Comprehensive Analysis of Differentially Expressed Circular Rnas in Patients with Senile Osteoporotic Vertebral Compression Fracture

Hindawi BioMed Research International Volume 2020, Article ID 4951251, 13 pages https://doi.org/10.1155/2020/4951251

Research Article Comprehensive Analysis of Differentially Expressed Circular RNAs in Patients with Senile Osteoporotic Vertebral Compression Fracture

Xiaocong Yao,1 Minbo Liu,1 Fang Jin,1 and Zhongxin Zhu 1,2

1Department of Osteoporosis Care and Control, The First People’s Hospital of Xiaoshan District, Hangzhou, Zhejiang 311200, China 2Institute of Orthopaedics and Traumatology of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China

Correspondence should be addressed to Zhongxin Zhu; [email protected]

Received 1 May 2020; Revised 18 August 2020; Accepted 24 August 2020; Published 5 October 2020

Academic Editor: Takashi Yazawa

Copyright © 2020 Xiaocong Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Aim. Circular RNAs (circRNAs) have been found to contribute to the regulation of many diseases and are abundantly expressed in various organisms. The present study is aimed at systematically characterizing the circRNA expression profiles in patients with senile osteoporotic vertebral compression fracture (OVCF) and predicting the potential functions of the regulatory networks correlated with these differentially expressed circRNAs. Methods. The circRNA expression profile in patients with senile OVCF was explored by using RNA sequencing. The prediction of the enriched signaling pathways and circRNA-miRNA networks was conducted by bioinformatics analysis. Real-time quantitative PCR was used to validate the selected differentially expressed circRNAs from 20 patients with senile OVCF relative to 20 matched healthy controls. Results. A total of 884 differentially expressed circRNAs were identified, of which 554 were upregulated and 330 were downregulated. The top 15 signaling pathways associated with these differentially expressed circRNAs were predicted. The result of qRT-PCR of the selected circRNAs was consistent with RNA sequencing. Conclusions. CircRNAs are differentially expressed in patients with senile OVCF, which might contribute to the pathophysiological mechanism of senile osteoporosis.

1. Introduction osteoporosis, a common age-related degenerative disease, is characterized by impaired bone formation principally with Osteoporosis has been the most common age-related bone a low bone turnover state [5, 6], whereas postmenopausal disorder characterized by low bone mineral density (BMD) osteoporosis is related to excessive bone absorption owing and deterioration of microarchitecture with increased bone to estrogen deficiency [7]. Despite the advances in therapeu- fragility and subsequent susceptibility to fractures [1, 2]. tic strategies, the diagnosis of senile osteoporosis at an early Senior population with osteoporosis often suffers from a fra- stage and the poor prognosis remains challenging. Therefore, gility fracture, which will remarkably influence their life qual- there is an urgent need for a better understanding of the ity and increase family economic burden [3]. With the aging pathogenesis of senile osteoporosis in order to identify novel of the population, this burden has been increasing drastically. biomarkers and to develop new treatment strategies for the In addition, every low-energy fracture is linked to a raised prevention and treatment of senile osteoporosis. risk of a subsequent fracture, which indicates a high risk of Emerging evidences demonstrate that microRNAs poor outcomes, and this risk is higher in men than women (miRNAs) are essential for bone remodeling and regeneration in the elderly [4]. In spite of its severe harm, osteoporosis is through the posttranscriptional regulation of gene expression normally asymptomatic clinically at the early stage, and it is [8–10]. With the development of next-generation sequencing always ignored until the first fragility fracture happens. technology, more noncoding RNAs have been discovered. As Clinically, primary osteoporosis can be divided into post- a large class of noncoding RNAs, circular RNAs (circRNAs) menopausal osteoporosis and senile osteoporosis. Senile have multiple biological properties, such as high conservation, 2 BioMed Research International widespread expression, specific expression, and high stability 2.5. Real-Time Quantitative PCR (qRT- PCR). We selected [11–13]. Furthermore, some studies have reported that cir- circ_0079449 and circ_0122913 to validate the expression cRNAs were involved in age-related disease and played impor- by qRT-PCR in 20 pairs of blood tissues from senile OVCF tant roles in many biological processes as miRNA sponges patients and normal controls. The qRT-PCR was carried [14]. Nevertheless, information about circRNAs on senile out by the Geneseed qPCR SYBR Green Master Mix on osteoporosis is very limited. ABI 7500 system. We used glyceraldehyde 3-phosphate In this study, we used RNA sequencing, a powerful and dehydrogenase (GAPDH) as the internal control. The rela- ΔΔ unbiased approach to identify and analyze the differences tive gene expression levels were analyzed using 2- ct method in expression profiles of circRNAs and their regulatory inter- against GAPDH for normalization. The primer sequences action networks in patients with senile osteoporotic vertebral were as follows: circ_0079449, forward: 5′-ATTACTCTG fi compression fracture (OVCF). These ndings may help TCGAAGTTGAA-3′, reverse: 5′-ATCACATACAGCTTTT elucidate the mechanism underlying senile osteoporosis TGTT-3′; circ_0122913, forward: 5′-GTTGGAACATC development and uncover novel diagnostic biomarkers for ATAAACGA-3′, reverse: 5′-TGATTTCAACTTGTTCTTC senile osteoporosis. T-3′. Their splicing junction sites are shown in Figure 1(a). 2. Materials and Methods Statistical analysis was performed by SPSS 24.0 software with a significance level of P <0:05. 2.1. Patients and Samples. This study was approved by the Ethics Committee of our hospital. Fresh peripheral blood 3. Results and written informed consent were attained from patients with newly diagnosed senile OVCF and healthy volunteers 3.1. Expression Profiles of Differentially Expressed circRNAs. receiving a regular physical examination at the same hospital. RNA sequencing was performed to identify circRNAs in The diagnosis for senile OVCF was confirmed through mag- peripheral blood in three patients with senile OVCF and netic resonance imaging, dual-energy X-ray absorptiometry, three healthy controls. We discovered 884 differentially and the history of patients. To decrease sources of circRNA expressed circRNAs (554 upregulated and 330 downregu- expression heterogeneity not connected with disease status, lated). The variations in circRNA expression were demon- selected subjects were all male, with similar age and body strated by scatter maps and volcano plots (Figures 2(a) and mass index. 2 ml blood was collected in ethylenediaminetet- 2(b)). Hierarchical clustering heat map analysis presented a raacetic acid (EDTA) tube and mixed with three volumes of recognizable circRNA expression profile in these two groups RNASaferTM LS Reagent (Magen, Guangzhou, China), then (Figure 2(c)). stored at -80C for batch processing. 3.2. GO, KEGG, Reactome Analysis, and circRNA–miRNA 2.2. RNA Extraction and High-Throughput Sequencing Network Analysis. To explore the role of differentially Analysis. Total RNA was extracted by Hipure PX Blood expressed circRNAs, GO, KEGG, and Reactome analyses RNA Mini Kit (Magen) according to the manufacturer’s pro- and circRNA–miRNA network analysis were performed. tocol. The concentration and integrity of the isolated RNA The top three GO terms in the biological process (BP) were were evaluated with Qubit 3.0 Fluorometer (Invitrogen, “DNA replication, nuclear chromosome segregation, mitotic Carlsbad, California) and Agilent 2100 Bioanalyzer (Applied nuclear division”, and “peptidyl−serine phosphorylation, Biosystems, Carlsbad, CA). High-throughput sequencing protein methylation, histone methylation” for upregulated was performed by Illumina Hiseq Xten to determine cir- and downregulated circRNAs, respectively. The top three cRNAs in patients with senile OVCF (n =3) and the controls GO terms in the cellular component (CC) were “chromosome, (n =3). centromeric region, microtubule organizing center part, con- ” “ 2.3. Data Analysis. We used Bowtie2 version 2.1.0 to map densed chromosome ,and ribonucleoprotein granule, nuclear the reads to the latest UCSC transcript set and RSEM transcription factor complex, cytoplasmic ribonucleoprotein ” v1.2.15 to estimate the gene expression levels. We performed granule for upregulated and downregulated circRNAs, respec- TMM to normalize the gene expression. We used edgeR pro- tively. The top three GO terms in the molecular function (MF) “ − fi gram to perform quantile normalization and subsequent data were histone binding, protein C terminus binding, modi ca- − ” “ − processing. We used STAR to map the reads to the genome. tion dependent protein binding ,and Ras guanyl nucleotide We used DCC to identify the circRNAs and estimate their exchange factor activity, phosphatidylinositol binding, histone ” expression. CircRNAs were considered differentially expressed methyltransferase activity for upregulated and downregulated according to P <0:01 and absolute log fold change >2. circRNAs, respectively. The top 10 most enriched GO terms of the upregulated 2.4. Bioinformatics Analysis. Gene ontology (GO), Kyoto and downregulated circRNAs in BP, CC, and MF are shown Encyclopedia of Genes and Genomes (KEGG), and Reac- in Figures 3(a) and 4(a), respectively. The top three KEGG tome pathway analyses of the differentially expressed cir- pathways were “RNA transport, cell cycle, and Hippo signal- cRNAs were performed by clusterProfiler R package. The ing pathway”, and “MAPK signaling pathway, human T−cell related target miRNAs of circRNAs were identified by leukemia virus 1 infection, and T cell receptor signaling path- miRanda (v3.3). The circRNA-miRNA-mRNA network was way” for upregulated and downregulated circRNAs, respec- constructed by Cytoscape software (v3.7.1). tively. The top three Reactome pathways were “M Phase, BioMed Research International 3

circ_0079449

50 60 70 80 90 100

circ_0122913

30 40 50 60 70 80

(a) 2.5 6 P = 0.0079 P = 0.0066

2.0 4 1.5

1.0 2 0.5 Relative expression level expression Relative level expression Relative

0.0 0 Control Senile OVCF Control Senile OVCF circ_0079449 circ_0122913 (b)

Figure 1: Continued. 4 BioMed Research International

ZCCHC7 CAMK2N1

ANTXR2 OTUD6B EPHA5

KIF5A GORAB

hsa-miR-6719-3p ANKRD26

DUSP6

hsa-miR-4797-5p

STXBP1 ZNF716 OLFM3

hsa_circ_0079449 HCN3 TNFRSF1A hsa-miR-122b-5p

FAM167A KPNA4 MBTD1 hsa-miR-449a

VPS26A DLL1

hsa-miR-5584-5p

FAM76A STK10 RAP1GDS1

IMPAD1

POU3F4 PPP1CB

RBM47 RIT2

PCDH15 DKC1 CREBRF

RNF166 NLRP5

hsa-miR-4778-3p PINX1

RBPMS

hsa-miR-5691

CYP3A5 ALX1 NEXMIF

hsa_circ_0122913 TANC2 KLHDC1 hsa-miR-501-5p

BACH2 ARF6 PEX12 hsa-let-7a-2-3p

RIPK2 LRIG1

hsa-miR-500a-5p

KCNJ6 NUP155 ARFGEF1

TM9SF1

SNX13 RAPGEF4

(c)

Figure 1: The qRT-PCR validation in the 20 paired blood samples from patients with senile OVCF compared with normal group and circRNA-miRNA-mRNA network prediction of circ_0079449 and circ_0122913. (a) The splicing junction sites of circ_0079449 and circ_ 0122913 were indicated by black arrow. (b) The relative expression of circ_0079449 and circ_0122913. (c) The top 5 miRNAs potentially regulated by circ_0079449 and circ_0122913, respectively, and top 5 target genes of each miRNA. OVCF, osteoporotic vertebral compression fracture. BioMed Research International 5

4 −log10 P−value

−4 0 4 log2 fold change

Up regulated 554 Down regulated 330 (a) 3

1 Senile OVCF log10cpm Senile OVCF

0 1 2 3 Control log10cpm

Down regulated 330 Up regulated 554 (b)

Figure 2: Continued. 6 BioMed Research International

Colour key

−2 −1 0 1 2 Value Control 1 Control 2 Control 3 Senile OVCF 1 Senile OVCF 2 Senile OVCF 3 (c)

Figure 2: Next-generation sequencing to identify the differences in expression profiles of circRNAs between the senile OVCF and control groups. (a, b) The volcano plot and scatter plot showed the dysregulated circRNAs. Green and red denote significantly downregulated circRNAs and upregulated circRNAs, respectively. (c) Hierarchical clustering heat map showes the circRNAs with altered expressions in senile OVCF samples compared with controls. Each column represents one sample and each row represents one circRNA. OVCF: osteoporotic vertebral compression fracture; circRNA: circular RNA. transcriptional regulation by TP53, and DNA repair”, and was consistent with the sequencing data and showed signifi- “chromatin organization, chromatin modifying enzymes, cant differences in expression for circ_0079449 and circ_ and regulation of TP53 activity” for upregulated and down- 0122913 (Figure 1(b)). Next, we showed the five miRNAs regulated circRNAs, respectively. The top 15 KEGG and that most potentially bind to circ_0079449 or circ_0122913, Reactome pathways related to the upregulated and downreg- and the top five target genes to each miRNA in the illustra- ulated circRNAs are shown in Figures 3(b), 3(c), 4(b) and tion (Figure 1(c)). 4(c), respectively. The circRNA–miRNA coexpression network analysis of these differentially expressed circRNAs is 4. Discussion shown in Figure 5. In the present study, after strict filtering of approximately 180 3.3. qRT-PCR Validation and circRNA-miRNA-mRNA million reads, we detected 76547 circRNAs, including 884 Network of the Selected circRNAs. The result of qRT-PCR circRNAs that were differentially expressed between patients BioMed Research International 7

Up-regulated

0.06 22

18 18 18 17 16 16 16 0.04 15 14 14 13 12 11 11 10 10 Gene ratio 99 999

0.02 777 77 66 6

0.00

Midbody

Histone binding Histone

DNA replication DNA

RNA localization RNA

Site of DNA damage DNA of Site

Spindle organization Spindle

Damaged DNA binding DNA Damaged

Mitotic nuclear division nuclear Mitotic

Nuclear replication fork replication Nuclear

Condensed chromosome Condensed

Ribonucleoprotein granule Ribonucleoprotein

Methyltransferase complex Methyltransferase

Androgen receptor binding receptor Androgen

Protein C−terminus binding C−terminus Protein

Ubiquitinyl hydrolase activity hydrolase Ubiquitinyl

Ubiquitin−like protein binding protein Ubiquitin−like

DNA−dependent ATPase activity ATPase DNA−dependent

Nuclear chromosome segregation chromosome Nuclear

Chromosome, centromeric region centromeric Chromosome,

Protein localization to centrosome to localization Protein

Transcriptional repressor complex repressor Transcriptional

Microtubule organizing center part center organizing Microtubule

Histone methyltransferase complex methyltransferase Histone

Mitotic sister chromatid segregation chromatid sister Mitotic

Regulation of chromatin organization chromatin of Regulation

Methylation−dependent protein binding protein Methylation−dependent

Modifcation−dependent protein binding protein Modifcation−dependent

Positive regulation of chromatin organizati... chromatin of regulation Positive

Protein localization to microtubule organiz... microtubule to localization Protein Tiol−dependent ubiquitin−specifc protease... ubiquitin−specifc Tiol−dependent

Biological process Cellular component Molecular function (a)

Figure 3: Continued. 8 BioMed Research International

P value RNA transport Cell cycle 0.03 Hippo signaling pathway Ubiquitin mediated proteolysis Fanconi anemia pathway mRNA surveillance pathway Choline metabolism in cancer 0.02

Basal transcription factors Lysine degradation Mitophagy - animal

RNA degradation 0.01 Colorectal cancer Ferroptosis ABC transporters Hippo signaling pathway Up-regulated

0.0 2.5 5.0 7.5 10.0 Gene count (b) P-value M phase 0.00100 Transcriptional regulation by TP53 DNA repair Cell cycle checkpoints Mitotic prometaphase 0.00075 Regulation of TP53 activity Mitotic spindle checkpoint Regulation of lipid metabolism 0.00050 Resolution of sister chromatid Amplifcation signal from unattach.. Amplifcation signal from kinetoch...

Regulation of PLK1 activity 0.00025 SUMOylation of DNA replication Resolution of D−loop structure syn. Resolution of D−loop structure hol. Up-regulated

0 5 10 15 20 Gene count (c)

Figure 3: GO, KEGG, and Reactome analyses of upregulated circRNAs. (a) Top 10 GO analysis to demonstrate the molecular functions of upregulated circRNAs (b) Top 15 significant enrichment KEGG pathways of upregulated circRNAs. (c) Top 15 significant enrichment Reactome pathways of upregulated circRNAs. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; circRNA: circular RNA. with senile OVCF and control individuals. Based on GO, tory interaction networks were constructed. These results KEGG, and Reactome analyses, the functions of differentially will be beneficial in future investigations of the molecular expressed circRNAs were enriched. Moreover, their regula- mechanism associated with senile osteoporosis. BioMed Research International 9

Down-regulated 14

0.04 10 10 9 9 8 8 7 777 66 6 Gene ratio 0.02 5 5 5 4 444 44 3 33 3333

0.00

Aggresome

Inclusion body Inclusion

ESCRT complex ESCRT

ethylation m Protein

Histone methylation Histone

Ran GTPase binding GTPase Ran

Immunological synapse Immunological

Protein kinase C activity C kinase Protein

Cytoplasmic stress granule stress Cytoplasmic

Ribonucleoprotein granule Ribonucleoprotein

mRNA export from nucleus from export mRNA

Phosphatidylinositol binding Phosphatidylinositol

ere capping ere telom of Regulation

Peptidyl−serine phosphorylation Peptidyl−serine

MAP kinase kinase kinase activity kinase kinase kinase MAP

Histone methyltransferase activity methyltransferase Histone

Regulation of histone modifcation histone of Regulation

bHLH transcription factor binding factor transcription bHLH

Histone methyltransferase complex methyltransferase Histone

Nuclear transcription factor complex factor transcription Nuclear

atin organization atin chrom of Regulation

achinery binding achinery m transcription Basal

Cytoplasmic ribonucleoprotein granule ribonucleoprotein Cytoplasmic

Regulation of double−strand break repair break double−strand of Regulation

Intrinsic component of organelle membrane organelle of component Intrinsic

Ras guanyl−nucleotide exchange factor activ... factor exchange guanyl−nucleotide Ras

Nucleotide−excision repair, preincision com... preincision repair, Nucleotide−excision

Tiol−dependent ubiquitin−specifc protease... ubiquitin−specifc Tiol−dependent

NA polymerase II transcription machi... transcription II polymerase NA R Basal mRNA−containing ribonucleoprotein complex e... complex ribonucleoprotein mRNA−containing Biological process Cellular component Molecular function (a)

Figure 4: Continued. 10 BioMed Research International

P-value MAPK signaling pathway 0.03 Human T−cell leukemia virus T cell receptor signaling pathway Cellular senescence Tight junction PD−L1 expression and PD−1 check.. 0.02 T1 and T2 cell diferentiation Infammatory mediator regulation Glucagon signaling pathway T17 cell diferentiation 0.01 Adipocytokine signaling pathway Peroxisome mRNA surveillance pathway Nucleotide excision repair Fatty acid biosynthesis Down-regulated

0369 Gene count (b) P-value Chromatin organization Chromatin modifying enzymes Regulation of TP53 activity 0.015 Cellular response to heat stress Sphingolipid metabolism Efects of PIP2 hydrolysis G alpha (z) signalling events 0.010 Regulation of TP53 activity methyla Regulation of TP53 activity acetyla Endosomal sorting complex FCERI mediated Ca+2 mobilization 0.005 Regulation of TP53 degradation HDR through single strand Interleukin−35 signalling Te activation of arylsulfatases Down-regulated

0.0 2.5 5.0 7.5 10.0 12.5 Gene count (c)

Figure 4: GO, KEGG, and Reactome analyses of downregulated circRNAs. (a) Top 10 GO analysis to demonstrate the molecular functions of upregulated circRNAs (b) Top 15 signiﬁcant enrichment KEGG pathways of downregulated circRNAs. (c) Top 15 signiﬁcant enrichment Reactome pathways of downregulated circRNAs. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; circRNA: circular RNA.

miRNA is one kind of most studied regulators in the bone miRNA-9-5p was lowly expressed in peripheral blood of remodeling process and likely to be the diagnostic biomarker osteoporosis patients and promoted the occurrence and pro- for osteoporosis [15]. Previous studies revealed miRNAs gression of osteoporosis through inhibiting osteogenesis and might play critical roles in the development of osteoporosis. promoting adipogenesis via targeting Wnt3a. In addition, For example, Mäkitie et al. [16] found circulating miRNAs miRNA-based therapies have shown great potential to in serum reﬂected status of WNT1 mutation, which leads become novel approaches against osteoporosis and associ- to skeletal pathologies. Zhang et al. [17] found that ated fractures [8]. As miRNA sponges, circRNAs can adsorb BioMed Research International 11

Figure 5: The potential regulatory network of circRNA-miRNA interactions in peripheral blood from patients with senile OVCF. Red triangle node and blue triangle node represent upregulated and downregulated circRNAs,respectively. Blue circular node represents miRNAs. OVCF: osteoporotic vertebral compression fracture; circRNA: circular RNA; miRNA: microRNA.

miRNAs and eliminate the inhibitory effects of miRNAs on Even though we identified the differentially expressed cir- their target genes, leading to upregulation of target genes cRNAs in patients with senile OVCF, the underlying mecha- [18]. Besides, circRNAs are more stable than linear RNA nism is still poorly understood. The GO terms provide due to their closed-loop structure [19]. CircRNAs have been computable knowledge regarding the activities of gene prod- considered potentially promising blood biomarkers in some ucts and have been good predictors of them [25]. KEGG is an musculoskeletal-related disease [20–22]. However, informa- integrated database for biological interpretation of genome tion about circRNAs on osteoporosis is limited. sequences and widely used in the enrichment analysis [26]. Zhao et al. [23] used chip microarray analysis to investi- Reactome is a knowledgebase of biomolecular pathways gate circRNA expression in postmenopausal osteoporosis and can be combined with other databases [27, 28]. There- and healthy menopausal women. Their data identified 203 fore, we systematically predicted the circRNA-related func- upregulated and 178 downregulated circRNAs and revealed tions and corresponding pathways in senile osteoporosis circ_0001275 was a potential diagnostic biomarker according with GO, KEGG, and Reactome analyses. In the present to qRT-PCR results. Huang et al. [24] performed microarray study, we found several pathways in the enrichment analysis analysis and PCR validation, and their result showed that were associated with osteoporosis. These pathways include circ_0006873 and circ_0002060 were linked to the low the mitogen-activated protein kinase (MAPK) signaling BMD state. In our study, rather than investigating by micro- pathway, DNA damage, DNA repair, and Th17 cell differen- array which could identify only thousands of circRNAs, we tiation. Previous studies reported that the MAPK signaling chose to sequence all circRNAs that were attained in blood pathway plays important roles in bone metabolism [29–31]. samples from patients with newly diagnosed senile OVCF Other pathways were also involved in the development of and healthy controls. osteoporosis, such as DNA damage, DNA repair, and Th17 12 BioMed Research International cell differentiation [32–35]. The results suggest that these cir- [3] N. A. Mohd-Tahir and S. C. Li, “Economic burden of cRNAs might play roles in regulating physiological processes osteoporosis-related hip fracture in Asia: a systematic review,” of osteoporosis. Osteoporosis International, vol. 28, no. 7, pp. 2035–2044, 2017. There are some limitations in this study. First, our [4] J. R. Center, “Fracture burden: what two and a half decades of patients were carefully selected. We chose male and age, Dubbo osteoporosis epidemiology study data reveal about BMI-matched subjects from a single center. Although this clinical outcomes of osteoporosis,” Current Osteoporosis – decreased heterogeneity for this initial study, it would limit Reports, vol. 15, no. 2, pp. 88 95, 2017. the generalizability of our findings. Second, the sample of this [5] Y. Xie, Y. Gao, L. Zhang, Y. Chen, W. Ge, and P. Tang, “ study is small. Therefore, a larger number of clinical samples Involvement of serum-derived exosomes of elderly patients fi with bone loss in failure of bone remodeling via alteration of are needed to con rm our results. Furthermore, the mecha- ” nism and function of the differentially expressed circRNAs exosomal bone-related proteins, Aging Cell, vol. 17, no. 3, article e12758, 2018. should be further verified by strict molecular biological [6] J. N. Farr, M. Xu, M. M. Weivoda et al., “Targeting cellular experiments research and studied deeply in future work. ” fi senescence prevents age-related bone loss in mice, Nature In summary, we reported the circRNA expression pro le Medicine, vol. 23, no. 9, pp. 1072–1079, 2017. in patients with senile OVCF by RNA sequencing analyses. [7] J. Kiernan, J. E. Davies, and W. L. Stanford, “Concise review: mus- Their regulatory interaction networks were constructed based culoskeletal stem cells to treat age-related osteoporosis,” Stem on the sequencing data. These original discoveries might pro- Cells Translational Medicine,vol.6,no.10,pp.1930–1939, 2017. vide some clues for the biological functions of circRNAs in the [8] X. Sun, Q. Guo, W. Wei, S. Robertson, Y. Yuan, and X. Luo, “Cur- pathophysiological mechanism of senile osteoporosis. rent progress on MicroRNA-Based gene delivery in the treatment of osteoporosis and osteoporotic fracture,” International Journal Data Availability of Endocrinology, vol. 2019, Article ID 6782653, 17 pages, 2019. [9] W. Zhao, G. Shen, H. Ren et al., “Therapeutic potential of The RNA-seq data have been uploaded to NCBI SRA data- microRNAs in osteoporosis function by regulating the biology base (Number: PRJNA562388). of cells related to bone homeostasis,” Journal of Cellular Phys- iology, vol. 233, no. 12, pp. 9191–9208, 2018. Ethical Approval [10] Q. Feng, S. Zheng, and J. Zheng, “The emerging role of micro- RNAs in bone remodeling and its therapeutic implications for The Institutional Review Board of The Xiaoshan First osteoporosis,” Bioscience Reports, vol. 38, no. 3, 2018. ’ People s Hospital approved this study (Protocol Number: [11] R. Yao, H. Zou, and W. Liao, “Prospect of circular RNA in 2019-XS-001). hepatocellular carcinoma: a novel potential biomarker and therapeutic target,” Frontiers in Oncology, vol. 8, 2018. Conflicts of Interest [12] L. L. Chen, “The biogenesis and emerging roles of circular RNAs,” Nature Reviews. Molecular Cell Biology, vol. 17, The authors declare no conflicts of interest. no. 4, pp. 205–211, 2016. [13] W. R. Jeck, J. A. Sorrentino, K. Wang et al., “Circular RNAs are Authors’ Contributions abundant, conserved, and associated with ALU repeats,” RNA, vol. 19, no. 2, pp. 141–157, 2013. ZZ was responsible for the concept and designing of this [14] H. Cai, Y. Li, J. D. Niringiyumukiza, P. Su, and W. Xiang, “Cir- study. YX and LM performed the experiments and drafted cular RNA involvement in aging: an emerging player with the manuscript. FJ and ZZ reviewed the manuscript. All great potential,” Mechanisms of Ageing and Development, authors read and approved the final manuscript. Xiaocong vol. 178, pp. 16–24, 2019. Yao and Minbo Liu are the first co-authors and contributed [15] M. Materozzi, D. Merlotti, L. Gennari, and S. Bianciardi, “The equally to this work. potential role of miRNAs as new biomarkers for osteoporosis,” International Journal of Endocrinology, vol. 2018, Article ID Acknowledgments 2342860, 10 pages, 2018. [16] R. E. Makitie, M. Hackl, R. Niinimaki, S. Kakko, J. Grillari, and This research is supported by major scientific and technolog- O. Makitie, “Altered microRNA profile in osteoporosis caused ical plan projects for social development in Xiaoshan District by impaired WNT signaling,” The Journal of Clinical Endocri- (Number: 2019212). nology and Metabolism, vol. 103, no. 5, pp. 1985–1996, 2018. [17] H. G. Zhang, X. B. Wang, H. Zhao, and C. N. Zhou, “Micro- References RNA-9-5p promotes osteoporosis development through inhibiting osteogenesis and promoting adipogenesis via targeting ” [1] J. A. Kanis, E. V. McCloskey, H. Johansson, A. Oden, L. J. Melton Wnt3a, European Review for Medical and Pharmacological – 3rd, and N. Khaltaev, “A reference standard for the description of Sciences, vol. 23, no. 2, pp. 456 463, 2019. osteoporosis,” Bone,vol.42,no.3,pp.467–475, 2008. [18] T. B. Hansen, T. I. Jensen, B. H. Clausen et al., “Natural RNA [2] E. Jacobs, R. Senden, C. McCrum, L. W. van Rhijn, K. Meijer, circles function as efficient microRNA sponges,” Nature, and P. C. Willems, “Effect of a semirigid thoracolumbar ortho- vol. 495, no. 7441, pp. 384–388, 2013. sis on gait and sagittal alignment in patients with an osteopo- [19] X. Li, L. Yang, and L. L. Chen, “The biogenesis, functions, and rotic vertebral compression fracture,” Clinical Interventions in challenges of circular RNAs,” Molecular Cell, vol. 71, no. 3, Aging, vol. 14, pp. 671–680, 2019. pp. 428–442, 2018. BioMed Research International 13

[20] A. Dolinar, B. Koritnik, D. Glavač, and M. Ravnik-Glavač, “Circular RNAs as potential blood biomarkers in amyotrophic lateral sclerosis,” Molecular Neurobiology, vol. 56, no. 12, pp. 8052–8062, 2019. [21] Q. Luo, L. Zhang, X. Li et al., “Identification of circular RNAs hsa_circ_0044235 in peripheral blood as novel biomarkers for rheumatoid arthritis,” Clinical and Experimental Immunology, vol. 194, no. 1, pp. 118–124, 2018. [22] L. Iparraguirre, M. Munoz-Culla, I. Prada-Luengo, T. Castillo- Trivino, J. Olascoaga, and D. Otaegui, “Circular RNA profiling reveals that circular RNAs from ANXA2 can be used as new biomarkers for multiple sclerosis,” Human Molecular Genetics, vol. 26, no. 18, pp. 3564–3572, 2017. [23] K. Zhao, Q. Zhao, Z. Guo et al., “Hsa_Circ_0001275: a potential novel diagnostic biomarker for postmenopausal osteoporosis,” Cellular Physiology and Biochemistry, vol. 46, no. 6, pp. 2508–2516, 2018. [24] Y. Huang, J. Xie, and E. Li, “Comprehensive circular RNA profiling reveals circ_0002060 as a potential diagnostic biomarkers for osteoporosis,” Journal of Cellular Biochemistry, vol. 120, no. 9, pp. 15688–15694, 2019. [25] The Gene Ontology Consortium, “The Gene Ontology Resource: 20 years and still GOing strong,” Nucleic Acids Research, vol. 47, no. D1, pp. D330–d338, 2019. [26] M. Kanehisa, Y. Sato, M. Furumichi, K. Morishima, and M. Tanabe, “New approach for understanding genome variations in KEGG,” Nucleic Acids Research, vol. 47, no. D1, pp. D590–d595, 2019. [27] A. Fabregat, F. Korninger, G. Viteri et al., “Reactome graph database: efficient access to complex pathway data,” PLoS Computational Biology, vol. 14, no. 1, article e1005968, 2018. [28] A. Bauer-Mehren, L. I. Furlong, and F. Sanz, “Pathway databases and tools for their exploitation: benefits, current limitations and challenges,” Molecular Systems Biology, vol. 5, no. 1, 2009. [29] H. Wu, B. Hu, X. Zhou et al., “Artemether attenuates LPS- induced inflammatory bone loss by inhibiting osteoclastogen- esis and bone resorption via suppression of MAPK signaling pathway,” Cell Death & Disease, vol. 9, no. 5, 2018. [30] C. Thouverey and J. Caverzasio, “Focus on the p38 MAPK signaling pathway in bone development and maintenance,” Bone- KEy reports, vol. 4, 2015. [31] S. Zheng, Y. B. Wang, Y. L. Yang et al., “LncRNA MALAT1 inhibits osteogenic differentiation of mesenchymal stem cells in osteoporosis rats through MAPK signaling pathway,” Euro- pean Review for Medical and Pharmacological Sciences, vol. 23, no. 11, pp. 4609–4617, 2019. [32] C. Ghayor and F. E. Weber, “Epigenetic regulation of bone remodeling and its impacts in osteoporosis,” International Journal of Molecular Sciences, vol. 17, no. 9, 2016. [33] X. Wu, J. Li, H. Zhang, H. Wang, G. Yin, and D. Miao, “Pyrro- loquinoline quinone prevents testosterone deficiency-induced osteoporosis by stimulating osteoblastic bone formation and inhibiting osteoclastic bone resorption,” American Journal of Translational Research, vol. 9, no. 3, pp. 1230–1242, 2017. [34] L. You, L. Chen, L. Pan, Y. Peng, and J. Chen, “SOST gene inhibits osteogenesis from adipose-derived mesenchymal stem cells by inducing Th17 cell differentiation,” Cellular Physiology and Biochemistry, vol. 48, no. 3, pp. 1030–1040, 2018. [35] R. Zhao, “Immune regulation of bone loss by Th17 cells in oestrogen-deficient osteoporosis,” European Journal of Clini- cal Investigation, vol. 43, no. 11, pp. 1195–1202, 2013.