Skip to main content
Download PDF

Bioinformatics screening and clinical validation of CircRNA and related miRNA in male osteoporosis

BMC Musculoskeletal Disorders volume 26, Article number: 117 (2025) Cite this article

Abstract

Background

The pathogenesis of male osteoporosis (MOP) remains unclear, with the role of genetic factors attracting the attention of researchers. In the present study, we aimed to investigate critical circRNA biomarkers associated with male osteoporosis.

Methods

RNA-sequencing was performed to investigate the circRNA expression profiles between 3 men with osteoporosis and 3 with normal mass density. Then, shared mRNAs between host genes acquired in this present study and mRNAs acquired in previous study were identified to screen vital circRNAs associated with male osteoporosis. PPI networks of shared mRNAs were constructed and the hub genes in the PPI networks were identified with CytoHubba, a plugin in Cytoscape software (3.10.1). Finally, a ceRNA network of four circRNAs derived from three hub genes was constructed. Validation experiments were performed on selected circRNAs and related miRNAs in this ceRNA network using peripheral blood clinical samples.

Results

In total, 657 circRNAs were detected in male osteoporosis. The shared mRNAs were significantly enriched in the metabolic pathways, RNA transport, Ubiquitin mediated proteolysis and Amyotrophic lateral sclerosis. Then, three genes, including SETD2, ATM and XPO1, were identified as hub genes with four algorithms. Ultimately, the ceRNA network, involving 4 circRNAs, 40 miRNAs, and 592 mRNAs, was obtained. Using 35 clinical samples, three potential circRNAs and three miRNAs associated with male osteoporosis were selected for validation. It was ultimately found that three miRNAs were upregulated in MOP, while hsa-circ-9130, novel_circ_0014940 and hsa-circ-0054894 were upregulated, hsa-circ-2484 and novel_circ_0033084 were downregulated in patients with MOP.

Conclusion

We emphasized the roles of several significantly up- and down-regulated circRNAs and four circRNAs derived from three hub genes in male osteoporosis. Differences in expression were confirmed for three miRNAs and five circRNAs in the ceRNA network among patients with male osteoporosis.

Peer Review reports

Introduction

Human bone metabolism is a complex process including bone formation and bone absorption mediated by osteoblasts and osteoclasts [1]. Osteoporosis is a kind of chronic systemic skeletal disorder with the feature of reduced bone mass, deterioration of the microstructure of the bone tissue and increased fragility, caused by a broken balance between bone formation and bone resorption [2]. Low activity of osteoblasts resulted in decreased bone formation while high that of osteoclasts for increased bone resorption [3]. Hence, osteoblast differentiation and osteoclast differentiation plays a significant role in osteoporosis. Considerable effort has been made to understand the mechanisms of osteoporosis, especially in postmenopausal women. Since the incidence of osteoporosis in men is lower than in postmenopausal women, not enough attention has been paid to osteoporosis in men, and the molecular mechanism remains unclear.

The circRNAs are more durable than linear RNAs as they have a stable loop structure that prevents exonuclease-mediated degradation [4]. It has been demonstrated that circRNAs are closely related with osteoporosis through their effects as miRNA sponges by targeting major genes and signaling pathways related to osteoblast and osteoclast differentiation [1]. However, due to the paucity of studies on circRNAs, only a few circRNAs related to osteoblast and osteoclast differentiation have been reported to date. A study reported that circRNA_0016624 promoted osteoblast differentiation by sponging miR-98 and enhanced BMP2 expression [5]. It has been suggested that circRNA_28313/miR-195a/CSF1 axis could regulate RANKL + CSF1-induced osteoclast differentiation in bone marrow monocyte/macrophage (BMM) cells [6]. Further studies may facilitate the development of novel treatments for osteoporosis.

We previously identified expression profiles of mRNA in male osteoporosis by RNA-sequencing and bioinformatics analysis [7]. Currently, we aimed to further investigate circRNAs associated with male osteoporosis by RNA-sequencing and constructing a ceRNA (circRNA-miRNA-mRNA) network. The shared mRNAs in host genes acquired in this present study and mRNAs acquired in previous study were identified to screen vital circRNAs associated with male osteoporosis. A better understanding of the pathogenesis of male osteoporosis will be instrumental in the development of explicit targeting approaches to treat osteoporosis.

Methods

Patients and samples

The RNA-Seq cohort included three men with osteoporosis and three men with normal bone mass density. Osteoporosis was defined by bone mineral density (BMD) measurements of the lumbar spine (L1-L4), femoral neck, and total hip using dual-energy X-ray absorptiometry (DXA) prior to study enrollment and serum sample collection. Individuals with osteoporosis were identified by a T-score of -2.5 or lower at least one skeletal site and a history of fragility fractures, while those with normal bone mass had T-scores above − 1.0 at all measured sites. Exclusion criteria included: (1) history of antiresorptive or pro-osteogenic drug use, except for calcium and vitamin D supplementation; (2) malignancies, bone metastases, endocrine disorders, and other bone metabolism diseases; (3) severe liver or kidney conditions, prolonged bed rest, organ transplantation, and specific medical histories; (4) abnormal biochemical parameters; and (5) lack of informed consent. Table 1 outlines the characteristics of these individuals. This study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (2017-P2-084-01), and conducted in accordance with the Declaration of Helsinki. Peripheral whole blood (2.5 ml) was collected from each patient and control subject for RNA extraction.

Table 1 Patient characteristics
Full size table

RNA sequencing, identification of DEcircRNAs and functional annotation of host genes

Using TRIzol reagent, total RNA was extracted from samples. RNA sequencing was performed based on HiSeq 10X-150PE. Low-quality data were filtered using SOAPnukev1.5.2. The clean reads with high quality were subsequently aligned to the human reference genome (GRCh38.p12) using Hisat2 [8]. CIRI (v2.0.5) and Find_circ (v1.2) were used to detect circRNAs [9, 10]. DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples, takes uniquely mapped reads from RNA-seq data for the two samples with a gene annotation as input [11]. With DEGseq, differentially expressed circRNAs (DEcircRNAs) were identified. Compared with controls, the DEcircRNAs in male osteoporosis were defined with p-value < 0.05 & |log2 FoldChange| > 1. Enrichment analysis with GeneCodis3 for host genes of DEcircRNAs was performed. Pathways with p-value_adj < 0.05 were defined as significantly enriched pathways.

Identification of shared mRNAs

The shared mRNAs in host genes acquired in this present study and mRNAs acquired in previous study were identified to screen vital circRNAs associated with male osteoporosis. With GeneCodis3, GO classification and KEGG pathway enrichment analysis of shared DEmRNAs were conducted. Statistical significance was defined as p-value_adj < 0.05. Online database STRING was employed to analyze the PPI networks. Then, the hub genes in the PPI network were identified with four algorithms, including maximum neighborhood component (MNC), degree, and Edge Percolated component (EPC), in CytoHubba, a plugin in Cytoscape software.

Construction of the ceRNA (circRNA-miRNA-mRNA) regulatory network

Based on miRanda, circRNA-miRNA interaction pairs were acquired. With miRWalk, miRNA-mRNA pairs were obtained as well. According to ceRNA theory, the ceRNA regulatory network was constructed with Cytoscape, by combining circRNA-miRNA pairs and miRNA-mRNA pairs.

Sample processing and qRT-PCR validation

From the constructed ceRNA network, circRNAs with significant differences in osteoporosis patients and the miRNA with the highest coverage were selected for validation in clinical samples. Whole blood was collected from patients using the same method as previously described. Human peripheral blood lymphocytes were isolated from fresh anticoagulated whole blood using lymphocyte separation medium (Solarbio, P8610). Total RNA was extracted using Trizol reagent (Sigma) and quantified with a nucleic acid protein analyzer (Biospec-nan). Reverse transcription PCR (RT-PCR) was performed on the TC-512 PCR system (TECHNE, UK) using the microRNA First-Strand cDNA Synthesis Kit (Novozymes NJ). Real-time quantitative PCR was conducted using the MicroRNAs Quantitative PCR Kit (Novozymes, Nanjing, China) and the ABI 7500 Real-Time PCR System (Applied Biosystems, USA).

Reverse transcription was performed with the PrimeScript RT Reagent Kit (Takara). Quantitative real-time PCR (qRT-PCR) was carried out on the Applied Biosystems® 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using the FastStart Universal SYBR Green Master (Rox) program. Non-specific amplification was monitored with a melting curve, and relative expression levels were calculated using the 2^(−ΔΔCt) method, with triplicate wells for each sample. Primer sequences are provided in Table 2.

Table 2 Primer sequences
Full size table

Results

Identification of DEcircRNAs

The circRNAs were identified by CIRI and Find_circ software. The length distribution of these circRNAs are shown in Fig. 1A. These DEcircRNAs were widely distributed across almost all human chromosomes, including the sex chromosomes (Fig. 1B). Since one gene could generate multiple circRNAs through an alternative back-splicing mechanism [12], we investigated to what extent the alternative back-splicing contributes to circRNA diversity in male osteoporosis. As shown in Fig. 1C, nearly 66% host genes corresponding to the dysregulated circRNAs can produce at least 2 circRNAs. Although ubiquitously locating across whole genomic regions, most circRNAs were back-spliced from exonic region, mainly (76%) consisting of 2–5 exons (Fig. 1D). Among these DEcircRNAs, 39% circRNAs are already recorded in circBase database, and thus 61% are considered novel (Fig. 1E). Compared with normal controls, 657 DEcircRNAs (126 up-regulated and 531 down-regulated DEcircRNAs) were detected in male osteoporosis with p-value < 0.05 and |log2FoldChange| > 1 (Fig. 2). Among these circRNAs, novel_circ_0014940 and hsa_circ_0002484 were the most up-regulated and down-regulated DEcircRNA, respectively (Table 3).

Fig. 1
figure 1

Characterization of circRNAs identified in male osteoporosis. A) The length distribution of identified circRNAs; B) The chromosome distribution of identified circRNAs; C) Number of circRNAs produced from one gene; D) Exon numbers of identified circRNAs; E) Comparison of circRNAs identified in this study and circBase

Full size image
Fig. 2
figure 2

Heatmap and volcano plot displaying dysregulated circRNAs in male osteoporosis

Full size image
Table 3 Top 10 up- and down-regulated DEcircRNAs in male osteoporosis
Full size table

In total, 607 host genes of DEcircRNAs were identified. GO analysis indicated several significantly enriched terms, such as, protein transport (p = 1.11E-11), chromatin organization (p = 2.68E-10), cytosol (p = 2.97E-44) and protein binding (p = 5.09E-72) (Figure S1A-C). KEGG pathway enrichment analysis indicated that several pathways were significantly enriched, including Lysine degradation (p = 1.20E-09), Endocytosis (p = 3.39E-09), Metabolic pathways (p = 1.20E-09) and Ubiquitin mediated proteolysis (p = 8.12E-08) (Figure S1D).

Identification of shared mRNAs

By overlapping 607 host genes with 3296 mRNAs acquired in previous study, 222 common genes were obtained. GO analysis indicated several significantly enriched terms, such as, protein transport (p = 5.06E-20), viral process (p = 3.33E-16), cytoplasm (p = 6.40E-62) and protein binding (p = 3.99E-107) (Fig. 3A-C). KEGG pathway enrichment analysis indicated that several pathways were significantly enriched, including Metabolic pathways (p = 2.17E-12), RNA transport (p = 9.92E-11), Ubiquitin mediated proteolysis (p = 3.32E-09) and Amyotrophic lateral sclerosis (p = 1.79E-08) (Fig. 3D). PPI networks were constructed as shown in Fig. 4, which included 177 nodes and 327 edges. According to the results of four topological analysis methods, three genes, including SETD2, ATM and XPO1, were identified as hub genes (Table 4).

Fig. 3
figure 3

Significantly enriched GO terms and KEGG pathways of common gene found in host genes and DEmRNAs. A) BP, biological process; B) CC, cellular component; C) MF, molecular function; D) KEGG pathways

Full size image
Fig. 4
figure 4

PPI network of common genes

Full size image
Table 4 Top 10 hub genes in four algorithms
Full size table

Construction of the ceRNA regulatory network

In this analysis, FLT3LG was the host gene of novel_circ_0014940, SETD2 was the host gene of hsa_circ_0065165 and novel_circ_0027056, ATM was the host gene of hsa_circ_0024236, and XPO1 -was the host gene of hsa_circ_0054894 (Table 5). Then, miRanda was used to predict the potential target miRNAs of hsa_circ_0065165, novel_circ_0027056, hsa_circ_0024236 and hsa_circ_0054894. Next, miRNA-mRNA pairs were predicted with miRWalk. Ultimately, the ceRNA network, involving 4 circRNAs, 40 miRNAs, and 592 mRNAs, was obtained (Fig. 5). Among them, hsa-miR-17-5p, hsa-miR-106b-5p and hsa-miR-93-5p were the top 3 covered most miRNAs.

Fig. 5
figure 5

CeRNA (circRNA-miRNA-mRNA) regulatory network. The elliptical nodes, inverted triangles and rectangle indicate circRNAs, miRNAs and mRNAs, respectively. Red and green color represent up-regulation and down-regulation, respectively

Full size image
Table 5 CircRNA of host genes as hub genes
Full size table

Clinical samples validation

Out of the initial 40 recruited individuals, 35 met the eligibility criteria for PCR analysis following assessment of serum samples for the absence of hemolysis. The study participants included 21 patients diagnosed with osteoporosis and 14 control individuals with normal bone mass density.

The expression levels of the miRNAs and circRNAs in peripheral blood lymphocytes were evaluated using qRT-PCR. The quantitative results were statistically compared between two subgroups of cases with different bone phenotypes (CTR and OP).

The expression levels of hsa-miR-17-5p, hsa-miR-106b-5p, and hsa-miR-93-5p in peripheral blood lymphocytes were significantly higher in the OP group compared to the control group (Fig. 6a). Furthermore, hsa-circ-9130, novel_circ_0014940 and hsa-circ-0054894 were significantly increased while hsa-circ-2484 and novel_circ_0033084 were decreased in the OP group compared to the control group. There were no significant differences in the expression of hsa-circ-8135, novel_circ_0027056, hsa-circ-0065165 and hsa-circ-0024236 between the two groups (Fig. 6bcd).

Fig. 6
figure 6

a) The expression levels of hsa-miR-17-5p, hsa-miR-106b-5p, and hsa-miR-93-5p in the OP group and the control group. b) The expression levels of hsa-circ-9130, hsa-circ-8135, hsa-circ-2484 in the OP group and the control group. c) The expression levels of novel-circ-0027056, novel-circ-0033084, novel-circ-0014940 in the OP group and the control group. d) The expression levels of hsa-circ-0065165, hsa-circ-0024236, hsa-circ-0054894 in the OP group and the control group

Full size image

Discussion

Osteoblastic bone formation and osteoclastic bone resorption dynamically maintain the bone homeostasis, and an imbalance of this process can be found in numerous bone diseases, including osteoporosis [13]. It is well established that genetic factors play an important role in the pathogenesis of osteoporosis. In this study, a total of 657 DEcircRNAs were detected in male osteoporosis, and a ceRNA network of four circRNAs derived from three hub genes, including SETD2, ATM and XPO1, was constructed.

During the aging process, bone marrow mesenchymal stem cells (BMSCs) exhibit declined osteogenesis accompanied by excess adipogenesis, which will lead to osteoporosis [11]. Set domain containing 2 (SETD2) is a histone methylase encoded by the SETD2 gene was the host gene of hsa_circ_0065165 and novel_circ_0027056specifically catalyzes H3K36me3 [14]. H3K36me3 mediated by SETD2 could regulate the cell fate of mesenchymal stem cells (MSCs) indicating that the regulation of H3K36me3 level by targeting SETD2 may represent a potential therapeutic way for new treatment in metabolic bone diseases, such as osteoporosis [11]. It was been reported that SETD2 has an active role in osteoblast differentiation and SETD2 may serve as an epigenetic target for new treatment options of osteoporosis [15]. In this study, SETD2 was the host gene of hsa_circ_0065165 and novel_circ_0027056, indicating that hsa_circ_0065165 and novel_circ_0027056 may participate in osteoblast differentiation of male osteoporosis. In our study, we predicted that the expression of hsa_circ_0065165 and novel_circ_0027056 may be downregulated in patients with male osteoporosis, however, no significant differences were observed in the clinical validation.

ATM is a Ser/Thr kinase involved in DNA damage response and mainly functions as a regulator of downstream molecules such as p53 and Brca1 to regulate cell cycle progression, apoptosis and DNA repair [16]. It has been reported that ATM has an essential function in the reconstitutive capacity of haematopoietic stem cells (HSCs) [17]. In addition, it has been demonstrated that ATM deficiency leads to osteoporosis mainly as a result of hypogonadism-induced bone resorption together with compromised osteoblast differentiation by modulating the expression of osterix, a lineage-specific transcription factor essential for osteoblast maturation, in Atm -/- mice [16]. ATM inhibition in B cells increased the production of TNFα, IL-6, and RANKL, which were factors known to induce osteoclast formation, suggesting impaired ATM activation in B cells may induce bone loss [18]. In this study, we found that ATM is the host gene of the hsa_circ_0024236, which may imply this circRNA is associated with male osteoporosis, and we predicted that hsa_circ_0024236 may be downregulated in patients with male osteoporosis, however, no significant differences were observed in the clinical sample validation.

The host gene of hsa_circ_0054894 was Exportin 1 (XPO1), also known as chromosome region maintenance 1 (CRM1), is key nuclear export protein that transports cargo proteins with leucine-rich nuclear export sequences from the nucleus to the cytoplasm [19]. It is believed to encode an oncogenic protein and has been reported in various types of cancer [20]. Highly expressed XPO1 was detected in multiple myeloma, a disease characterized by excess bone marrow plasma cells and monoclonal protein, commonly associated with osteoporosis and lytic bone disease [21]. The above content implies that hsa_circ_0054894 may be associated with male osteoporosis. Our study predicted that hsa_circ_0054894 may be downregulated in patients with male osteoporosis, but the clinical sample validation yielded contrary results, indicating that further validation is needed through an increased sample size or multicenter studies.

In this study, novel_circ_0014940 is the most up-regulated DEcircRNA, and the host gene of novel_circ_0014940 is FLT3LG (Fms related receptor tyrosine kinase 3 ligand), a growth factor that binds to FLT3 (CD135), is reported to negatively regulate osteoclast formation [22]. FLT3LG transdifferentiates dendritic cells into osteoclasts in an inflammatory environment by substituting for macrophage colony stimulating factor, which is essential for the proliferation and survival of osteoclast precursors [23, 24]. In addition, higher FLT3LG expression has been associated with poor overall survival in patients with osteosarcoma [25]. Strawberry notch homologue 2 (SBNO2), which is the host gene of hsa_circ_0009130, plays a pivotal role in bone homeostasis in vivo by fine-tuning osteoclast fusion [26]. Gender-specific SBNO2 was identified as a potential target for osteoporosis development in male ankylosing spondylitis patients [27]. Besides, HOMER3 is the host gene of hsa_circ_0008135, a previous study suggested that Homer3 regulates NFATc1 function through its interaction with calcineurin to regulate RANKL-induced osteoclastogenesis and bone metabolism [28]. Hence, we speculated that novel_circ_0014940, hsa_circ_0009130 and hsa_circ_0008135, top 3 most significantly up-regulated circRNAs, were involved in osteoclastogenesis and bone metabolism in male osteoporosis. In our clinical sample validation, both novel_circ_0014940 and hsa_circ_0009130 expressed as predicted, while no significant differences were observed for hsa_circ_0008135.

Moreover, novel_circ_0033084 was significantly down-regulated DEcircRNA, which was hosted by HLA-B (human leukocyte antigen-B). The major histocompatibility complex in humans, known as HLA region, is the most polymorphic human genetic system and it is known as a cluster of genetic markers, associated with several diseases. Several studies have explored the relationships between polymorphisms of HLA-B gene and postmenopausal osteoporosis. As different populations have different HLA polymorphisms, the results are different in different populations. A higher frequency of HLA-B7 has been related to the group of osteoporotic women in a Greek population [29]. In addition, healthy young Japanese women who possessed the HLA-B7 allele had a significant lower peak bone density in comparison to those without the HLA-B7 allele [30]. HLA-B* 3501 was likely an important risk factor for postmenopausal osteoporosis in a Chinese Han population [31]. The clinical validation results are consistent with predictions. These findings may indicate the important role of novel_circ_0033084 in male osteoporosis.

In addition, in the ceRNA network, hsa_circ_0024236 acts as sponge of hsa-miR-17-5p, hsa-miR-106b-5p and hsa-miR-93-5p to capture 112 mRNAs, 103 mRNAs and 101mRNAs, respectively. It has been indicated that hsa-miR-17-5p promotes adipogenesis and inhibit osteogenesis of adipose-derived mesenchymal stem cells (hADSCs) [32]. It was reported that hsa-miR-17-5p was significantly up-regulated in postmenopausal osteoporosis [33]. It was demonstrated that miR-106b-5p negatively regulated osteogenic differentiation of MSCs in vitro [34]. One study reported elevated levels of hsa-miR-93-5p in osteoporotic patients [35]. Another study also showed significant up-regulated hsa-miR-93-5p in serum, tissue and bone cells of osteoporotic patients [36]. CircRNA_0048211 negatively targets miRNA-93-5p to up-regulate BMP2, thus promotes osteoblast differentiation, indicating important role of miRNA-93-5p in osteoporosis [37]. The validation experiments in this study revealed that the expression levels of the aforementioned three miRNAs were significantly elevated in the peripheral blood lymphocytes of patients with osteoporosis.

The clinical validation experiments suggest that some of the circRNAs in this ceRNA network are differentially expressed between osteoporosis patients and people with normal bone mass. Further in-depth studies are necessary to validate this experiment, including expanding the sample size through conducting a multicentre study. Additionally, as peripheral blood samples are highly influenced by individual patient factors, bone marrow progenitor macrophages can be subsequently extracted for further analysis.

In conclusion, we emphasized the roles of several significantly up- and down-regulated DEcircRNAs and also identified three of the most widely covered miRNAs. Additionally, clinical validation experiments were performed on selected circrna and miRNAs, and a portion of the results obtained is consistent with the preliminary predictions.

Data availability

Sequence data that support the findings of this study have been deposited in the NCBI with the primary accession code PRJNA825243.

References

  1. Yang Y, Wang Y, Wang F, Yuan L, Guo Z, Wei Z et al. The roles of miRNA, lncRNA and circRNA in the development of osteoporosis. Biol Res. 2020;53(1).

  2. Rachner TD, Khosla S, Hofbauer LC. Osteoporosis: now and the future. Lancet. 2011;377(9773):1276–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. He T, Liu W, Cao L, Liu Y, Zou Z, Zhong Y, et al. CircRNAs and LncRNAs in osteoporosis. Differentiation. 2020;116:16–25.

    Article  CAS  PubMed  Google Scholar 

  4. Zhao K, Zhao Q, Guo Z, Chen Z, Hu Y, Su J, et al. Hsa_Circ_0001275: a potential Novel Diagnostic Biomarker for Postmenopausal osteoporosis. Cell Physiol Biochem. 2018;46(6):2508–16.

    Article  CAS  PubMed  Google Scholar 

  5. Yu L, Liu Y. circRNA_0016624 could sponge miR-98 to regulate BMP2 expression in postmenopausal osteoporosis. Biochem Biophys Res Commun. 2019;516(2):546–50.

    Article  CAS  PubMed  Google Scholar 

  6. Chen X, Ouyang Z, Shen Y, Liu B, Zhang Q, Wan L, et al. CircRNA_28313/miR-195a/CSF1 axis modulates osteoclast differentiation to affect OVX-induced bone absorption in mice. RNA Biol. 2019;16(9):1249–62.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Fei Q, Li X, Lin J, Yu L, Yang Y. Identification of aberrantly expressed long non-coding RNAs and nearby targeted genes in male osteoporosis. Clin Interv Aging. 2020;15:1779–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495(7441):333–8.

    Article  CAS  PubMed  Google Scholar 

  10. Gao Y, Wang J, Zhao F. CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol. 2015;16(1):4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang L, Niu N, Li L, Shao R, Ouyang H, Zou W. H3K36 trimethylation mediated by SETD2 regulates the fate of bone marrow mesenchymal stem cells. PLoS Biol. 2018;16(11):e2006522.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Li X, Yang L, Chen LL. The Biogenesis, functions, and challenges of Circular RNAs. Mol Cell. 2018;71(3):428–42.

    Article  CAS  PubMed  Google Scholar 

  13. Nakashima T. Coupling and communication between bone cells. Clin Calcium. 2014;24(6):853–61.

    CAS  PubMed  Google Scholar 

  14. Li J, Duns G, Westers H, Sijmons R, van den Berg A, Kok K. SETD2: an epigenetic modifier with tumor suppressor functionality. Oncotarget. 2016;7(31):50719–34.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Jia X, Long Q, Miron RJ, Yin C, Wei Y, Zhang Y, Wu M. Setd2 is associated with strontium-induced bone regeneration. Acta Biomater. 2017;53:495–505.

    Article  CAS  PubMed  Google Scholar 

  16. Rasheed N, Wang X, Niu QT, Yeh J, Li B. Atm-deficient mice: an osteoporosis model with defective osteoblast differentiation and increased osteoclastogenesis. Hum Mol Genet. 2006;15(12):1938–48.

    Article  CAS  PubMed  Google Scholar 

  17. Ito K, Hirao A, Arai F, Matsuoka S, Takubo K, Hamaguchi I, et al. Regulation of oxidative stress by ATM is required for self-renewal of haematopoietic stem cells. Nature. 2004;431(7011):997–1002.

    Article  CAS  PubMed  Google Scholar 

  18. Mensah KA, Chen JW, Schickel JN, Isnardi I, Yamakawa N, Vega-Loza A, et al. Impaired ATM activation in B cells is associated with bone resorption in rheumatoid arthritis. Sci Transl Med. 2019;11:519.

    Article  Google Scholar 

  19. Xu D, Grishin NV, Chook YM. NESdb: a database of NES-containing CRM1 cargoes. Mol Biol Cell. 2012;23(18):3673–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Turner JG, Dawson J, Cubitt CL, Baz R, Sullivan DM. Inhibition of CRM1-dependent nuclear export sensitizes malignant cells to cytotoxic and targeted agents. Semin Cancer Biol. 2014;27:62–73.

    Article  CAS  PubMed  Google Scholar 

  21. Tai YT, Landesman Y, Acharya C, Calle Y, Zhong MY, Cea M, et al. CRM1 inhibition induces tumor cell cytotoxicity and impairs osteoclastogenesis in multiple myeloma: molecular mechanisms and therapeutic implications. Leukemia. 2014;28(1):155–65.

    Article  CAS  PubMed  Google Scholar 

  22. Svensson MND, Erlandsson MC, Jonsson I-M, Andersson KME, Bokarewa MI. Impaired signaling through the < i > fms-like tyrosine kinase 3 receptor increases osteoclast formation and bone damage in arthritis. J Leukoc Biol. 2016;99(3):413–23.

    Article  CAS  PubMed  Google Scholar 

  23. Lean JM, Fuller K, Chambers TJ. FLT3 ligand can substitute for macrophage colony-stimulating factor in support of osteoclast differentiation and function. Blood. 2001;98(9):2707–13.

    Article  CAS  PubMed  Google Scholar 

  24. Speziani C, Rivollier A, Gallois A, Coury F, Mazzorana M, Azocar O, et al. Murine dendritic cell transdifferentiation into osteoclasts is differentially regulated by innate and adaptive cytokines. Eur J Immunol. 2007;37(3):747–57.

    Article  CAS  PubMed  Google Scholar 

  25. Flores RJ, Kelly AJ, Li Y, Nakka M, Barkauskas DA, Krailo M, et al. A Novel Prognostic Model for Osteosarcoma using circulating CXCL10 and FLT3LG. Cancer. 2017;123(1):144–54.

    Article  CAS  PubMed  Google Scholar 

  26. Maruyama K, Uematsu S, Kondo T, Takeuchi O, Martino MM, Kawasaki T, Akira S. Strawberry notch homologue 2 regulates osteoclast fusion by enhancing the expression of DC-STAMP. J Exp Med. 2013;210(10):1947–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Li T, Liu WB, Tian FF, Jiang JJ, Wang Q, Hu FQ, et al. Gender-specific SBNO2 and VPS13B as a potential driver of osteoporosis development in male ankylosing spondylitis. Osteoporos Int. 2021;32(2):311–20.

    Article  CAS  PubMed  Google Scholar 

  28. Son A, Kang N, Oh SY, Kim KW, Muallem S, Yang YM, Shin DM. Homer2 and Homer3 modulate RANKL-induced NFATc1 signaling in osteoclastogenesis and bone metabolism. J Endocrinol. 2019;242(3):241–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Douroudis K, Tarassi K, Athanassiades T, Giannakopoulos F, Kominakis A, Thalassinos N, Papasteriades C. HLA alleles as predisposal factors for postmenopausal osteoporosis in a Greek population. Tissue Antigens. 2007;69(6):592–6.

    Article  CAS  PubMed  Google Scholar 

  30. Tsuji S, Munkhbat B, Hagihara M, Tsuritani I, Abe H, Tsuji K. HLA-A*24-B*07-DRB1*01 haplotype implicated with genetic disposition of peak bone mass in healthy young Japanese women. Hum Immunol. 1998;59(4):243–9.

    Article  CAS  PubMed  Google Scholar 

  31. Li SM, Zhou DX, Liu MY. Associations between polymorphisms of HLA-B gene and postmenopausal osteoporosis in Chinese Han population. Int J Immunogenet. 2014;41(4):324–9.

    Article  PubMed  Google Scholar 

  32. Li H, Li T, Wang S, Wei J, Fan J, Li J, et al. Mir-17-5p and miR-106a are involved in the balance between osteogenic and adipogenic differentiation of adipose-derived mesenchymal stem cells. Stem Cell Res. 2013;10(3):313–24.

    Article  CAS  PubMed  Google Scholar 

  33. Wang R, Lu A, Liu W, Yue J, Sun Q, Chen J, et al. Searching for valuable differentially expressed miRNAs in postmenopausal osteoporosis by RNA sequencing. J Obstet Gynaecol Res. 2020;46(7):1183–92.

    Article  CAS  PubMed  Google Scholar 

  34. Liu K, Jing Y, Zhang W, Fu X, Zhao H, Zhou X, et al. Silencing miR-106b accelerates osteogenesis of mesenchymal stem cells and rescues against glucocorticoid-induced osteoporosis by targeting BMP2. Bone. 2017;97:130–8.

    Article  CAS  PubMed  Google Scholar 

  35. Seeliger C, Karpinski K, Haug AT, Vester H, Schmitt A, Bauer JS, van Griensven M. Five freely circulating miRNAs and bone tissue miRNAs are associated with osteoporotic fractures. J Bone Min Res. 2014;29(8):1718–28.

    Article  CAS  Google Scholar 

  36. Kelch S, Balmayor ER, Seeliger C, Vester H, Kirschke JS, van Griensven M. miRNAs in bone tissue correlate to bone mineral density and circulating miRNAs are gender independent in osteoporotic patients. Sci Rep. 2017;7(1):15861.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Qiao L, Li CG, Liu D. CircRNA_0048211 protects postmenopausal osteoporosis through targeting miRNA-93-5p to regulate BMP2. Eur Rev Med Pharmacol Sci. 2020;24(7):3459–66.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by Beijing Natural Science Foundation Funded Project (NO: 7222033).

Author information

Author notes

  1. Jiayi Li and Sijia Guo contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopedics, Beijing Friendship Hospital, Capital Medical University, Xicheng District, Beijing, 100050, People’s Republic of China

    Jiayi Li, Sijia Guo, Qingyun Sun, Ning An, Jisheng Lin & Qi Fei

Authors
  1. Jiayi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sijia Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Qingyun Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ning An
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jisheng Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Qi Fei
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Each author made substantial contributions to this work. JYL, GSJ, NA, JSL, and QF contributed to the conception and design of the work. JYL, GSJ, NA, JSL contributed to the acquisition of study data. JYL, GSJ, NA, JSL contributed to the analysis and interpretation of data. All authors have drafted the work or substantively revised it, and all authors read and approved the final manuscript.

Corresponding author

Correspondence to Qi Fei.

Ethics declarations

Ethics approval and consent to participate

This cross sectional study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University, and all subjects provided signed informed consent. All participants provided informed consent prior to commencing study involvement.

Consent for publication

Not applicable.

Guidelines

All methods of our study were performed in accordance with the relevant regulations in the methods section.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

12891_2024_8171_MOESM1_ESM.tif

Supplementary Material 1: Figure S1 Significantly enriched GO terms and KEGG pathways of host genes of DEcircRNAs in male osteoporosis patients. A) BP, biological process; B) CC, cellular component; C) MF, molecular function; D) KEGG pathways.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Guo, S., Sun, Q. et al. Bioinformatics screening and clinical validation of CircRNA and related miRNA in male osteoporosis. BMC Musculoskelet Disord 26, 117 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-024-08171-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-024-08171-w

Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

Contact us