smallest to largest using the combined rank score, and the top-ranking 1000 DM promoters were selected for further analysis. In order to correct for multiple testing, the Benjamini-Hochberg procedure was utilized to set the false discovery rate (FDR) at 5%.

Gene ontology enrichment analysis

Enrichment analysis for gene ontology (GO) terms associated with the top-ranking 500 DM promoters was performed using the GO consortium [14].

Signaling pathway analysis

A signaling network of the top-ranking 1000 DM promoters was investigated using the SIGnaling Network Open Resource (SIGNOR) 2.0 [15]. Due to the large number of connections, the type of relation was selected to only include 'direct' interactions with a relaxed layout and a score of '0.0'.

Results

Sample clustering based on methylation data

Based on all methylation values of the top-ranking 1000 DM promoters, the 24 samples showed an expected clustering pattern, as samples with similar methylation patterns or phenotypes tended to cluster together (Figure 3). In addition, the dimension reduction test was applied to the dataset using multidimensional scaling (MDS) and principal component analysis (PCA) in order to inspect for a strong signal in the methylation values of the samples (Figures 4 and 5). MDS and PCA confirmed that the difference between wart (W) and normal skin (NS) samples predominated the analysis.

 Figure 1 

Distribution of CpG sites per promoter.

Int J Med Sci Image (Click on the image to enlarge.)
 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.

Int J Med Sci Image (Click on the image to enlarge.)
 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.

Int J Med Sci Image (Click on the image to enlarge.)
 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.

Int J Med Sci Image (Click on the image to enlarge.)
 Figure 5 

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

Int J Med Sci Image (Click on the image to enlarge.)

Differential methylation of promoters

44,929 genomic identifiers passed the quality control and pre-processing steps, including some identifiers that did not map to gene symbols or which were not assigned (NA). Genomic identifiers without symbols were then removed, leaving 27,790 with symbols. The list of DM promoters in warts was limited to the top-ranking 1000 DM promoters using the combined rank score. Using this scoring method, a total of 576 and 424 promoters were found to be hypomethylated and hypermethylated, respectively, in warts (W) compared to normal skin (NS), with a mean β difference =>0.064 and =< -0.064 and p-value =< 0.001 (adjusted p-value =<0.007) (Figure 6). Among the 576 hypomethylated promoters, the β difference ranged from -0.064 to -0.458, while the mean β difference ranged between 0.064 and 0.367 for the 424 hypermethylated promoters. The log2 of the quotient in methylation between warts and normal skin had a maximum value of 1.633 and minimum value of -1.924 (Figure 7). The top-ranking 100 DM promoters with the lowest combined rank score are shown in Table 1.

Gene ontology enrichment analysis

Gene ontology (GO) enrichment analysis of biological process (BP) and molecular function (MF) was conducted on the top-ranking 500 DM hypermethylated promoters (Figure 8, Figure 9, Table 2, and Table 3) and the top-ranking 500 DM hypomethylated promoters (Figure 10, Figure 11, Table 4, and Table 5).

Pathway analysis

Signaling network analysis of the top-ranking 1000 DM promoters illustrated that several promoter genes were common regulators of this gene network, with a minimum of 7 direct connectivities each. These promoter genes include H3F3A, CDKN1A, MAPK13, IKBKG, CAPN2, CAMKK1 and CUL1 (Figure 12). Moreover, H3F3A was found to be the most common regulator when the signaling network analysis was carried out on the top 100 DM promoters.

Discussion

To the best of the authors' knowledge, this is the first study to investigate the genome-wide changes in promoter methylation patterns associated with HPV-induced cutaneous warts. The present findings provide an exploratory analysis that creates clear lines of future research on this topic, especially with regard to validation studies involving larger sample sizes.

In the present study, the most differentially methylated (DM) promoter in warts compared to normal skin was found within the eukaryotic translation initiation factor 3 subunit E pseudogene 2 (EIF3EP2) gene, a pseudogene with no function or association previously reported in the literature. Likewise, little is known about the second most DM gene in warts, the chromosome 9 open reading frame 30 (C9orf30) pseudogene. In contrast, the third most DM gene is the protein-coding cysteinyl leukotriene receptor 1 (CYSLTR1) gene, which is normally involved in allergic and hypersensitive reactions [16]. Variation in the CYSLTR1 gene modulates asthma risk as well as adenoid hypertrophy progression, and it has been implicated in the disease outcome of colorectal, prostate, and squamous cell carcinoma [17-21]. Moreover, CYSLTR1 is highly expressed in the normal human skin epidermis, but its expression was found to be even higher in atopic dermatitis [22]. Table 2 depicts all the protein-coding genes containing DM promoters from among the top-ranking 100 listed in Table 1.

 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).

Int J Med Sci Image (Click on the image to enlarge.)
 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.

Int J Med Sci Image (Click on the image to enlarge.)
 Table 1 

The 100 top-ranking promoters based on combined ranking score.

GeneGene symbolCategoryRNA classChromosomemean.mean β value (NS)mean.mean β value (W)mean.mean β value diff.
(W-NS)
mean.mean. quot.log2comb.p.valcomb.p.adj. (FDR)Combined rank
ENSG00000224674EIF3EP2Pseudogene20.6110.154-0.458-1.9247.832E-163.519E-111
ENSG00000263368AC069366.1Pseudogeneantisense170.2060.5730.3671.4311.336E-132.001E-0914
ENSG00000173198CYSLTR1Protein codingX0.1660.5200.3531.5801.671E-114.415E-0817
ENSG00000266228MIR3611RNA genemiRNA100.4030.128-0.275-1.5841.083E-113.744E-0827
ENSG00000267125AC012615.3RNA gene190.1920.4650.2731.2803.531E-133.410E-0929
ENSG00000270808AC022400.4PseudogenelncRNA100.6910.295-0.396-1.2021.686E-101.756E-0740
ENSG00000241114AC008280.2Pseudogene20.3830.129-0.254-1.5031.876E-101.756E-0747
ENSG00000272156AC008280.3RNA gene20.3830.129-0.254-1.5031.876E-101.756E-0747
ENSG00000207258RF00019RNA geneY RNA10.5080.192-0.315-1.3566.373E-104.522E-0762
ENSG00000226545AL357552.1Pseudogene10.5080.192-0.315-1.3566.373E-104.522E-0762
ENSG00000270002AC022028.2RNA gene100.4580.199-0.259-1.0565.656E-104.246E-0770
ENSG00000227096HMGB3P8Pseudogene100.6530.246-0.408-1.3761.138E-096.141E-0782
ENSG00000250532AC021180.1RNA gene40.6210.233-0.388-1.3781.576E-097.956E-0789
ENSG00000254653AC024475.1RNA gene110.2280.4400.2121.0711.411E-101.756E-0799
ENSG00000265503MIR1269BRNA genemiRNA170.3460.141-0.205-1.2391.140E-096.141E-07109
ENSG00000238024DDX39BP2Pseudogene60.3260.124-0.202-1.3231.391E-097.248E-07113
ENSG00000273044AL022334.2RNA gene220.2430.4810.2380.9561.766E-101.756E-07119
ENSG00000234105AC009468.2RNA gene70.5760.307-0.269-0.9613.100E-091.151E-06121
ENSG00000188373C10orf99Protein coding100.4000.202-0.198-0.9825.786E-104.262E-07124
ENSG00000271597AC112230.1PseudogenelncRNA20.3060.5940.2870.9333.019E-091.151E-06136
ENSG00000271265AL355297.3RNA genelncRNA60.3470.6670.3200.9241.317E-113.856E-08145
ENSG00000244286ITGB5-AS1RNA genencRNA30.2020.3930.1901.2238.912E-123.640E-08152
ENSG00000226403AL392089.1RNA gene90.0800.2690.1891.6331.086E-126.969E-09154
ENSG00000234936AC010883.1RNA gene20.2880.4980.2100.9092.020E-114.908E-08158
ENSG00000203527Z99756.1RNA genencRNA220.3850.198-0.187-0.9065.290E-091.674E-06161
ENSG00000242147AL365356.5RNA genencRNA100.3340.148-0.186-1.2633.506E-117.161E-08166
ENSG00000270781AC091133.5Pseudogene170.4160.219-0.197-0.8964.980E-103.925E-07170
ENSG00000250282AC002401.2RNA gene170.2250.4430.2170.8943.488E-091.234E-06174
ENSG00000255158AC131934.1RNA gene110.2990.5900.2910.9771.016E-082.625E-06174
ENSG00000232486AL592437.2Pseudogene90.6660.354-0.312-0.8921.930E-098.758E-07175
ENSG00000262067AC120057.1PseudogenelncRNA170.5050.171-0.333-1.5051.139E-082.816E-06181
ENSG00000266258LINC01909RNA genencRNA180.6290.299-0.330-1.0481.479E-083.408E-06195
ENSG00000257496AC025031.1RNA gene120.2170.3970.1800.9821.649E-083.703E-06200
ENSG00000185479KRT6BProtein-coding120.3400.166-0.174-1.0457.004E-111.124E-07216
ENSG00000270255AC009884.2Pseudogene80.2790.5290.2500.9002.221E-084.587E-06217
ENSG00000167751KLK2Protein coding190.3280.136-0.192-1.2142.772E-085.463E-06227
ENSG00000268518AC020909.2RNA gene190.4320.238-0.194-0.8392.897E-116.197E-08229
ENSG00000243795LINC02044RNA genencRNA30.3870.6630.2760.8251.229E-113.856E-08246
ENSG00000267632AC067852.3RNA genelncRNA170.4020.7190.3160.8211.561E-101.756E-07254
ENSG00000259265AC027088.3RNA gene150.3620.195-0.167-0.9182.209E-084.587E-06260
ENSG00000264733MIR4718RNA genemiRNA160.3420.176-0.166-0.9221.674E-098.164E-07263
ENSG00000253630AC026407.1Pseudogeneantisense50.5370.301-0.236-0.8151.148E-096.141E-07264
ENSG00000228918LINC01344RNA genencRNA10.1800.3460.1660.9084.232E-103.475E-07264
ENSG00000232878DPYD-AS1RNA genencRNA10.5720.387-0.185-0.8154.216E-087.523E-06265
ENSG00000112769LAMA4Protein coding60.3250.5120.1870.8103.888E-087.072E-06269
ENSG00000237126AC073254.1RNA gene20.3680.202-0.166-0.8351.684E-083.745E-06270
ENSG00000256746AC018410.1RNA genencRNA110.3440.5360.1920.8072.084E-099.002E-07271
ENSG00000232560C21orf37RNA genencRNA210.3000.4950.1950.8055.048E-088.338E-06274
ENSG00000198796ALPK2Protein coding180.1650.3290.1630.9241.006E-082.622E-06286
ENSG00000185432METTL7AProtein coding120.3890.6730.2830.7957.318E-135.480E-09286
ENSG00000087076HSD17B14Protein coding190.1450.3460.2011.1685.925E-089.251E-06287
ENSG00000239255AC007620.1Pseudogene30.3470.5750.2271.0856.353E-089.775E-06292
ENSG00000230403LINC01066RNA genencRNA130.4750.302-0.173-0.9028.129E-081.192E-05306
ENSG00000132475H3F3BProtein coding170.1730.3580.1841.0038.349E-081.218E-05308
ENSG00000258274AC012085.2RNA genencRNA120.4150.6240.2080.7856.143E-111.062E-07308
ENSG00000244167AC005532.2PseudogenelncRNA70.4880.281-0.207-0.7754.380E-087.657E-06324
ENSG00000266740MIR4708RNA genemiRNA140.2400.4160.1770.7714.844E-103.887E-07328
ENSG00000258657AL136018.1RNA gene140.4480.234-0.213-0.9461.163E-071.588E-05329
ENSG00000186715MST1LProtein coding10.3000.145-0.156-1.0066.774E-111.124E-07335
ENSG00000253543AC083923.1Pseudogene80.2770.121-0.156-1.1276.441E-104.522E-07339
ENSG00000261095AC136285.1RNA genencRNA160.4870.272-0.215-0.9581.368E-071.803E-05341
ENSG00000213316LTC4SProtein coding50.2110.3650.1541.0751.025E-082.631E-06348
ENSG00000267299AC011444.3RNA gene190.1410.3000.1590.7522.936E-085.687E-06352
ENSG00000234502FYTTD1P1Pseudogene90.3610.180-0.182-0.9691.678E-072.118E-05356
ENSG00000265666RARA-AS1RNA genencRNA170.1890.3390.1510.8681.088E-071.501E-05370
ENSG00000182264IZUMO1Protein coding190.3080.4680.1600.7373.157E-085.910E-06376
ENSG00000254113AC090193.2RNA gene80.2430.4190.1770.7361.371E-083.276E-06378
ENSG00000204933CD177P1Pseudogene190.3750.6070.2320.7332.798E-091.103E-06382
ENSG00000110203FOLR3Protein coding110.5360.357-0.180-0.7461.990E-072.311E-05387
ENSG00000266964FXYD1Protein coding190.2990.4520.1540.7313.068E-091.151E-06391
ENSG00000221857AC020907.2RNA gene190.2990.4520.1540.7313.068E-091.151E-06391
ENSG00000213417KRTAP2-4Protein coding170.4710.309-0.163-0.8552.328E-072.604E-05401
ENSG00000254853AP004247.1Pseudogene110.2470.100-0.147-1.2213.631E-091.265E-06410
ENSG00000283664; ENSG00000265375MIR4679-1; MIR4679-2RNA genemiRNA100.3530.5890.2360.7221.858E-101.756E-07410
ENSG00000261257AP000821.1RNA genelncRNA110.3940.5430.1490.7462.524E-072.757E-05411
ENSG00000204880KRTAP4-8Protein coding170.3560.198-0.158-0.8232.945E-073.114E-05425
ENSG00000215930MIR942RNA genemiRNA10.4100.266-0.144-0.7828.361E-092.305E-06427
ENSG00000271680AC098935.2Pseudogeneantisense10.2440.100-0.144-1.2114.741E-087.978E-06428
ENSG00000258380AL356805.1RNA gene140.2920.4350.1441.0432.010E-084.354E-06432
ENSG00000249717AC110760.2RNA genencRNA40.4800.6940.2130.7075.817E-089.171E-06436
ENSG00000265462; ENSG00000266758MIR3680-1; MIR3680-2RNA genemiRNA160.3830.6300.2470.7058.156E-105.161E-07438
ENSG00000263361MIR378HRNA genemiRNA50.4110.268-0.143-0.7317.162E-081.080E-05443
ENSG00000249483AC026726.1RNA genelncRNA50.1140.2570.1420.8521.524E-083.459E-06446
ENSG00000227735CYCSP5Pseudogeneantisense10.2120.070-0.142-1.4782.842E-091.110E-06449
ENSG00000267130AC008738.2RNA gene190.1630.3100.1460.6983.087E-085.835E-06449
ENSG00000269480AC020913.2RNA gene190.3880.226-0.162-0.7554.211E-073.933E-05481
ENSG00000260673AL034376.1RNA gene60.3920.254-0.139-0.7038.046E-081.187E-05482
ENSG00000261392AC087190.2RNA gene160.7350.481-0.255-0.6812.657E-072.869E-05488
ENSG00000196344ADH7Protein coding40.2900.152-0.138-0.9362.488E-072.740E-05488
ENSG00000170454KRT75Protein coding120.4670.293-0.175-0.7294.448E-074.087E-05489
ENSG00000254175IGLVI-42Pseudogene220.2320.095-0.137-1.2011.475E-101.756E-07498
ENSG00000254073IGLVVII-41-1Pseudogene220.2320.095-0.137-1.2011.475E-101.756E-07498
ENSG00000253947AC008705.1RNA gene50.3930.5820.1890.6771.507E-083.438E-06498
ENSG00000275874PICSARRNA genencRNA210.4670.318-0.150-0.6752.541E-072.757E-05503
ENSG00000233930KRTAP5-AS1RNA genencRNA110.1620.2980.1360.9441.138E-082.816E-06503
ENSG00000188100FAM25AProtein coding100.3890.254-0.135-0.6881.426E-071.862E-05509
ENSG00000261078AC009118.1RNA gene160.2500.115-0.135-1.0082.220E-084.587E-06513
ENSG00000259195AC021739.1Pseudogene150.2840.149-0.134-0.9274.219E-087.523E-06519
ENSG00000260905AC009021.1RNA gene160.6160.384-0.232-0.6678.523E-081.235E-05523
ENSG00000006831ADIPOR2Protein coding120.7210.501-0.220-0.6671.007E-071.410E-05527
 Figure 8 

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

Int J Med Sci Image (Click on the image to enlarge.)
 Figure 9 

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

Int J Med Sci Image (Click on the image to enlarge.)
 Figure 10 

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

Int J Med Sci Image (Click on the image to enlarge.)
 Table 2 

Function and clinical relevance of the protein-coding genes containing the most differentially methylated promoters in warts

Gene symbolGene nameMain physiological function
CYSLTR1Cysteinyl leukotriene receptor 1Mediates bronchoconstriction
C10orf99Chromosome 10 Open Reading Frame 99Mediates recruitment of lymphocytes to epithelia
KRT6BKeratin 6BEpithelial wound repair and inflammation
KLK2Kallikrein Related Peptidase 2Sperm liquefication
LAMA4Laminin Subunit Alpha 4Cell adhesion, differentiation, and migration
ALPK2Alpha Kinase 2Unknown
METTL7AMethyltransferase Like 7AUnknown
HSD17B1417β-Hydroxysteroid dehydrogenase type 14Steroid metabolism
H3F3BH3 Histone Family Member 3BFound at sites of nucleosomal displacement
MST1LMacrophage Stimulating 1 LikeUnknown
LTC4SLeukotriene C4 SynthaseInvolved in cysteinyl leukotriene biosynthesis
IZUMO1Izumo sperm-egg fusion 1Essential for fusion and binding of sperm and egg
FOLR3Folate receptor 3Mediate delivery of 5-methyltetrahydrofolate to cell interior
FXYD1FXYD Domain Containing Ion Transport Regulator 1Regulates ion channel activity
KRTAP2-4Keratin Associated Protein 2-4Involved in hair formation
KRTAP4-8Keratin Associated Protein 4-8Involved in hair formation
ADH7Alcohol dehydrogenase 7Functions in retinoic acid synthesis
KRT75Keratin 75Involved in hair and nail formation
FAM25AFamily with sequence similarity 25 member AUnknown
ADIPOR2Adiponectin receptor 2Regulates glucose and lipid metabolism
 Table 3 

GO enrichment analysis showing the significant biological processes (BP) of the top 500 hypermethylated promoters.

GOMFIDP-valueOdds ratioExpCountCountSizeTerm
GO:0009913011.32152.108119328epidermal cell differentiation
GO:004358809.32762.673720409skin development
GO:0070268014.80630.71269110cornification
GO:0031424013.94090.6648111keratinization
GO:006042903.71638.1779251251epithelium development
GO:004274208.03561.562411239defense response to bacterium
GO:003015402.559726.423494042cell differentiation
GO:000695906.71291.49059228humoral immune response
GO:005170703.55295.458517835response to other organism
GO:00704880Inf0.013122neutrophil aggregation
GO:0031581058.55960.0719311hemidesmosome assembly
GO:00096071e-043.36225.746117879response to biotic stimulus
GO:00487311e-042.172930.6198504684system development
GO:00508321e-0417.9860.2549439defense response to fungus
GO:00325022e-042.027140.0921596133developmental process
GO:00164772e-042.6399.1062211393cell migration
GO:00906303e-049.54330.5753588activation of GTPase activity
GO:00618445e-0411.86470.3726457antimicrobial humoral immune response mediated by antimicrobial peptide
GO:00096058e-042.50578.646191419response to external stimulus
GO:00516748e-042.37810.0018211530localization of cell
GO:00071559e-042.46998.6421191322cell adhesion
GO:00313380.0019.8190.4445468regulation of vesicle fusion
GO:00975300.0017.05990.76485117granulocyte migration
GO:00023760.00142.002618.1078312770immune system process
GO:00025230.001838.67820.0654210leukocyte migration involved in inflammatory response
GO:00305950.00194.96541.29436198leukocyte chemotaxis
GO:00400110.0022.160211.4792221756locomotion
GO:19049950.002234.37860.0719211negative regulation of leukocyte adhesion to vascular endothelial cell
GO:00451040.002312.63960.2615340intermediate filament cytoskeleton organization
GO:00305930.00257.56260.5687487neutrophil chemotaxis
GO:00033340.002730.93890.0784212keratinocyte development
GO:00321190.002730.93890.0784212sequestering of zinc ion
GO:00082190.00292.005614.1398252163cell death
GO:00308560.0035.44240.98065150regulation of epithelial cell differentiation
GO:00181190.003228.12460.085213peptidyl-cysteine S-nitrosylation
GO:00344970.003228.12460.085213protein localization to phagophore assembly site
GO:00321010.00342.67784.857112743regulation of response to external stimulus
GO:00224080.00365.22421.01985156negative regulation of cell-cell adhesion
GO:00069280.00451.97913.0285231993movement of cell or subcellular component
GO:00069350.00492.81243.817710584chemotaxis
GO:00450870.00512.43535.785313885innate immune response
GO:00033360.0065Inf0.006511corneocyte desquamation
GO:00215930.0065Inf0.006511rhombomere morphogenesis
GO:00216600.0065Inf0.006511rhombomere 3 formation
GO:00216660.0065Inf0.006511rhombomere 5 formation
GO:00330370.0065Inf0.006511polysaccharide localization
GO:00345160.0065Inf0.006511response to vitamin B6
GO:00356440.0065Inf0.006511phosphoanandamide dephosphorylation
GO:00434200.0065Inf0.006511anthranilate metabolic process
GO:00456600.0065Inf0.006511positive regulation of neutrophil differentiation
GO:00720460.0065Inf0.006511establishment of planar polarity involved in nephron morphogenesis
GO:00727400.0065Inf0.006511cellular response to anisomycin
GO:19057160.0065Inf0.006511negative regulation of cornification
GO:00069500.0081.693824.6188363766response to stress
GO:19030360.00817.78360.4118363positive regulation of response to wounding
GO:00507290.00825.3540.7914121positive regulation of inflammatory response
GO:00305390.008216.27490.1373221male genitalia development
GO:19028070.00875.26340.80414123negative regulation of cell cycle G1/S phase transition
GO:00456060.009814.72310.1504223positive regulation of epidermal cell differentiation
GO:00017750.00992.05638.459161294cell activation
 Figure 11 

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

Int J Med Sci Image (Click on the image to enlarge.)
 Figure 12 

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

Int J Med Sci Image (Click on the image to enlarge.)
 Table 4 

GO enrichment analysis showing the significant molecular functions (MF) of the top 500 hypermethylated promoters.

GOMFIDP-valueOdds ratioExpCountCountSizeTerm
GO:0050786099.3750.0655411RAGE receptor binding
GO:00171371e-047.23371.06537179Rab GTPase binding
GO:00356621e-04340.83670.017923Toll-like receptor 4 binding
GO:00505443e-04113.59860.029825arachidonic acid binding
GO:00052004e-048.91730.6135103structural constituent of cytoskeleton
GO:00452940.001937.85260.0655211alpha-catenin binding
GO:00360410.002234.06530.0714212long-chain fatty acid binding
GO:00081460.003510.73070.3035351sulfotransferase activity
GO:00018560.006Inf0.00611complement component C5a binding
GO:00051300.006Inf0.00611granulocyte colony-stimulating factor receptor binding
GO:00304290.006Inf0.00611kynureninase activity
GO:00364580.006Inf0.00611hepatocyte growth factor binding
GO:00478880.006Inf0.00611fatty acid peroxidase activity
GO:00619810.006Inf0.006113-hydroxykynureninase activity
GO:19015670.009614.79950.1488225fatty acid derivative binding
 Table 5 

GO enrichment analysis showing the significant biological processes (BP) of the top 500 hypomethylated promoters.

GOMFIDP-valueOdds ratioExpCountCountSizeTerm
GO:19017500102.8210.078948leukotriene D4 biosynthetic process
GO:0006751082.25190.088849glutathione catabolic process
GO:0006691021.52820.2861529leukotriene metabolic process
GO:00464561e-0412.5890.4538546icosanoid biosynthetic process
GO:00515723e-04203.17070.029623negative regulation of histone H3-K4 methylation
GO:00065754e-044.97081.73638176cellular modified amino acid metabolic process
GO:00722686e-04101.57930.039524pattern specification involved in metanephros development
GO:00487629e-044.32031.98298201mesenchymal cell differentiation
GO:00720819e-0467.71540.049325specification of nephron tubule identity
GO:00226120.00125.49161.17396119gland morphogenesis
GO:00400120.00122.29889.440920957regulation of locomotion
GO:00301550.00152.58316.234715632regulation of cell adhesion
GO:00303340.00162.34358.286718840regulation of cell migration
GO:00518930.00188.37090.5229453regulation of focal adhesion assembly
GO:00171440.00182.37737.694817780drug metabolic process
GO:00482930.00240.62440.069127regulation of isotype switching to IgE isotypes
GO:00860360.00240.62440.069127regulation of cardiac muscle cell membrane potential
GO:00324120.0023.74932.2698230regulation of ion transmembrane transporter activity
GO:00335980.002312.75840.2664327mammary gland epithelial cell proliferation
GO:00714930.002633.85160.078928cellular response to UV-B
GO:19020410.00277.4550.582459regulation of extrinsic apoptotic signaling pathway via death domain receptors
GO:00351480.0034.5231.41076143tube formation
GO:00160640.00315.41550.98655100immunoglobulin mediated immune response
GO:00336890.003329.01390.088829negative regulation of osteoblast proliferation
GO:00458690.003329.01390.088829negative regulation of single stranded viral RNA replication via double stranded DNA intermediate
GO:00703830.003329.01390.088829DNA cytosine deamination
GO:00720480.003329.01390.088829renal system pattern specification
GO:00512700.00342.13289.57919971regulation of cellular component movement
GO:00430010.003510.93320.3058331Golgi to plasma membrane protein transport
GO:00324090.00353.4212.47618251regulation of transporter activity
GO:00715260.003810.55550.3157332semaphorin-plexin signaling pathway
GO:00436480.00385.14321.03585105dicarboxylic acid metabolic process
GO:00017560.00416.61050.6511466somitogenesis
GO:00099720.004125.38570.0987210cytidine deamination
GO:00460870.004125.38570.0987210cytidine metabolic process
GO:00355100.004110.20310.3255333DNA dealkylation
GO:00488700.00431.871615.0936261530cell motility
GO:00709880.00486.30430.6807469demethylation
GO:00303070.00484.07291.55876158positive regulation of cell growth
GO:00347540.00494.85031.0955111cellular hormone metabolic process
GO:00607660.00522.56370.1085211negative regulation of androgen receptor signaling pathway
GO:00070450.00566.02510.7103472cell-substrate adherens junction assembly
GO:00604290.00581.918412.3412221251epithelium development
GO:00018670.00620.30610.1184212complement activation, lectin pathway
GO:00165540.00620.30610.1184212cytidine to uridine editing
GO:00461330.00620.30610.1184212pyrimidine ribonucleoside catabolic process
GO:00725200.00620.30610.1184212seminiferous tubule development
GO:00485130.00671.579433.344473380animal organ development
GO:00321010.00682.17417.329815743regulation of response to external stimulus
GO:00018380.0074.42951.19375121embryonic epithelial tube formation
GO:00459950.0074.42951.19375121regulation of embryonic development
GO:00105660.00718.4590.1282213regulation of ketone biosynthetic process
GO:00026990.0073.74911.68696171positive regulation of immune effector process
GO:00160530.00762.60594.044710410organic acid biosynthetic process
GO:00456680.00768.05120.4045341negative regulation of osteoblast differentiation
GO:00903820.00768.05120.4045341phagosome maturation
GO:00507720.00775.46040.7793479positive regulation of axonogenesis
GO:19018880.00815.38820.7892480regulation of cell junction assembly
GO:00007220.008116.91970.1381214telomere maintenance via recombination
GO:00424460.00845.31790.7991481hormone biosynthetic process
GO:00016670.00852.564.113710417ameboidal-type cell migration
GO:00302780.00923.53271.78566181regulation of ossification
GO:00109590.00922.67933.53179358regulation of metal ion transport
GO:19040620.00942.86842.92998297regulation of cation transmembrane transport
GO:00004150.0099Inf0.009911negative regulation of histone H3-K36 methylation
GO:00031470.0099Inf0.009911neural crest cell migration involved in heart formation
GO:00302090.0099Inf0.009911dermatan sulfate catabolic process
GO:00357130.0099Inf0.009911response to nitrogen dioxide
GO:00443450.0099Inf0.009911stromal-epithelial cell signaling involved in prostate gland development
GO:00469010.0099Inf0.009911tetrahydrofolylpolyglutamate biosynthetic process
GO:00486940.0099Inf0.009911positive regulation of collateral sprouting of injured axon
GO:00509280.0099Inf0.009911negative regulation of positive chemotaxis
GO:00605980.0099Inf0.009911dichotomous subdivision of terminal units involved in mammary gland duct morphogenesis
GO:00617130.0099Inf0.009911anterior neural tube closure
GO:00617670.0099Inf0.009911negative regulation of lung blood pressure
GO:00712500.0099Inf0.009911cellular response to nitrite
GO:00719540.0099Inf0.009911chemokine (C-C motif) ligand 11 production
GO:00721680.0099Inf0.009911specification of anterior mesonephric tubule identity
GO:00721690.0099Inf0.009911specification of posterior mesonephric tubule identity
GO:00721840.0099Inf0.009911renal vesicle progenitor cell differentiation
GO:00722590.0099Inf0.009911metanephric interstitial fibroblast development
GO:00902460.0099Inf0.009911convergent extension involved in somitogenesis
GO:00987490.0099Inf0.009911cerebellar neuron development
GO:19002810.0099Inf0.009911positive regulation of CD4-positive, alpha-beta T cell costimulation
GO:19043280.0099Inf0.009911regulation of myofibroblast contraction
GO:19046350.0099Inf0.009911positive regulation of glomerular visceral epithelial cell apoptotic process
GO:19048770.0099Inf0.009911positive regulation of DNA ligase activity
GO:19055800.0099Inf0.009911positive regulation of ERBB3 signaling pathway
GO:19059430.0099Inf0.009911negative regulation of formation of growth cone in injured axon
GO:20000800.0099Inf0.009911negative regulation of canonical Wnt signaling pathway involved in controlling type B pancreatic cell proliferation
GO:20001840.0099Inf0.009911positive regulation of progesterone biosynthetic process
GO:20005720.0099Inf0.009911positive regulation of interleukin-4-dependent isotype switching to IgE isotypes
 Table 6 

GO enrichment analysis showing the significant molecular functions (MF) of the top 500 hypomethylated promoters.

GOMFIDP-valueOdds ratioExpCountCountSizeTerm
GO:00363740105.30380.057536glutathione hydrolase activity
GO:00478440.001941.85160.067127deoxycytidine deaminase activity
GO:00009790.00459.8550.3354335RNA polymerase II core promoter sequence-specific DNA binding
GO:00041260.005720.91950.115212cytidine deaminase activity
GO:00506810.00758.08280.4025342androgen receptor binding
GO:00314920.00917.50410.4312345nucleosomal DNA binding
GO:00039400.0096Inf0.009611L-iduronidase activity
GO:00043260.0096Inf0.009611tetrahydrofolylpolyglutamate synthase activity
GO:00044410.0096Inf0.009611inositol-1,4-bisphosphate 1-phosphatase activity
GO:00087250.0096Inf0.009611DNA-3-methyladenine glycosylase activity
GO:00088290.0096Inf0.009611dCTP deaminase activity
GO:00088410.0096Inf0.009611dihydrofolate synthase activity
GO:00319620.0096Inf0.009611mineralocorticoid receptor binding
GO:00345120.0096Inf0.009611box C/D snoRNA binding
GO:00439160.0096Inf0.009611DNA-7-methylguanine glycosylase activity
GO:00506490.0096Inf0.009611testosterone 6-beta-hydroxylase activity
GO:00528210.0096Inf0.009611DNA-7-methyladenine glycosylase activity
GO:00528220.0096Inf0.009611DNA-3-methylguanine glycosylase activity
GO:00528290.0096Inf0.009611inositol-1,3,4-trisphosphate 1-phosphatase activity
GO:00860380.0096Inf0.009611calcium:sodium antiporter activity involved in regulation of cardiac muscle cell membrane potential
GO:00316250.00962.85782.94178307ubiquitin protein ligase binding
 Figure 13 

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

Int J Med Sci Image (Click on the image to enlarge.)

Among the protein-coding genes, C10orf99 and KRT6B promoters exhibited high levels of differential methylation in warts. The chromosome 10 open reading frame 99 (C10orf99) gene encodes for an antimicrobial peptide that is widely expressed in the skin and digestive tract [23]. In a pathologic context, C10orf99 was determined to contribute to psoriasis development by promoting keratinocyte proliferation [24, 25]. Likewise, the keratin 6B (KRT6B) gene encodes for a type II keratin that is normally present in mammalian epithelial cells and is rapidly induced in human keratinocytes after skin wounding [26]. KRT6B has been identified as a potential biomarker for differentiating between lung adenocarcinoma and lung squamous cell carcinoma, and its increased expression is associated with lower disease-free survival rates in young breast cancer patients [27, 28]. Mutations in the KRT6B gene result in an autosomal dominant skin disorder known as pachyonychia congenita, which involves plantar keratoderma and pain alongside thickened toenails [29]. In contrast, two of the most differentially methylated protein-coding promoters, namely the kallikrein related peptidase 2 (KLK2) and Izumo sperm-egg fusion 1 (IZUMO1) genes, are integral for sperm function. KLK2 over-expression has been associated with the promotion of prostate cancer cell growth [30].

As previously mentioned, the ephemeral nature of warts hints towards the involvement of an epigenetic component. Correspondingly, some of the most DM promoters were found within the laminin subunit alpha 4 (LAMA4) and H3 histone family member 3B (H3F3B) genes, which are responsible for cell differentiation and nucleosomal displacement, respectively [31, 32]. In certain breast cancer subtypes, increased LAMA4 expression was noted to contribute to the chromatin remodeling mechanisms that are a part of cancer progression [33]. Moreover, atypical HF3B expression was reported to be associated with colorectal cancer and chondroblastoma [34, 35]. On a similar note, epigenetic modifications have been linked to changes in metabolism in a number of different non-communicable diseases, including cancer and diabetes [36]. In the present study, promoters were differentially methylated within the 17β-hydroxysteroid dehydrogenase type 14 (HSD17B14), leukotriene C4 synthase (LTC4S), folate receptor 3 (FOLR3), alcohol dehydrogenase 7 (ADH7), and adiponectin receptor 2 (ADIPOR2) genes that are involved in steroid, eicosanoid, folate, retinol, and glucose and lipid metabolism, respectively [37-41]. Like the CYSLTR1 gene, LTC4S polymorphisms were associated with asthma risk and drug responsiveness in different ethnic populations [42-45].

Pathway analysis demonstrated that the most common regulator among the top-ranking 1000 DM promoters was the H3 histone family member 3A (H3F3A) gene. Like the H3F3B gene, H3F3A encodes for a histone variant and is involved in transcriptional regulation [46]. Aberrant H3F3A expression has been associated with the promotion of pediatric and adolescent cancers as well as lung cancer cell migration [46, 47]. The second most common regulator was the cyclin dependent kinase inhibitor 1A (CDKN1A) gene, which is mostly involved in CDK2 inhibition and is a primary target of p53 activity [48]. The CDKN1A gene was associated with better patient survival in HPV-related oropharyngeal squamous cell carcinoma [49]. The third most common regulator in HPV-induced warts is the mitogen-activated protein kinase 13 (MAPK13) gene. MAPK13 is a member of the MAP kinase family and functions to regulate cellular responses to a range of different stimuli, especially in the context of keratinocyte apoptosis and skin homeostasis [50]. Analysis of genome-wide promoter methylation revealed that MAPK13 was hypermethylated in the majority of primary and metastatic melanomas [51]. MAPK13 was also found to be hypermethylated in esophageal squamous cell carcinoma [52].

In summary, it is apparent that HPV-induced warts possess certain patterns of promoter methylation that could be responsible for their formation and maintenance. One limitation of the current study is that it is not possible at this stage to determine whether the differential methylation occurred as a result of the host cells' response to infection or due to HPV-driven processes responsible for wart formation and progression. Future research is required in order to assess the functional and clinical importance of the hypo- and hypermethylated promoter sites as well as to determine the exact nature of this differential methylation.

Acknowledgements

This work was supported by the Deanship of Research at Jordan University of Science and Technology under grant number 184/2017. The authors would like to express their gratitude to King Khalid University, Saudi Arabia, for providing administrative and technical support.

Ethics Committee Approval and Patient Consent

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

Competing Interests

The authors have declared that no competing interest exists.

References

1. Lim DHK, Maher ER. DNA methylation: a form of epigenetic control of gene expression. Obstet Gynaecol. 2010;12:37-42

2. Wagner JR, Busche S, Ge B. et al. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15:R37

3. Deaton AM, Bird A. CpG islands and the regulation of transcription. Genes Dev. 2011;25:1010-22

4. Takai D, Jones PA. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci. 2002;99:3740-3745

5. Jones PA, Baylin SB. The Epigenomics of Cancer. Cell. 2007;128:683-692

6. Brooks J, Cairns P, Zeleniuch-Jacquotte A. Promoter methylation and the detection of breast cancer. Cancer Causes Control. 2009;20:1539-50

7. Kuss-Duerkop SK, Westrich JA, Pyeon D. DNA Tumor Virus Regulation of Host DNA Methylation and Its Implications for Immune Evasion and Oncogenesis. Viruses. 2018 [Epub ahead of print]

8. McKinney C, Hussmann K, McBride A. et al. The Role of the DNA Damage Response throughout the Papillomavirus Life Cycle. Viruses. 2015;7:2450-2469

9. Ljubojevic S, Skerlev M. HPV-associated diseases. Clin Dermatol. 2014;32:227-234

10. Lacarrubba F, Verzì AE, Quattrocchi E. et al. Cutaneous and Anogenital Warts. Atlas of Pediatric Dermatoscopy. Cham: Springer International Publishing. 2018:35-44

11. Plasencia JM. Cutaneous warts: diagnosis and treatment. Prim Care. 2000;27:423-34

12. Assenov Y, Müller F, Lutsik P. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods. 2014;11:1138-1140

13. Ritchie ME, Phipson B, Wu D. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47-e47

14. The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017;45:D331-D338

15. Perfetto L, Briganti L, Calderone A. et al. SIGNOR: a database of causal relationships between biological entities. Nucleic Acids Res. 2016;44:D548-D554

16. Jiang Y, Borrelli LA, Kanaoka Y. et al. CysLT2 receptors interact with CysLT1 receptors and down-modulate cysteinyl leukotriene dependent mitogenic responses of mast cells. Blood. 2007;110:3263-70

17. Gao W, Li J, Li Q. et al. CYSLTR1 promotes adenoid hypertrophy by activating ERK1/2. Exp Ther Med. 2018;16:966-970

18. Hong X, Zhou H, Tsai H-J. et al. Cysteinyl leukotriene receptor 1 gene variation and risk of asthma. Eur Respir J. 2009;33:42-8

19. McGovern T, Goldberger M, Chen M. et al. CysLT1 Receptor Is Protective against Oxidative Stress in a Model of Irritant-Induced Asthma. J Immunol. 2016;197:266-77

20. Öhd JF, Nielsen CK, Campbell J. et al. Expression of the leukotriene D4 receptor CysLT1, COX-2, and other cell survival factors in colorectal adenocarcinomas. Gastroenterology. 2003;124:57-70

21. Bai S, Zhang P, Zhang J-C. et al. A gene signature associated with prognosis and immune processes in head and neck squamous cell carcinoma. Head Neck. 2019 [Epub ahead of print]

22. Hussain I, Kitagaki K, Businga TR. et al. Expression of cysteinyl leukotriene receptor-1 in skin. J Am Acad Dermatol. 2004;51:1032-1033

23. Yang M, Tang M, Ma X. et al. AP-57/C10orf99 is a new type of mutifunctional antimicrobial peptide. Biochem Biophys Res Commun. 2015;457:347-352

24. Roberson EDO, Liu Y, Ryan C. et al. A Subset of Methylated CpG Sites Differentiate Psoriatic from Normal Skin. J Invest Dermatol. 2012;132:583-592

25. Chen C, Wu N, Duan Q. et al. C10orf99 contributes to the development of psoriasis by promoting the proliferation of keratinocytes. Sci Rep. 2018;8:8590

26. Moll R, Divo M, Langbein L. The human keratins: biology and pathology. Histochem Cell Biol. 2008;129:705-733

27. Xiao J, Lu X, Chen X. et al. Eight potential biomarkers for distinguishing between lung adenocarcinoma and squamous cell carcinoma. Oncotarget. 2017;8:71759-71771

28. Johnson RH, Hu P, Fan C. et al. Gene expression in “young adult type” breast cancer: a retrospective analysis. Oncotarget. 2015;6:13688

29. Cao L-H, Luo Y, Wen W. et al. A novel frameshift mutation in keratin 16 underlies pachyonychia congenita with focal palmoplantar keratoderma. Br J Dermatol. 2011;165:1145-7

30. Shang Z, Niu Y, Cai Q. et al. Human kallikrein 2 (KLK2) promotes prostate cancer cell growth via function as a modulator to promote the ARA70-enhanced androgen receptor transactivation. Tumor Biol. 2014;35:1881-1890

31. Bush KM, Yuen BT, Barrilleaux BL. et al. Endogenous mammalian histone H3.3 exhibits chromatin-related functions during development. Epigenetics Chromatin. 2013;6:7

32. Shan N, Zhang X, Xiao X. et al. Laminin α4 (LAMA4) expression promotes trophoblast cell invasion, migration, and angiogenesis, and is lowered in preeclamptic placentas. Placenta. 2015;36:809-820

33. Triulzi T, Casalini P, Sandri M. et al. Neoplastic and Stromal Cells Contribute to an Extracellular Matrix Gene Expression Profile Defining a Breast Cancer Subtype Likely to Progress. PLoS One. 2013;8:e56761

34. Behjati S, Tarpey PS, Presneau N. et al. Distinct H3F3A and H3F3B driver mutations define chondroblastoma and giant cell tumor of bone. Nat Genet. 2013;45:1479-1482

35. Ayoubi HA, Mahjoubi F, Mirzaei R. Investigation of the human H3.3B (H3F3B) gene expression as a novel marker in patients with colorectal cancer. J Gastrointest Oncol. 2017;8:64-69

36. Tzika E, Dreker T, Imhof A. Epigenetics and Metabolism in Health and Disease. Front Genet. 2018;9:361

37. Lukacik P, Kavanagh KL, Oppermann U. Structure and function of human 17β-hydroxysteroid dehydrogenases. Mol Cell Endocrinol. 2006;248:61-71

38. Haeggström JZ, Rinaldo-Matthis A, Wheelock CE. et al. Advances in eicosanoid research, novel therapeutic implications. Biochem Biophys Res Commun. 2010;396:135-139

39. O'Byrne MR, Au KS, Morrison AC. et al. Association of folate receptor (FOLR1, FOLR2, FOLR3) and reduced folate carrier (SLC19A1) genes with meningomyelocele. Birth Defects Res A Clin Mol Teratol. 2010;88:689-94

40. Kumar S, Sandell LL, Trainor PA. et al. Alcohol and aldehyde dehydrogenases: retinoid metabolic effects in mouse knockout models. Biochim Biophys Acta. 2012;1821:198-205

41. Tao C, Sifuentes A, Holland WL. Regulation of glucose and lipid homeostasis by adiponectin: effects on hepatocytes, pancreatic β cells and adipocytes. Best Pract Res Clin Endocrinol Metab. 2014;28:43-58

42. Kang M-J, Kwon J-W, Kim B-J. et al. Polymorphisms of the PTGDR and LTC4S influence responsiveness to leukotriene receptor antagonists in Korean children with asthma. J Hum Genet. 2011;56:284-289

43. Arriba-Méndez S, Sanz C, Isidoro-García M. et al. Analysis of 927T > C CYSLTR1 and -444A > C LTC4S polymorphisms in children with asthma. Allergol Immunopathol (Madr). 2008;36:259-263

44. Zhang Y, Huang H, Huang J. et al. The -444A/C Polymorphism in the LTC4S Gene and the Risk of Asthma: A Meta-analysis. Arch Med Res. 2012;43:444-450

45. Kumar A, Sharma S, Agrawal A. et al. Association of the -1072G/A Polymorphism in the LTC4S Gene with Asthma in an Indian Population. Int Arch Allergy Immunol. 2012;159:271-277

46. Park S-M, Choi E-Y, Bae M. et al. Histone variant H3F3A promotes lung cancer cell migration through intronic regulation. Nat Commun. 2016;7:12914

47. Lan F, Shi Y. Histone H3.3 and cancer: A potential reader connection. Proc Natl Acad Sci U S A. 2015;112:6814-9

48. Abbas T, Dutta A. p21 in cancer: intricate networks and multiple activities. Nat Rev Cancer. 2009;9:400-14

49. Chernock RD, Wang X, Gao G. et al. Detection and significance of human papillomavirus, CDKN2A(p16) and CDKN1A(p21) expression in squamous cell carcinoma of the larynx. Mod Pathol. 2013;26:223-231

50. Efimova T. p38δ mitogen-activated protein kinase regulates skin homeostasis and tumorigenesis. Cell Cycle. 2010;9:498-505

51. Gao L, Smit MA, van den Oord JJ. et al. Genome-wide promoter methylation analysis identifies epigenetic silencing of MAPK 13 in primary cutaneous melanoma. Pigment Cell Melanoma Res. 2013;26:542-554

52. O' Callaghan C, Fanning L, Barry O. et al. Hypermethylation of MAPK13 Promoter in Oesophageal Squamous Cell Carcinoma Is Associated with Loss of p38δ MAPK Expression. Cancers (Basel). 2015;7:2124-2133

Author contact

Corresponding address Corresponding author: Email: lneitanedu.jo


Received 2019-8-12
Accepted 2019-12-8
Published 2020-1-14