Abstract

   Osteoporosis is characterized by systemic microarchitecture impairment
   and bone loss, which ultimately lead to fragility fractures. This
   disease is most common in older people, especially in postmenopausal
   women. Cancellous bone is affected by osteoporosis earlier than
   cortical bone, and DNA methylation microarray analysis of the hip
   cancellous bone of patients with osteoarthritis revealed differential
   methylation. In view of the important role of cancellous bone in bone
   development, we examined genome‐wide DNA methylation profiles in the
   cancellous bone from patients with postmenopausal osteoporosis versus
   healthy postmenopausal women using Illumina 850K methylation microarray
   analysis. Under a threshold of P < 0.05, we obtained a total of 8973
   differentially methylated genes, such as SOX6, ACE, SYK and TGFB3.
   Under a threshold of P < 0.05 and |△β| > 0.2, a total of 17 and 34 key
   differentially methylated genes were further identified at the promoter
   region and cytosine‐ phosphate‐ guanine (CpG) islands (such as PRKCZ,
   GNA11 and COL4A1), respectively. PLEKHA2, PLEKHB1, PNPLA7, SCD, MGST3
   and TSNAX were the most common differentially methylated genes at both
   the promoter region and CpG islands. Five important signaling pathways,
   including the calcium signaling pathway, the cyclic guanosine
   phospho‐protein kinase G (cGMP‐PKG) signaling pathway, endocytosis, the
   Rap1 signaling pathway and the AMPK signaling pathway were identified.
   Our study may be suitable as a basis for exploring the mechanisms
   underlying osteoporosis in postmenopausal women.

   Keywords: differentially methylated genes, DNA methylation profiles,
   osteoporosis, postmenopausal women, signaling pathways
     __________________________________________________________________

   We studied DNA methylation profiles of cancellous bone in
   postmenopausal women with osteoporosis. Thirteen differentially
   methylated genes, including PLEKHA2, PLEKHB1, PNPLA7, SCD, MGST3,
   TSNAX, PRKCZ, GNA11, COL4A1, SOX6, ACE, SYK and TGFB3, and five related
   signaling pathways (calcium, cGMP‐PKG, endocytosis, Rap1 and AMPK) were
   identified. Our study may facilitate the identification of a novel DNA
   methylation molecular mechanism underlying postmenopausal osteoporosis.

   graphic file with name FEB4-10-1516-g009.jpg
     __________________________________________________________________

Abbreviations

   ACE
          angiotensin I–converting enzyme

   Ang
          angiotensin

   COL4A1
          collagen type IV alpha 1 chain

   GAN11
          G protein subunit alpha 11

   PLEKHA2
          pleckstrin homology domain containing A2

   PLEKHB1
          pleckstrin homology domain containing B1

   PNPLA7
          patatin‐like phospholipase domain containing 7

   PRKCZ
          protein kinase C zeta

   SCD
          stearoyl‐CoA desaturase

   SYK
          spleen‐associated tyrosine kinase

   TGFB3
          transforming growth factor beta 3

   TSNAX
          translin‐associated factor X

   Osteoporosis, the most common bone disease, is characterized by
   systemic microarchitecture impairment and bone loss, which ultimately
   lead to fragility fractures. The bone disease is most common in older
   people, especially in postmenopausal women [[38]1]. It is estimated
   that approximately 50% of the postmenopausal female population
   (>50 years old) suffer from osteoporosis [[39]2]. The onset of
   postmenopausal osteoporosis is without any obvious symptoms until the
   fracture occurs. Generally, fragility fracture (such as in the spine,
   hip or femur) usually leads to pain, malformation, dysfunction and even
   death.

   The primary reason for postmenopausal osteoporosis incidence is the
   disequilibrium between bone formation and bone resorption [[40]3].
   Evidence has uncovered risk factors of postmenopausal osteoporosis,
   such as advanced age, hypoovarianism, estrogen deficiency, increase of
   follicle‐stimulating hormone and luteinizing hormone, amenorrhea
   period, parental history of fracture, continuous calcium loss,
   inflammatory background and immune deficiency syndrome [[41]2]. Serious
   complications and high morbidity of postmenopausal osteoporosis have
   attracted major focus on its pathological mechanism.

   It has been found that some gene mutations, including
   colony‐stimulating factor 1 and low‐density lipoprotein
   receptor‐related protein 5, are associated with postmenopausal
   osteoporosis [[42]4]. Some gene methylations are also related to
   postmenopausal osteoporosis. Bone DNA methylation in the sclerostin
   (SOST) promoter, bone transcripts and serum levels are associated
   significantly with fracture risk in postmenopausal women [[43]5].
   Methylation of bone sclerostin also impairs Sp7 transcription factor,
   RUNX family transcription factor 2, and estrogen receptor alpha
   transactivation in postmenopausal patients with osteoporosis [[44]6].
   Positive correlations between Alula hypomethylation in blood cells and
   several age‐related phenotypes in bone and body fat have been found in
   postmenopausal women with osteoporosis [[45]7]. In addition, superoxide
   dismutase 1, serpin family Amember 1 and tripartite motif containing 63
   could be considered as potential biomarkers for postmenopausal
   osteoporosis [[46]8]. However, the pathogenesis of postmenopausal
   osteoporosis is still complex and not yet fully elucidated.

   It has been noted that cancellous bone, composed of interwoven
   trabeculae, accounts for 20% of the body’s bone mass, but forms 80% of
   the bone surface. Mechanical properties of cancellous bone are
   important because they are significantly associated with fracture risk
   [[47]9, [48]10, [49]11, [50]12, [51]13]. Cancellous bone is much
   earlier affected by osteoporosis than cortical bone at other locations
   of the skeleton [[52]14]. Delgado‐Calle et al. performed DNA
   methylation microarray analysis on the hip cancellous bone of patients
   with osteoarthritis and found different methylation regions [[53]15].
   In view of the important role of cancellous bone in bone development,
   we tried to explore genome‐wide DNA methylation profiles in the
   cancellous bone from patients with postmenopausal osteoporosis. Our
   study may provide useful information to explore the epigenetic
   pathology mechanism of the disease.

Materials and methods

Study individuals

   According to the clinical criteria of osteoporosis (T ≤ −2.5 standard
   deviations), five postmenopausal women with osteoporosis and three
   normal postmenopausal women (T ≥ −1.0 standard deviation) were
   recruited in this study. All of these individuals were selected
   outpatients from the clinic of the People’s Hospital of Deyang City.
   Postmenopausal women with osteoporosis and healthy postmenopausal women
   required natural menopause for 2–10 years. The body mass index (BMI)
   and age of menopause were matched between postmenopausal women with
   osteoporosis and healthy postmenopausal women. The inclusion criteria
   were as follows: none of the individuals had a history of drug use that
   might affect bone metabolism, such as glucocorticoids, estrogen,
   thyroid hormone, parathyroid hormone, fluoride, calcitonin, thiazines,
   barbiturates, antiepileptics, vitamin D or calcium‐containing
   preparations. Patients with bone metabolic diseases, such as kidney
   disease, liver disease, thyroid disease, diabetes, hyperprolactinemia,
   oophorectomy, rheumatoid arthritis, ankylosing spondylitis, absorb the
   adverse symptoms of chronic diarrhea, malignant tumor, blood disease,
   pathological fracture or traumatic fractures, hypertension, coronary
   atherosclerosis, myocardial infarction, cerebral infarction and
   infectious diseases, were excluded from this study. For genome‐wide
   methylation analysis, the cancellous bone of these individuals was
   collected for DNA extraction. Informed written consent was provided by
   all of the participants in this study. This study was approved by the
   Ethics Committee of our hospital and was performed in compliance with
   the Declaration of Helsinki.

DNA isolation and bisulfite treatment

   Cancellous bone tissue was first collected after removing connective
   tissue and adipose tissue. Then the cancellous bone tissue was quickly
   rinsed with 0.9% normal saline, quickly sucked off the blood with
   absorbent paper and cut into small pieces on ice (5 mm). Lastly, the
   cancellous bone tissue was put in the spiral centrifugal pipe with
   liquid nitrogen precooling, frozen in liquid nitrogen for more than 5
   min and transferred to −80℃ refrigerator for long‐term storage. Genomic
   DNA was obtained from cancellous bone using the TIANamp Genomic DNA Kit
   (Tiangen Biotech, Beijing, China). The concentration of extracted DNA
   was measured using a NanoDrop 2000 spectrophotometer (NanoDrop, Thermo
   Scientific, Wilmington, DE, USA). Only cancellous bone samples with DNA
   purity from 1.8 to 2.05 were retained. Approximately 200–500 ng genomic
   DNA from each cancellous bone sample was chemically modified and
   bisulfite converted using the EZ DNA Methylation kit (Zymo Research,
   Irvine, CA, USA), which converts unmethylated cytosines into uracil and
   methylated cytosines remain unchanged during the treatment.

Illumina 850K methylation microarray data preprocessing

   To obtain the raw signal value and DetectionP of each site, we used the
   GenomeStudio
   ([54]https://www.illumina.com/techniques/microarrays/array‐data‐analysi
   s‐experimental‐design/genomestudio.html) software to analyze the raw
   data. Then quality control of the data was performed, including site
   control and individual control. The bisulfite conversion of genomic DNA
   was calculated in the process of quality control (Table [55]S1). With
   that, software of lumi2.22.1 in R package [[56]16] was used to perform
   the correction of fluorescence bias and quantile normalization. For
   probe‐type bias, the software of BMIQv1.3 (beta‐mixture quantile
   normalization) [[57]17] was applied for correction of methylation level
   (beta value, β). Software of IMA3.1.2 in R package was utilized for
   analysis of differential methylation sites. In this process, the method
   of empirical Bayes statistics in limma [[58]18] was put into use.
   Lastly, differentially methylated sites were identified under the
   threshold of P < 0.05. In addition, the cluster3.0 software was used
   for clustering analysis of differentially methylated sites.

Genomic characteristic analysis of differentially methylated sites

   To understand the genomic characteristic, we annotated differentially
   methylated sites with respect to defined CpG sites (transcription start
   site, 5′ UTR, 3′ UTR, body, exon, intron and intergenic region)
   according to the Infinium Methylation EPIC array annotation file
   ([59]http://www.illumina.com).

Functional annotation analysis of genes at differentially methylated sites

   The functional annotation of identified genes at differentially
   methylated sites was analyzed by Kobas
   ([60]http://kobas.cbi.pku.edu.cn/kobas3) [[61]19]. Gene Ontology
   enrichment analysis and Kyoto Encyclopedia of Genes and Genomes
   signaling pathway enrichment analysis were performed. False discovery
   rate <0.05 was considered as significant.

Methylation analysis at the promoter region and CpG islands

   The threshold of P < 0.05 and |△β| > 0.2 was used to identify
   differentially methylated sites at the promoter region and CpG islands.
   Among which, △β> 0 and △β < 0 represented hypermethylation sites and
   hypomethylation sites, respectively. The cluster3.0 software was used
   for clustering analysis of differentially methylated sites in the
   promoter region and CpG islands.

Electronic validation of genes in differential methylation sites

   The dataset of [62]GSE100609 (involving four cases and four normal
   control subjects) was used to test the expression of genes in
   differential methylation sites. Student’s t test was applied for
   statistical analysis. The expression result of these genes was
   visualized by boxplots.

Results

Illumina 850K methylation microarray of individuals

   In this study, a total of 12 postmenopausal women with osteoporosis and
   4 normal postmenopausal women were included. After a series of
   screenings, five postmenopausal women with osteoporosis and three
   normal postmenopausal women were included. The flowchart for
   participants selected for the study was shown in Fig. [63]S1. In
   addition, the clinical information of these individuals was shown in
   Table [64]1. The DNA of cancellous bone of these individuals was used
   for the genome‐wide DNA methylation profiling analysis. After data
   processing, a total of 843 958 CpG sites were obtained. There were 9603
   hypermethylation sites and 5706 hypomethylation sites in postmenopausal
   women with osteoporosis (Fig. [65]1). The Pearson correlation chart was
   produced for each CpG site between postmenopausal women with
   osteoporosis and normal postmenopausal women (Fig. [66]2). The Pearson
   correlation coefficient was 0.997, which indicated that the gene
   expression at the CpG sites was relatively consistent between cases and
   normal control subjects.

Fig. 1.

   Fig. 1
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   The number of differential methylation sites in postmenopausal women
   with osteoporosis. DMP, differentially methylated position.

Table 1.

   The clinical information of postmenopausal women with osteoporosis and
   normal postmenopausal women. BMDFN, bone mineral density of femoral
   neck; BMDLS, bone mineral density of lumbar spine; BMDTH, bone mineral
   density of total hip.
   Patients Patient no. Sample Age (years) BMDFN (g/cm^2) BMDFN (T‐value)
   BMDLS (g/cm^2) BMDLS (T‐value) BMDTH (g/cm^2) BMDTH (T‐value) Weight
   (hg) Height (cm)
   Postmenopausal women with osteoporosis 1 Bone tissue 86 0.496 −3.6
   0.607 −4.2 0.582 −3 41 140
   2 Bone tissue 80 0.616 −2.6 0.718 −3.3 0.664 −2.5 65 155
   3 Bone tissue 87 0.555 −3.1 1.053 −0.5 0.586 −3 50 155
   4 Bone tissue 89 0.529 −3.3 0.549 −4.7 0.572 −3.1 43 153
   5 Bone tissue 72 0.621 −2.6 0.82 −2.5 0.649 −2.5 64 157
   Postmenopausal women 1 Bone tissue 56 0.804 −1 0.822 2.1 0.825 −1 60
   150
   2 Bone tissue 78 0.748 −1 0.849 −1 0.824 −0.9 60 162
   3 Bone tissue 55 0.92 −0.1 1.346 1.9 1.138 1.3 59 155
   [68]Open in a new tab

Fig. 2.

   Fig. 2
   [69]Open in a new tab

   The Pearson correlation chart of each CpG site between postmenopausal
   women with osteoporosis and normal postmenopausal women. The x axis and
   y axis presented the average beta value of CpG sites in postmenopausal
   women with osteoporosis and normal postmenopausal women, respectively.
   PCC, Pearson correlation coefficient.

Identification of significantly differentially methylated sites

   To analyze DNA methylation differences between postmenopausal women
   with osteoporosis and normal postmenopausal women, we examined the
   P‐values between both groups. A total of 15 309 CpG sites were
   significantly differentially methylated with P < 0.05, including 9603
   hypermethylated sites [involving 5200 genes, such as SOX6, angiotensin
   I (Ang I)‐converting enzyme (ACE) and spleen‐associated tyrosine kinase
   (SYK)] and 5706 hypomethylated sites [involving 3773 genes, such as
   transforming growth factor beta 3 (TGFB3)] (Table [70]S2). The heatmap
   of all significantly differentially methylated sites was shown in
   Fig. [71]3.

Fig. 3.

   Fig. 3
   [72]Open in a new tab

   Unsupervised hierarchical clustering dendrogram of all differentially
   methylated sites in postmenopausal women with osteoporosis. Red, below
   the reference channel; blue, higher than the reference.

Genomic features of significantly differentially methylated sites

   The CpG island of significantly differentially methylated sites was
   analyzed in relationship with genomic locations. Significant methylated
   site differences were observed between postmenopausal women with
   osteoporosis and normal postmenopausal women according to the CpG
   content. The percentage of methylation in transcription start site, 5′
   UTR, 3′ UTR, body, exon, intron and intergenic region for each of the
   samples of postmenopausal women with osteoporosis was shown in
   Fig. [73]4. It is indicated that most of the significantly
   differentially methylated sites in postmenopausal women with
   osteoporosis were found within the body area.

Fig. 4.

   Fig. 4
   [74]Open in a new tab

   Genomic features of differentially methylated sites in postmenopausal
   women with osteoporosis. Graph showing percentages of differentially
   methylated sites according to their CpG content. TSS, transcription
   start site.

Functional enrichment analysis of genes at significantly differentially
methylated sites

   To further search the molecular function of 8973 genes (5200
   hypermethylated genes and 3773 hypomethylated genes) at significantly
   differentially methylated sites, we performed analysis of Gene Ontology
   and Kyoto Encyclopedia of Genes and Genomes. Functional annotation of
   these genes indicated that nervous system development, multicellular
   organismal development and system development were the most
   significantly enriched biological processes (Fig. [75]5A); binding,
   protein binding and ion binding were the most significantly enriched
   molecular functions (Fig. [76]5B); and synapse, intracellular part and
   intracellular were the most significantly enriched cellular components
   (Fig. [77]5C). In addition, the calcium signaling pathway, cGMP‐PKG
   signaling pathway, endocytosis, Rap1 signaling pathway and adenosine
   activated protein kinase (AMPK) signaling pathway were several
   significantly enriched signaling pathways (Fig. [78]5D). Significantly,
   several significantly differentially methylated genes were enriched in
   these signaling pathways. For instance, GNA11 was involved in both the
   calcium signaling pathway and the cGMP‐PKG signaling pathway, protein
   kinase C zeta (PRKCZ) was involved in both endocytosis and the Rap1
   signaling pathway, and stearoyl‐CoA desaturase (SCD) was involved in
   the AMPK signaling pathway. The earlier signaling pathways and enriched
   genes were listed in Table [79]S3.

Fig. 5.

   Fig. 5
   [80]Open in a new tab

   (A) Top 30 significantly enriched biological processes of genes at
   differentially methylated sites in postmenopausal women with
   osteoporosis. The x axis and y axis presented influencing factors of
   dot and biological processes, respectively. (B) Top 30 significantly
   enriched molecular functions of genes at differentially methylated
   sites in postmenopausal women with osteoporosis. The x axis and y axis
   presented influencing factors of dot and molecular functions,
   respectively. (C) Top 30 significantly enriched cellular components of
   genes at differentially methylated sites in postmenopausal women with
   osteoporosis. The x axis and y axis presented influencing factors of
   dot and cellular components, respectively. (D) Top 30 significantly
   enriched signaling pathways of genes at differentially methylated sites
   in postmenopausal women with osteoporosis. The x axis and y axis
   presented influencing factors of dot and signaling pathways,
   respectively.

Identification of significantly differentially methylated sites at the
promoter region and CpG islands

   Under the threshold of P < 0.05, a total of 7142 and 6386 significantly
   differentially methylated genes were identified in the promoter region
   and CpG islands, respectively (data not shown). The heatmap of
   significantly differentially methylated genes in the promoter region
   and CpG islands was shown in Figs [81]6 and [82]7, respectively. In
   addition, under the threshold of P < 0.05 and |△β| > 0.2, a total of 21
   (Table [83]2) and 63 (Table [84]3) key significantly differentially
   methylated sites were identified at the promoter region (17
   differentially methylated genes involved) and CpG islands (34
   differentially methylated genes involved, such as hypermethylated
   genes, including GNA11 and PRKCZ, and hypomethylated genes, including
   collagen type IV alpha 1 chain [COL4A1]), respectively. It is noted
   that three significantly hypermethylated genes [pleckstrin homology
   domain containing A2 (PLEKHA2), pleckstrin homology domain containing
   B1 (PLEKHB1) and patatin‐like phospholipase domain containing 7
   (PNPLA7)] and three significantly hypomethylated genes (SCD, MGST3 and
   translin‐associated factor X (TSNAX)] were the common significantly
   differentially methylated genes between the promoter region and CpG
   islands.

Fig. 6.

   Fig. 6
   [85]Open in a new tab

   Unsupervised hierarchical clustering dendrogram of differentially
   methylated sites in the promoter region of postmenopausal women with
   osteoporosis. Red, below the reference channel; blue, higher than the
   reference.

Fig. 7.

   Fig. 7
   [86]Open in a new tab

   Unsupervised hierarchical clustering dendrogram of differentially
   methylated sites at the CpG islands in postmenopausal women with
   osteoporosis. Red, below the reference channel; blue, higher than the
   reference.

Table 2.

   Key differentially methylated sites at the promoter region. △β > 0:
   hypermethylation; △β < 0: hypomethylation.
   Cg ID P‐value △β UCSC_Refgene_Name UCSC_CpG_Islands_Name
   Relation_To_UCSC_CpG_Island
   cg02121736 0.025469 0.202933 ZNF639 chr3:179040466‐179041632 Island
   cg02564175 0.035203 0.201
   cg03422583 0.004043 0.290533 TRABD chr22:50632707‐50633041 N_Shore
   cg05779406 0.009116 −0.20987 ZFAND2A chr7:1198965‐1200144 N_Shore
   cg09019154 0.017332 −0.22153 chr8:19614131‐19615307 S_Shore
   cg10117077 0.015097 0.2958 DENND2D
   cg10507965 0.043917 −0.30447 SCD chr10:102106182‐102107722 Island
   cg12201190 0.001231 0.466267 PLEKHF1 chr19:30164768‐30165556 N_Shore
   cg13068698 0.045191 −0.20613 DPY19L1 chr7:35076136‐35077808 S_Shore
   cg15704280 0.005264 −0.24573 13‐Sep chr7:45808183‐45808745 Island
   cg16848221 0.02825 −0.31247 CLPP chr19:6361442‐6362002 S_Shore
   cg16866567 1.38E−8 0.3714 PLEKHA2 chr8:38758457‐38759380 Island
   cg16915828 0.007808 0.2352 PLEKHB1 chr11:73371800‐73372632 Island
   cg17250082 0.003449 0.242067 chr6:31276241‐31276526 N_Shore
   cg17873037 0.005253 −0.30533 MGST3 chr1:165599563‐165600574 Island
   cg18559901 0.005793 0.200133 PNPLA7 chr9:140356314‐140356987 Island
   cg19193962 0.001661 −0.21107 TSNAX chr1:231663999‐231664608 Island
   cg21945639 0.003314 −0.23253 chr1:200271276‐200271538 Island
   cg24657788 0.005024 −0.23553 C4orf3 chr4:120221626‐120222007 N_Shore
   cg25285484 0.007359 0.4492 ZNF597 chr16:3493098‐3493569 Island
   cg25345738 0.040382 −0.21127 PWP1 chr12:108079442‐108079893 N_Shore
   [87]Open in a new tab

Table 3.

   Key significantly differentially methylated sites at the CpG islands.
   △β> 0: hypermethylation; △β < 0: hypomethylation.
   Cg ID P‐value △β UCSC_Refgene_Name UCSC_CpG_Islands_Name
   Relation_To_UCSC_CpG_Island
   cg00545199 0.011003 0.352133 ZFYVE28 chr4:2305514‐2305793
   cg00947782 0.029792 0.224267 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg01156747 6.41E−5 −0.58947 chr7:120519‐120758
   cg01392179 0.002224 0.315267 FGF18 chr5:170845760‐170848124
   cg01701555 0.027398 −0.25573 chr1:146543987‐146544439
   cg01710670 0.048918 0.2076 chr16:15018805‐15019032
   cg01818076 0.036892 0.210333 chr16:86530747‐86532994
   cg02121736 0.025469 0.202933 ZNF639 chr3:179040466‐179041632
   Promoter_Associated
   cg02148024 0.009049 0.225333 SLC22A16 chr6:110797297‐110798201
   cg02157463 0.028075 0.2118 JPH3 chr16:87648086‐87648688
   cg02599361 0.027559 0.201867 ADAMTS2 chr5:178547421‐178548701
   cg03019812 0.041739 0.249267 HCG9 chr6:29944402‐29945169 Unclassified
   cg03063057 3.15E−7 0.209467 GNA11 chr19:3110096‐3110438
   cg03343571 0.03773 0.2408 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg03929089 0.004207 −0.22047 chr4:120375746‐120376363
   cg04546413 0.040255 0.2238 chr19:29218001‐29218733
   cg05554346 0.023945 0.286467 chr4:4144575‐4145667
   cg06249604 0.04433 0.285333 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg07832337 0.00619 −0.21753 ATP2C2 chr16:84401957‐84402497
   cg08292959 0.004096 −0.3684 MGAT5B chr17:74878245‐74878455
   cg08861434 0.032836 −0.24613 chr13:112062648‐112062903
   cg08880261 0.010549 −0.47967 UMODL1 chr21:43547871‐43548089
   cg09194930 0.033303 −0.23093 MT1L chr16:56650981‐56651384
   Unclassified_Cell_type_specific
   cg09279736 0.037658 0.243667 RNF39 chr6:30038881‐30039477
   cg10202835 0.023391 0.300867 chr5:25190503‐25191113
   cg10507965 0.043917 −0.30447 SCD chr10:102106182‐102107722
   Promoter_Associated
   cg10930308 0.040368 0.2862 RNF39 chr6:30038881‐30039477
   cg11651932 0.033821 0.4722 chr8:1327331‐1327547
   cg12401798 0.034347 −0.22453 KCNQ2 chr20:62097193‐62098254
   Unclassified_Cell_type_specific
   cg12633154 0.028544 0.309 RNF39 chr6:30038881‐30039477
   cg12801256 0.002018 0.237533 ST6GAL2 chr2:107459523‐107459882
   cg13185413 0.018182 0.233667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg13401893 0.020209 0.32 RNF39 chr6:30038881‐30039477
   cg14188106 0.039917 0.200667 TNXB chr6:32063533‐32065044
   Unclassified_Cell_type_specific
   cg15704280 0.005264 −0.24573 13‐Sep chr7:45808183‐45808745
   Promoter_Associated
   cg15877520 0.048874 0.209667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg16078649 0.029298 0.280067 RNF39 chr6:30038881‐30039477
   cg16193862 0.000695 0.215467 chr15:24506126‐24506423
   cg16788050 0.005304 0.3398 chr11:8053245‐8053490
   cg16866567 1.38E−8 0.3714 PLEKHA2 chr8:38758457‐38759380
   Promoter_Associated
   cg16913250 1.35E−5 −0.56647 CTTNBP2 chr7:117512942‐117513865
   cg16915828 0.007808 0.2352 PLEKHB1 chr11:73371800‐73372632
   Promoter_Associated
   cg17550997 0.003944 0.269467 chr6:168529982‐168530307
   cg17873037 0.005253 −0.30533 MGST3 chr1:165599563‐165600574
   Promoter_Associated
   cg18025438 0.013693 0.222933 chr1:228744110‐228784168
   cg18559901 0.005793 0.200133 PNPLA7 chr9:140356314‐140356987
   Promoter_Associated
   cg18662228 0.00046 0.430533 AGAP1 chr2:236867652‐236867906
   cg18792536 0.002719 −0.30193 UPK3B chr7:76145396‐76145781 Unclassified
   cg19193962 0.001661 −0.21107 TSNAX chr1:231663999‐231664608
   Promoter_Associated
   cg19252199 2.12E−6 −0.26647 COL4A1 chr13:110960924‐110961143
   cg19405842 0.003469 0.476867 PRKCZ chr1:2038555‐2038863
   cg20242889 0.003057 0.246733 chr19:41317792‐41318151
   cg20249327 0.047443 0.221133 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg20546215 0.024787 −0.21073 chr3:194785977‐194786549
   cg21873524 0.018211 0.355333 chr4:190942734‐190944898
   cg21945639 0.003314 −0.23253 chr1:200271276‐200271538
   Promoter_Associated_Cell_type_specific
   cg22172057 0.00272 0.215267 ADARB2 chr10:1404659‐1406219
   cg24043411 0.039615 −0.26847 CPNE5 chr6:36807678‐36808808
   Unclassified_Cell_type_specific
   cg24440302 0.047802 −0.2928 SIGIRR chr11:406491‐407871 Unclassified
   cg24536782 0.035538 0.260133 chr8:216352‐216828
   cg24696067 2.09E−7 0.359067 MAD1L1 chr7:1881181‐1881391
   cg25285484 0.007359 0.4492 ZNF597 chr16:3493098‐3493569
   Promoter_Associated
   cg26951705 0.039354 −0.44867 ZNF787 chr19:56612299‐56612743
   cg00545199 0.011003 0.352133 ZFYVE28 chr4:2305514‐2305793
   cg00947782 0.029792 0.224267 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg01156747 6.41E−5 −0.58947 chr7:120519‐120758
   cg01392179 0.002224 0.315267 FGF18 chr5:170845760‐170848124
   cg01701555 0.027398 −0.25573 chr1:146543987‐146544439
   cg01710670 0.048918 0.2076 chr16:15018805‐15019032
   cg01818076 0.036892 0.210333 chr16:86530747‐86532994
   cg02121736 0.025469 0.202933 ZNF639 chr3:179040466‐179041632
   Promoter_Associated
   cg02148024 0.009049 0.225333 SLC22A16 chr6:110797297‐110798201
   cg02157463 0.028075 0.2118 JPH3 chr16:87648086‐87648688
   cg02599361 0.027559 0.201867 ADAMTS2 chr5:178547421‐178548701
   cg03019812 0.041739 0.249267 HCG9 chr6:29944402‐29945169 Unclassified
   cg03063057 3.15E−7 0.209467 GNA11 chr19:3110096‐3110438
   cg03343571 0.03773 0.2408 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg03929089 0.004207 −0.22047 chr4:120375746‐120376363
   cg04546413 0.040255 0.2238 chr19:29218001‐29218733
   cg05554346 0.023945 0.286467 chr4:4144575‐4145667
   cg06249604 0.04433 0.285333 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg07832337 0.00619 −0.21753 ATP2C2 chr16:84401957‐84402497
   cg08292959 0.004096 −0.3684 MGAT5B chr17:74878245‐74878455
   cg08861434 0.032836 −0.24613 chr13:112062648‐112062903
   cg08880261 0.010549 −0.47967 UMODL1 chr21:43547871‐43548089
   cg09194930 0.033303 −0.23093 MT1L chr16:56650981‐56651384
   Unclassified_Cell_type_specific
   cg09279736 0.037658 0.243667 RNF39 chr6:30038881‐30039477
   cg10202835 0.023391 0.300867 chr5:25190503‐25191113
   cg10507965 0.043917 −0.30447 SCD chr10:102106182‐102107722
   Promoter_Associated
   cg10930308 0.040368 0.2862 RNF39 chr6:30038881‐30039477
   cg11651932 0.033821 0.4722 chr8:1327331‐1327547
   cg12401798 0.034347 −0.22453 KCNQ2 chr20:62097193‐62098254
   Unclassified_Cell_type_specific
   cg12633154 0.028544 0.309 RNF39 chr6:30038881‐30039477
   cg12801256 0.002018 0.237533 ST6GAL2 chr2:107459523‐107459882
   cg13185413 0.018182 0.233667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg13401893 0.020209 0.32 RNF39 chr6:30038881‐30039477
   cg14188106 0.039917 0.200667 TNXB chr6:32063533‐32065044
   Unclassified_Cell_type_specific
   cg15704280 0.005264 −0.24573 13‐Sep chr7:45808183‐45808745
   Promoter_Associated
   cg15877520 0.048874 0.209667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg16078649 0.029298 0.280067 RNF39 chr6:30038881‐30039477
   cg16193862 0.000695 0.215467 chr15:24506126‐24506423
   cg16788050 0.005304 0.3398 chr11:8053245‐8053490
   cg16866567 1.38E−8 0.3714 PLEKHA2 chr8:38758457‐38759380
   Promoter_Associated
   cg16913250 1.35E−5 −0.56647 CTTNBP2 chr7:117512942‐117513865
   cg16915828 0.007808 0.2352 PLEKHB1 chr11:73371800‐73372632
   Promoter_Associated
   cg17550997 0.003944 0.269467 chr6:168529982‐168530307
   cg17873037 0.005253 −0.30533 MGST3 chr1:165599563‐165600574
   Promoter_Associated
   cg18025438 0.013693 0.222933 chr1:228744110‐228784168
   cg18559901 0.005793 0.200133 PNPLA7 chr9:140356314‐140356987
   Promoter_Associated
   cg18662228 0.00046 0.430533 AGAP1 chr2:236867652‐236867906
   cg18792536 0.002719 −0.30193 UPK3B chr7:76145396‐76145781 Unclassified
   cg19193962 0.001661 −0.21107 TSNAX chr1:231663999‐231664608
   Promoter_Associated
   cg19252199 2.12E−6 −0.26647 COL4A1 chr13:110960924‐110961143
   cg19405842 0.003469 0.476867 PRKCZ chr1:2038555‐2038863
   cg20242889 0.003057 0.246733 chr19:41317792‐41318151
   cg20249327 0.047443 0.221133 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg20546215 0.024787 −0.21073 chr3:194785977‐194786549
   cg21873524 0.018211 0.355333 chr4:190942734‐190944898
   cg21945639 0.003314 −0.23253 chr1:200271276‐200271538
   Promoter_Associated_Cell_type_specific
   cg22172057 0.00272 0.215267 ADARB2 chr10:1404659‐1406219
   cg24043411 0.039615 −0.26847 CPNE5 chr6:36807678‐36808808
   Unclassified_Cell_type_specific
   cg24440302 0.047802 −0.2928 SIGIRR chr11:406491‐407871 Unclassified
   cg24536782 0.035538 0.260133 chr8:216352‐216828
   cg24696067 2.09E−7 0.359067 MAD1L1 chr7:1881181‐1881391
   cg25285484 0.007359 0.4492 ZNF597 chr16:3493098‐3493569
   Promoter_Associated
   cg26951705 0.039354 −0.44867 ZNF787 chr19:56612299‐56612743
   cg00545199 0.011003 0.352133 ZFYVE28 chr4:2305514‐2305793
   cg00947782 0.029792 0.224267 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg01156747 6.41E−5 −0.58947 chr7:120519‐120758
   cg01392179 0.002224 0.315267 FGF18 chr5:170845760‐170848124
   cg01701555 0.027398 −0.25573 chr1:146543987‐146544439
   cg01710670 0.048918 0.2076 chr16:15018805‐15019032
   cg01818076 0.036892 0.210333 chr16:86530747‐86532994
   cg02121736 0.025469 0.202933 ZNF639 chr3:179040466‐179041632
   Promoter_Associated
   cg02148024 0.009049 0.225333 SLC22A16 chr6:110797297‐110798201
   cg02157463 0.028075 0.2118 JPH3 chr16:87648086‐87648688
   cg02599361 0.027559 0.201867 ADAMTS2 chr5:178547421‐178548701
   cg03019812 0.041739 0.249267 HCG9 chr6:29944402‐29945169 Unclassified
   cg03063057 3.15E−7 0.209467 GNA11 chr19:3110096‐3110438
   cg03343571 0.03773 0.2408 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg03929089 0.004207 −0.22047 chr4:120375746‐120376363
   cg04546413 0.040255 0.2238 chr19:29218001‐29218733
   cg05554346 0.023945 0.286467 chr4:4144575‐4145667
   cg06249604 0.04433 0.285333 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg07832337 0.00619 −0.21753 ATP2C2 chr16:84401957‐84402497
   cg08292959 0.004096 −0.3684 MGAT5B chr17:74878245‐74878455
   cg08861434 0.032836 −0.24613 chr13:112062648‐112062903
   cg08880261 0.010549 −0.47967 UMODL1 chr21:43547871‐43548089
   cg09194930 0.033303 −0.23093 MT1L chr16:56650981‐56651384
   Unclassified_Cell_type_specific
   cg09279736 0.037658 0.243667 RNF39 chr6:30038881‐30039477
   cg10202835 0.023391 0.300867 chr5:25190503‐25191113
   cg10507965 0.043917 −0.30447 SCD chr10:102106182‐102107722
   Promoter_Associated
   cg10930308 0.040368 0.2862 RNF39 chr6:30038881‐30039477
   cg11651932 0.033821 0.4722 chr8:1327331‐1327547
   cg12401798 0.034347 −0.22453 KCNQ2 chr20:62097193‐62098254
   Unclassified_Cell_type_specific
   cg12633154 0.028544 0.309 RNF39 chr6:30038881‐30039477
   cg12801256 0.002018 0.237533 ST6GAL2 chr2:107459523‐107459882
   cg13185413 0.018182 0.233667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg13401893 0.020209 0.32 RNF39 chr6:30038881‐30039477
   cg14188106 0.039917 0.200667 TNXB chr6:32063533‐32065044
   Unclassified_Cell_type_specific
   cg15704280 0.005264 −0.24573 13‐Sep chr7:45808183‐45808745
   Promoter_Associated
   cg15877520 0.048874 0.209667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg16078649 0.029298 0.280067 RNF39 chr6:30038881‐30039477
   cg16193862 0.000695 0.215467 chr15:24506126‐24506423
   cg16788050 0.005304 0.3398 chr11:8053245‐8053490
   cg16866567 1.38E−8 0.3714 PLEKHA2 chr8:38758457‐38759380
   Promoter_Associated
   cg16913250 1.35E−5 −0.56647 CTTNBP2 chr7:117512942‐117513865
   cg16915828 0.007808 0.2352 PLEKHB1 chr11:73371800‐73372632
   Promoter_Associated
   cg17550997 0.003944 0.269467 chr6:168529982‐168530307
   cg17873037 0.005253 −0.30533 MGST3 chr1:165599563‐165600574
   Promoter_Associated
   cg18025438 0.013693 0.222933 chr1:228744110‐228784168
   cg18559901 0.005793 0.200133 PNPLA7 chr9:140356314‐140356987
   Promoter_Associated
   cg18662228 0.00046 0.430533 AGAP1 chr2:236867652‐236867906
   cg18792536 0.002719 −0.30193 UPK3B chr7:76145396‐76145781 Unclassified
   cg19193962 0.001661 −0.21107 TSNAX chr1:231663999‐231664608
   Promoter_Associated
   cg19252199 2.12E−6 −0.26647 COL4A1 chr13:110960924‐110961143
   cg19405842 0.003469 0.476867 PRKCZ chr1:2038555‐2038863
   cg20242889 0.003057 0.246733 chr19:41317792‐41318151
   cg20249327 0.047443 0.221133 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg20546215 0.024787 −0.21073 chr3:194785977‐194786549
   cg21873524 0.018211 0.355333 chr4:190942734‐190944898
   cg21945639 0.003314 −0.23253 chr1:200271276‐200271538
   Promoter_Associated_Cell_type_specific
   cg22172057 0.00272 0.215267 ADARB2 chr10:1404659‐1406219
   cg24043411 0.039615 −0.26847 CPNE5 chr6:36807678‐36808808
   Unclassified_Cell_type_specific
   cg24440302 0.047802 −0.2928 SIGIRR chr11:406491‐407871 Unclassified
   cg24536782 0.035538 0.260133 chr8:216352‐216828
   cg24696067 2.09E−7 0.359067 MAD1L1 chr7:1881181‐1881391
   cg25285484 0.007359 0.4492 ZNF597 chr16:3493098‐3493569
   Promoter_Associated
   cg26951705 0.039354 −0.44867 ZNF787 chr19:56612299‐56612743
   cg00545199 0.011003 0.352133 ZFYVE28 chr4:2305514‐2305793
   cg00947782 0.029792 0.224267 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg01156747 6.41E−5 −0.58947 chr7:120519‐120758
   cg01392179 0.002224 0.315267 FGF18 chr5:170845760‐170848124
   cg01701555 0.027398 −0.25573 chr1:146543987‐146544439
   cg01710670 0.048918 0.2076 chr16:15018805‐15019032
   cg01818076 0.036892 0.210333 chr16:86530747‐86532994
   cg02121736 0.025469 0.202933 ZNF639 chr3:179040466‐179041632
   Promoter_Associated
   cg02148024 0.009049 0.225333 SLC22A16 chr6:110797297‐110798201
   cg02157463 0.028075 0.2118 JPH3 chr16:87648086‐87648688
   cg02599361 0.027559 0.201867 ADAMTS2 chr5:178547421‐178548701
   cg03019812 0.041739 0.249267 HCG9 chr6:29944402‐29945169 Unclassified
   cg03063057 3.15E−7 0.209467 GNA11 chr19:3110096‐3110438
   cg03343571 0.03773 0.2408 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg03929089 0.004207 −0.22047 chr4:120375746‐120376363
   cg04546413 0.040255 0.2238 chr19:29218001‐29218733
   cg05554346 0.023945 0.286467 chr4:4144575‐4145667
   cg06249604 0.04433 0.285333 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg07832337 0.00619 −0.21753 ATP2C2 chr16:84401957‐84402497
   cg08292959 0.004096 −0.3684 MGAT5B chr17:74878245‐74878455
   cg08861434 0.032836 −0.24613 chr13:112062648‐112062903
   cg08880261 0.010549 −0.47967 UMODL1 chr21:43547871‐43548089
   cg09194930 0.033303 −0.23093 MT1L chr16:56650981‐56651384
   Unclassified_Cell_type_specific
   cg09279736 0.037658 0.243667 RNF39 chr6:30038881‐30039477
   cg10202835 0.023391 0.300867 chr5:25190503‐25191113
   cg10507965 0.043917 −0.30447 SCD chr10:102106182‐102107722
   Promoter_Associated
   cg10930308 0.040368 0.2862 RNF39 chr6:30038881‐30039477
   cg11651932 0.033821 0.4722 chr8:1327331‐1327547
   cg12401798 0.034347 −0.22453 KCNQ2 chr20:62097193‐62098254
   Unclassified_Cell_type_specific
   cg12633154 0.028544 0.309 RNF39 chr6:30038881‐30039477
   cg12801256 0.002018 0.237533 ST6GAL2 chr2:107459523‐107459882
   cg13185413 0.018182 0.233667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg13401893 0.020209 0.32 RNF39 chr6:30038881‐30039477
   cg14188106 0.039917 0.200667 TNXB chr6:32063533‐32065044
   Unclassified_Cell_type_specific
   cg15704280 0.005264 −0.24573 13‐Sep chr7:45808183‐45808745
   Promoter_Associated
   cg15877520 0.048874 0.209667 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg16078649 0.029298 0.280067 RNF39 chr6:30038881‐30039477
   cg16193862 0.000695 0.215467 chr15:24506126‐24506423
   cg16788050 0.005304 0.3398 chr11:8053245‐8053490
   cg16866567 1.38E−8 0.3714 PLEKHA2 chr8:38758457‐38759380
   Promoter_Associated
   cg16913250 1.35E−5 −0.56647 CTTNBP2 chr7:117512942‐117513865
   cg16915828 0.007808 0.2352 PLEKHB1 chr11:73371800‐73372632
   Promoter_Associated
   cg17550997 0.003944 0.269467 chr6:168529982‐168530307
   cg17873037 0.005253 −0.30533 MGST3 chr1:165599563‐165600574
   Promoter_Associated
   cg18025438 0.013693 0.222933 chr1:228744110‐228784168
   cg18559901 0.005793 0.200133 PNPLA7 chr9:140356314‐140356987
   Promoter_Associated
   cg18662228 0.00046 0.430533 AGAP1 chr2:236867652‐236867906
   cg18792536 0.002719 −0.30193 UPK3B chr7:76145396‐76145781 Unclassified
   cg19193962 0.001661 −0.21107 TSNAX chr1:231663999‐231664608
   Promoter_Associated
   cg19252199 2.12E−6 −0.26647 COL4A1 chr13:110960924‐110961143
   cg19405842 0.003469 0.476867 PRKCZ chr1:2038555‐2038863
   cg20242889 0.003057 0.246733 chr19:41317792‐41318151
   cg20249327 0.047443 0.221133 RNF39 chr6:30038881‐30039477
   Unclassified_Cell_type_specific
   cg20546215 0.024787 −0.21073 chr3:194785977‐194786549
   cg21873524 0.018211 0.355333 chr4:190942734‐190944898
   cg21945639 0.003314 −0.23253 chr1:200271276‐200271538
   Promoter_Associated_Cell_type_specific
   cg22172057 0.00272 0.215267 ADARB2 chr10:1404659‐1406219
   cg24043411 0.039615 −0.26847 CPNE5 chr6:36807678‐36808808
   Unclassified_Cell_type_specific
   cg24440302 0.047802 −0.2928 SIGIRR chr11:406491‐407871 Unclassified
   cg24536782 0.035538 0.260133 chr8:216352‐216828
   cg24696067 2.09E−7 0.359067 MAD1L1 chr7:1881181‐1881391
   cg25285484 0.007359 0.4492 ZNF597 chr16:3493098‐3493569
   Promoter_Associated
   cg26951705 0.039354 −0.44867 ZNF787 chr19:56612299‐56612743
   [88]Open in a new tab

Electronic validation of genes in significantly differential methylation
sites

   In this study, two significantly hypermethylated genes (SOX6 and GNA11)
   and four significantly hypomethylated genes (SCD, MGST3, TSNAX and
   TGFB3) in significantly differential methylation sites were randomly
   selected for validation in the [89]GSE100609 dataset (Fig. [90]8). Our
   result showed that SCD, MGST3, TSNAX and TGFB3 were up‐regulated, and
   SOX6 and GNA11 were down‐regulated with no statistical significance.
   The expression was consistent with the bioinformatics analysis.

Fig. 8.

   Fig. 8
   [91]Open in a new tab

   The boxplots of electronic validation of SOX6, GNA11, SCD, MGST3, TSNAX
   and TGFB3 in the [92]GSE100609 dataset.

Discussion

   In this study, we found that DNA methylation was involved in the
   process of osteoporosis in postmenopausal women. In the identification
   analysis of differentially methylated sites (under the threshold of
   P < 0.05 and |△β| > 0.2), three common significantly hypermethylated
   genes (PLEKHA2, PLEKHB1 and PNPLA7) and three significantly
   hypomethylated genes (SCD, MGST3 and TSNAX) were found between the
   promoter region and CpG islands. PLEKHA2 (also called TAPP2) plays an
   important role for phosphatidylinositol 3‐kinase‐driven cytoskeletal
   reorganization [[93]20]. It is reported that PLEKHA2 is associated with
   juvenile idiopathic arthritis and rheumatoid arthritis [[94]17]. The
   down‐regulation of PLEKHB1 was found in human osteoblast‐like cells
   [[95]21]. PNPLA7 is a conserved protein in mouse and human. The
   expression of PNPLA7 is remarkably increased in human neonatal
   articular cartilage [[96]22]. The mutation of PNPLA7 gene (rs3812499)
   is related to rheumatoid arthritis [[97]23]. It is found that SCD is
   up‐regulated in skeletal muscle tissues of patients with osteoporosis
   [[98]24]. Moreover, high SCD activity significantly increases the risk
   for fracture in men [[99]25]. Microsomal glutathione S‐transferase 3
   (MGST3), an oxidative stress protein, is associated with rheumatoid
   arthritis [[100]26]. The expression of TSNAX is found in bone
   marrow‐derived very small embryonic‐like cells [[101]27]. Herein, we
   first found the significant expression of PLEKHA2, PLEKHB1, PNPLA7,
   SCD, MGST3 and TSNAX in osteoporosis of postmenopausal women, which may
   be valuable in understanding the pathology mechanism of the disease.

   Under the threshold of P < 0.05 and |△β| > 0.2, we also found some
   significantly differentially methylated genes at the CpG islands,
   including two significantly hypermethylated genes [PRKCZ and G protein
   subunit alpha 11 (GAN11)] and one significantly hypomethylated gene
   (COL4A1). It is believed that the activation of PRKCZ leads to the
   production of reactive oxygen species and facilitates osteoclast
   differentiation in synovium tissue of patients with rheumatoid
   arthritis [[102]28]. In addition, PRKCZ is a potential prognosis marker
   for patients with osteosarcoma [[103]29]. GAN11 is involved in skeletal
   growth. It is noted that GAN11 plays a key role in osteogenesis and
   chondrogenesis of osteoarthritis [[104]30]. COL4A1 is significantly
   associated with the collarbone and thigh bone density [[105]25]. COL4A1
   is up‐regulated in bone tissue of patients with osteoporosis [[106]31].
   It is worth mentioning that regulation of the TGF‐β/Smad2/COL4A1
   signaling pathway promotes osteogenic differentiation of bone marrow
   stromal cells and is of great significance for the new treatments
   strategy for postmenopausal osteoporosis [[107]32]. Our result
   indicated that PRKCZ, GAN11 and COL4A1 may be associated with the
   development of osteoporosis in postmenopausal women.

   In addition, we found four significantly differentially methylated
   genes, including three significantly hypermethylated genes (SOX6, ACE
   and SYK) and one significantly hypomethylated gene (TGFB3), under the
   threshold of P < 0.05. SRY‐box transcription factor 6 (SOX6), a
   cartilage‐expressed transcription factor, plays an important essential
   role in cartilage formation. The expression of SOX6 is decreased in
   primary cultured osteoblasts from the patient with high bone mass
   [[108]33]. In addition, the association between SOX6 and osteoporosis
   has been documented [[109]34]. By combing transcript profiling with DNA
   methylation analyses in bone, Reppe et al. found reduced methylation of
   SOX6 in 26 osteoporotic postmenopausal women [[110]35]. Perhaps,
   analytical method, sample differences or sample size could account for
   the inconsistent result. It is suggested that ACE could convert Ang I
   to Ang II in osteoblasts or osteoclasts [[111]36]. It has been
   demonstrated that SYK is associated with osteoblast differentiation and
   osteoclasts resorption [[112]37]. It is noted that SYK is considered as
   a possible target for rheumatoid arthritis treatment because of its
   biologic roles within bone metabolism [[113]31]. TGFB3 could promote
   osteoblastogenesis at various stages. The expression of TGFB3 was
   detected in osteoblasts of patients with osteoporosis [[114]38]. Our
   findings suggested that SOX6, ACE, SYK and TGFB3 may play important
   roles in the bone formation of osteoporosis in postmenopausal women.

   In addition to the earlier differentially methylated genes, we also
   found five important signaling pathways, including calcium, cGMP‐PKG,
   endocytosis, Rap1 and AMPK, in postmenopausal women with osteoporosis.
   Furthermore, several previously discussed significantly differentially
   methylated genes were enriched in these signaling pathways. For
   example, GNA11 was involved in both the calcium signaling pathway and
   the cGMP‐PKG signaling pathway, PRKCZ was involved in both endocytosis
   and the Rap1 signaling pathway, and SCD was involved in the AMPK
   signaling pathway. In bone cells, osteoblasts, osteoclasts and
   osteocytes contain the calcium‐sensing receptor that is activated by
   extracellular calcium ion. Inadequate intake of calcium leads to
   increased bone loss. For patients with osteoporosis, ingesting adequate
   calcium through supplementation or diet modification is part of
   standard care. The phosphatidylinositol 3‐kinase/Akt/endothelial nitric
   oxide synthase/nitric oxide/cGMP/PKG signaling pathway is involved in
   the osteogenesis of bone marrow mesenchymal stem cells [[115]39]. It is
   found that osteoclasts ingest bisphosphonates (a mainstay in treating
   and preventing osteoporosis) through endocytosis [[116]40]. Rap1
   promotes talin/integrin recognition. In osteoclasts of mice, specific
   deletion of Rap1 will yield similar osteopetrosis syndrome [[117]41].
   The AMPK signaling pathway is involved in osteoblastic differentiation
   in osteoblasts and bone cells. It is worth mentioning that the AMPK
   signaling pathway is associated with postmenopausal osteoporosis
   [[118]42]. Our result showed that the earlier signaling pathways could
   be involved in the development of osteoporosis in postmenopausal women.

Conclusions

   In summary, 13 differentially methylated genes, including PLEKHA2,
   PLEKHB1, PNPLA7, SCD, MGST3, TSNAX, PRKCZ, GNA11, COL4A1, SOX6, ACE,
   SYK and TGFB3, and 5 related signaling pathways (calcium, cGMP‐PKG,
   endocytosis, Rap1 and AMPK) were identified in postmenopausal women
   with osteoporosis in this study. Our study may provide a novel DNA
   methylation molecular mechanism of postmenopausal osteoporosis.
   However, there are limitations of our study. First, the sample size was
   small. A large number of subjects is needed for further research.
   Second, some in vitro experiments, such as fluorescence quantitative
   PCR, western blotting and immunohistochemistry, are further needed to
   validate the expression of identified differentially methylated genes
   in large numbers of cancellous bone samples. Third, we did not
   investigate the deeper molecular mechanism of the disease. Additional
   cell experiments and animal models are further needed for
   investigation.

Author contributions

   HW, WJ, ZW and SL analyzed and interpreted the data. YZ and LY were
   major contributors in writing the manuscript. YL and XC designed the
   project. All authors read and approved the final manuscript.

Conflict of interest

   The authors declare no conflict of interest.

Supporting information

   Fig. S1. The flow charts for participants selected for the study.
   [119]Click here for additional data file.^ (236KB, tif)

   Table S1. The bisulfite conversion of genomic DNA in the process of
   quality control.
   [120]Click here for additional data file.^ (39.5KB, doc)

   Table S2. 15309 CpG significantly differentially methylated sites with
   P < 0.05.
   [121]Click here for additional data file.^ (515KB, xls)

   Table S3. Enriched signaling pathways of significantly differentially
   methylated genes.
   [122]Click here for additional data file.^ (11.5KB, xlsx)

Acknowledgements