Conditioned medium of PC‑3 prostate cancer cells affects microRNA and mRNA profiles in mechanically strained osteoblasts
- Authors:
- Published online on: February 13, 2023 https://doi.org/10.3892/etm.2023.11837
- Article Number: 138
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Copyright: © Cao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Bone is associated with electrolyte balance, energy metabolism and mechanical competence; it performs these functions through a continuous homeostatic balance between modeling and remodeling processes that is carried out mainly by osteoblasts (OBs) and osteoclasts (OCs), which act via bone formation and resorption, respectively (1). Prostate cancer is a common malignant tumor that can be fatal for male patients with advanced tumors, and it is also one of the malignant tumors most associated with bone metastasis (2). The common metastasis of prostate cancer cells to bone can disrupt bone remodeling, and results in lesions that cause significant pain and patient morbidity (3,4). Therefore, it is of great significance to study the mechanism underlying pathological bone formation and osteogenic differentiation caused by prostate cancer bone metastases. However, effective therapies for bone metastatic disease are underdeveloped, and the majority of therapies are palliative with only a modest survival benefit (3,5). The exact mechanisms underlying the metastasis of prostate cancer to the bone are unclear. However, evidence has suggested that the bone marrow microenvironment, particularly its related microRNAs (miRNAs/miRs) and pathways, is a significant factor of prostate cancer bone metastasis (6,7).
Bone metastasis of prostate cancer leads to abnormal osteoblastic differentiation, and causes fatal osteolytic and osteoblastic abrasions, which results in a poor quality of life in individuals with prostate cancer (8,9). The development of either osteolytic or osteoblastic lesions caused by prostate cancer bone metastasis results from functional interactions between tumor cells and osteoclasts or osteoblasts, respectively (10). Therefore, it is important to investigate the effect of prostate cancer cells on osteoblasts or osteoclasts in vitro or in vivo.
The growth of bone requires an appropriate mechanical environment, osteoblasts, important functional cells in bone, are usually stimulated by a mechanically loaded, and there is a dynamic balance between bone resorption and bone formation (11). Physiological bone formation and osteogenic differentiation is induced by osteoblasts under physiological mechanical loads. The physiological strain of human weight-bearing bone is generally <2,000 µε (12). In Frost's bone function adaptability model, 50-2,500 µε is considered strain in the physiological range (13,14). Previous studies of osteoblasts cultured in vitro revealed that mechanical tensile strain of 2,500 µε at 0.5 Hz could promote osteogenic differentiation and improve osteogenic function (15-17), and that mechanical tensile strain of 2,500 µε at 0.5 Hz could be regarded as normal physiological load (17). Under normal physiological load, the abnormal osteogenic differentiation caused by bone metastasis of prostate cancer is regarded as pathological osteogenic differentiation. Therefore, in the present study, in vitro osteoblasts were stimulated with physiological mechanical strain and were simultaneously treated with the condition medium (CM) of prostate cancer cells; this is considered a good cell model of bone metastasis from prostate cancer.
In the present study, the MC3T3-E1 osteoblastic cells were treated with the CM of prostate cancer cells and physiological mechanical tensile strain (2,500 µε at 0.5 Hz) was simultaneously applied to the cells using a four-point bending device. Subsequently, the osteogenic differentiation of cells under the conditions of pathology (CM and physiological mechanical strain) and physiology (fresh medium and physiological mechanical strain) were detected. The differentially expressed mRNAs and miRNAs in the pathology group were detected and verified by high-throughput RNA sequencing and reverse transcription-quantitative PCR (RT-qPCR). Finally, bioinformatics technology was used to screen and predict the signal molecules and signaling pathways related to bone metastasis.
Materials and methods
Preparation of CM
PC-3 prostate cancer cells (American Type Culture Collection) were cultured in Dulbecco's modified Eagle's medium (DMEM; Gibco; Thermo Fisher Scientific, Inc.) containing 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.) in an incubator containing 5% CO2 at 37˚C. PC-3 cells (1x106 ) were grown in a 25-cm2 culture flask in cell culture medium until 60-70% confluent. After two washes with phosphate-buffered saline (PBS), cells were incubated in DMEM containing 1% FBS for 48 h, and the crude CM (CCM) was then collected, filtered through a 0.2-µm sterilizing filter and stored at -80˚C. The PC-3 CM contained 20% PC-3 CCM, and 80% DMEM containing 10% FBS (FBS; Gibco; Thermo Fisher Scientific, Inc.) culture medium.
PC-3 CM treatment and mechanical stimulus
The MC3T3-E1 mouse pre-osteoblastic cell line (provided by the Institute of Basic Medicine of Peking Union Medical College, Beijing, China), at the third passage, was seeded into mechanical loading dishes, which were reformed from cell culture dishes (Nunc; Thermo Fisher Scientific, Inc.) in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin (Gibco; Thermo Fisher Scientific, Inc.), mechanical loading dishes were reformed from cell culture dishes (Nunc; Thermo Fisher Scientific, Inc.) according to the previously described method (17).
At 100% confluence, the medium was replaced with the PC-3 CM in the pathological group and with DMEM in the physiological group. All of the MC3T3-E1 cells were subjected to a mechanical tensile strain of 2,500 µε at 0.5 Hz, 1 h/day for 3 days. The mechanical strain was generated using a specially designed four-point bending device (provided by The Institute of Medical Equipment, Academy of Military Medical Sciences, Tianjin, China), as previously described (17).
Alkaline phosphatase (ALP) activity assay
After treatment with CM and mechanical stimulation, the MC3T3-E1 cells were lysed by brief sonication at 20 kHz for 1 min on ice in radioimmunoprecipitation (RIPA) lysis buffer (CW Biosciences). The protein concentration of the cell lysates was measured using the Bicinchoninic Acid (BCA) Protein Assay kit (CW Biosciences). ALP activity in the lysates was subsequently measured with an alkaline phosphatase assay kit (cat. no. A059-2-1; Nanjing Jiancheng Bioengineering Institute) using the p-nitrophenyl phosphate method, according to the manufacturer's instructions.
ALP and alizarin red-S staining
Following treatment, the cells were washed with PBS and fixed with 4% paraformaldehyde for 20 min at 37˚C. The fixed cells were incubated with the ALP staining kit (cat. no. D001-2-1; Nanjing Jiancheng Bioengineering Institute) and Alizarin red-S staining kit (cat. no. C0148; Beyotime Institute of Biotechnology) to reveal the calcium deposition of cells, according to the manufacturer's instructions. Cells were then observed under an optical microscope.
Western blotting
Following treatment, the protein extracts of the cells were harvested in RIPA lysis buffer (Beijing Solarbio Science & Technology Co., Ltd.) and protein concentration in the cell lysates was quantified using the BCA Protein Assay kit. Proteins (15 µg) were then separated by SDS-PAGE on 10% gels and transferred to nitrocellulose membranes, which were blocked with 2.5% BSA (Beijing Solarbio Science & Technology Co., Ltd.) in Tris-buffered saline with 0.1% Tween-20 (Beijing Solarbio Science & Technology Co., Ltd.) at room temperature for 2 h. Membranes were then incubated with the following primary antibodies at 4˚C for 15 h: Anti-RUNX-2 (1:200; cat. no. sc-390351) and anti-β-actin (1:1,000; cat. no. sc-8432) (both from Santa Cruz Biotechnology, Inc.). The membranes were then incubated with horseradish peroxidase-conjugated secondary antibodies (1:2,000; cat. no. sc-2005; Santa Cruz Biotechnology, Inc.) at 37˚C for 1 h. Immunoreactive bands were detected using an enhanced chemiluminescence detection reagent (Pierce; Thermo Fisher Scientific, Inc.). β-actin in the cell lysates was used as a loading control and data were normalized against the corresponding optical density of β-actin. ImageJ 1.48 software (National Institutes of Health) was used for semi-quantification of bands.
Preparation of total RNA and cDNA for small RNA sequencing
Total RNA was isolated from cells using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. RNA purity was assessed using the ND-1000 Nanodrop (NanoDrop; Thermo Fisher Scientific, Inc.). Each RNA sample had an A260:A280 ratio >1.8 and an A260:A230 ratio >2.0. RNA integrity was evaluated using the Agilent 2200 TapeStation (Agilent Technologies, Inc.) and each sample had the RNA integrity number >7.0. Briefly, RNA molecules were ligated with a 3' RNA adapter, followed by 5' adapter ligation, using a Truseq™ Small RNA sample prep Kit (cat. no. RS-200-0012; Illumina, Inc.). Subsequently, the adapter-ligated RNAs were reversed transcribed to cDNA and PCR amplified. cDNA was synthesized using an AmpliSeq cDNA Synthesis for Illumina kit (cat. no. 20022654; Illumina, Inc.), the thermocycling conditions used for reverse transcription were 12˚C for 15 min and 95˚C for 3 min. PCR amplification was performed using the Illumina® DNA PCR-Free Sequencing and Indexing Primer kit (cat. no. 20041797; Illumina, Inc.), the thermocycling conditions for PCR amplification were 94˚C for 180 sec, followed by 20 cycles of 98˚C for 20 sec, annealing at 56˚C for 15 sec and extension at 72˚C for 15 sec. Subsequently, the size of PCR products was selected by PAGE gel according to the instructions of the NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (cat. no. E7330S; New England Biolabs, Inc.). The purified library products were evaluated using the Agilent 2200 TapeStation and the Qubit® 2.0 Fluorometer (Invitrogen; Thermo Fisher Scientific, Inc.), then diluted to 2 pM for sequencing. The type of sequencing performed was single-end from 3' ends and 50 bp was sequenced each time using HiSeq X Reagent Kits (cat. no. FC-501-2501; Illumina, Inc.).
Differential expression analysis was conducted using miRDeep2 (v2.0.0) software (18). A miRNA with ‘P-adjust’ <0.05 and |log2FC|≥1.0 compared with the physiological group was considered to be differentially expressed.
Preparation of total RNA and cDNA for transcriptome sequencing
Briefly, the total RNA quality was assessed as in the previous subsection, rRNAs were removed from total RNA using Epicentre Ribo-Zero rRNA Removal Kit (Illumina, Inc.) and fragmented to ~200 bp. Subsequently, the purified RNAs were subjected to first strand and second strand cDNA synthesis according to the aforementioned method, followed by adaptor ligation and enrichment with a low-cycle according to instructions of the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina (cat. no. E7420L; New England Biolabs, Inc.). The purified library products were evaluated using the Agilent 2200 TapeStation and Qubit 2.0 Fluorometer, and the final library was diluted to 10 pM for sequencing. The type of sequencing was paired-end from 3' ends and 150 bp was sequenced each time using HiSeq X Reagent Kits.
The differential expression analysis was conducted using DEGseq 1.38.0 software (19). Genes with ‘P-adjust’<0.05 and |log2FC|≥1.0 compared with the physiological group were considered differentially expressed genes.
RT-qPCR for mRNA and miRNA
RT-qPCR was performed to further validate selected differentially expressed mRNAs and miRNAs identified from small RNA sequencing and transcriptome sequencing.
Total RNA was isolated from cells using TRIzol reagent, according to the manufacturer's protocol. After total RNA was extracted from the cells, cDNA was synthesized using a Quant Script RT kit (Tiangen Biotech Co., Ltd.); the thermocycling conditions for RT were 12˚C for 15 min and 95˚C for 3 min. qPCR was performed to detect the mRNA expression levels using SYBR Green I PCR mix (CW Biosciences) on a Real-Time PCR system (7900; Applied Biosystems; Thermo Fisher Scientific, Inc.), according to the manufacturer's instructions. The amplification reaction included a denaturation step at 94˚C for 180 sec, followed by 40 cycles of 94˚C for 15 sec, annealing and extension at 60˚C for 30 sec. Each sample was analyzed in triplicate. The expression levels of mRNA were normalized to the internal control β-actin using the 2-ΔΔCq method (20). The mRNA primer sequences are shown in Table I.
The miRNA expression levels were assessed using RT-qPCR by Guangzhou Ribobio Co., Ltd. The miRNA Uni-Reverse Primers and miRNA Primers (specific primers) for RT-qPCR of miRNA were provided by Guangzhou Ribobio Co., Ltd. Poly(A) tailing. Total RNA was isolated from cells using TRIzol reagent, according to the manufacturer's protocol. After total RNA was extracted from the cells cDNA was synthesized by reverse transcription according to the aforementioned method and qPCR was successively performed using the miDETECT A Track miRNA qRT-PCR Starter Kit (Guangzhou Ribobio Co., Ltd.). The reactions were incubated in a 96-well optical plate at 95˚C for 20 sec, followed by 40 cycles of 10 sec at 95˚C, 20 sec at 60˚C and 10 sec at 70˚C. The miRNA expression levels were normalized to U6 snRNA. The miRNA primer sequences are shown in Table II.
Competing endogenous RNA (CeRNA) network
According to the results of the differential expression analysis, the regulatory relationships between long non-coding RNA (lncRNA) and miRNA were predicted using miRanda (http://cbio.mskcc.org/miRNA2003/miranda.html), pictar (https://pictar.mdc-berlin.de/) and RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/). The prediction results of these three types of software were used as the candidate lncRNA, which acts as a sponge of miRNAs, the expression of lncRNAs reduces the expression of corresponding miRNAs. Therefore, the present study selected the lncRNA-miRNA pairs where the expression patterns between lncRNAs and miRNAs were different. In addition, significant differentially expressed miRNAs and mRNAs were extracted to construct the miRNA-mRNA regulatory network. Among the differentially expressed mRNAs, the potential targets of the differentially expressed miRNAs were predicted by TargetScan database (http://www.targetscan.org), miRDB (http://mirdb.org/) and miRWalk (http://mirwalk.umm.uni-heidelberg.de/). The prediction results of these three types of software were considered the candidate mRNAs of miRNA. The miRNA-mRNA pairs with an inverse expression relationship were included for network construction. The lncRNA-miRNA-mRNA regulatory network was constructed using a combination of lncRNA-miRNA pairs and miRNA-mRNA pairs. Finally, the regulatory network was visualized using Cytoscape 3.4.0 (https://cytoscape.org/).
Bioinformatics analysis
Since miRNAs serve their biological roles through regulating the expression of target genes at the post-transcriptional level, target genes of the miRNAs were predicted using online software, including miRDB (http://www.mirdb.org/), RNA22 (http://cm.jefferson.edu/rna22v2/), TargetScan (http://www.targetscan.org), and miRanda (http://www.microrna.org/). To identify the biological functions of these genes, Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/) enrichment analysis were performed. Based on the KEGG database (https://www.genome.jp/), gene sets enriched in the biological pathway associated with osteoblastic differentiation were selected.
Statistical analysis
Experiments were performed in triplicate as a minimum, and data from RT-qPCR, the ALP activity assay and western blotting are presented as the mean ± standard deviation. To identify differentially expressed miRNAs and mRNAs among the groups and to compare differences between groups, unpaired Student's t-test was performed using SPSS 18.0 (IBM Corp.). P<0.05 was considered to indicate a statistically significant difference.
Results
PC-3 CM inhibits osteoblastic differentiation of MC3T3-E1 cells. All of the MC3T3-E1 cells were stimulated with a mechanical tensile strain of 2,500 µε at 0.5 Hz. Treatment with the PC-3 CM decreased the ALP activity of cells, as well as the mRNA expression levels of collagen, type I, alpha 1 (Col1a1) and osteocalcin (OCN) (Fig. 1A and B). Western blotting indicated that treatment with the PC-3 CM decreased the protein expression levels of RUNX-2 in the MC3T3-E1 cells (Fig. 1C). In addition, the PC-3 CM reduced ALP staining and calcium deposition (Fig. 1D).
PC-3 CM alters the miRNA and mRNA profiles
As shown in Fig. 2, the small RNA sequencing indicated 51 differentially expressed miRNAs (15 upregulated, 36 downregulated). Based on the results of miRNA sequencing, 23 miRNAs were selected from the 51 differentially expressed miRNAs (the top 24 miRNAs demonstrated highly significant differential expression however, one of these had no specific reference sequence). Furthermore, RT-qPCR was used to verify the 23 differentially expressed miRNAs between the physiological and pathological groups. The results indicated that seven miRNAs were significantly upregulated and 12 were significantly downregulated, which is consistent with the results of sequencing (Fig. 3).
As shown in Fig. 4, the mRNA sequencing indicated 153 differentially expressed mRNAs (77 upregulated, 76 downregulated). Furthermore, the RNA22 (selection criteria: miR22 score >97.0), TargetScan (selection criteria: Target Scan score >99.0) and miRDB (selection criteria: miRDB score >95.0) databases were used to explore the potential miRNA-mRNA interactions of the differentially expressed miRNAs and mRNAs. Potential miRNA-mRNA interactions of miRNAs were assessed using the three, RNA22, TargetScan and miRDB databases. By combining the results from the three databases and 153 differentially expressed genes from mRNA sequencing, 28 genes were selected for further verification using RT-qPCR. The trend observed in mRNA sequencing for 10 upregulated and 11 downregulated mRNAs was consistent with the RT-qPCR results (Fig. 5).
PC-3 CM alters the lncRNA profile and construction of the ceRNA network
As shown in Fig. 6, RNA sequencing indicated 133 differentially expressed lncRNAs (81 upregulated, 52 downregulated). Furthermore, a ceRNA regulatory network was constructed based on the differentially expressed mRNAs-miRNAs-lncRNAs.
As shown in Fig. 7, 20 differentially expressed miRNAs were selected as core nodes, 6 differentially expressed mRNAs were selected as candidate target genes based on miRDB, TargetScan and miRWalk, and 28 differentially expressed lncRNAs were selected as miRNA sponges according to miRanda, pictar and RNAhybrid. The results suggested that LncRNAs act as ceRNAs to regulate mRNAs by sponging their shared miRNAs.
Prediction of target genes of the differentially expressed miRNAs and signaling pathways involved in osteoblastic differentiation caused by CM
The KEGG enrichment analyses predicted certain signaling pathways were associated with osteoblastic differentiation, including ‘Wnt signaling pathway’, ‘MAPK signaling pathway’, ‘Calcium signaling pathway’, ‘Osteoclast differentiation’, ‘Pathways in cancer’, ‘TNF signaling pathway’, ‘HIF-1 signaling pathway’, ‘MicroRNAs in cancer’ and ‘Rheumatoid arthritis’ (Table III). These signaling pathways may be associated with ‘pathological’ osteoblastic differentiation (21-28) induced by the CM of PC-3 prostate cancer cells.
 | Table IIIEnrichment analyses of KEGG pathway identified certain signaling pathways related to osteoblastic differentiation. |
Based on the RT-qPCR results of miRNAs and mRNAs, and KEGG analysis, the complete targeting regulatory relationships of mRNAs, miRNAs and signaling pathways were listed. Seven differently expressed mRNAs, and associated miRNAs and signaling pathways were selected (Table IV), which reflect a large range of regulation in the network diagram.
| Table IVTargeting regulatory relationships among mRNAs, miRNAs and signaling pathways based on the results of reverse transcription-quantitative PCR and Kyoto Encyclopedia of Genes and Genomes analysis. |
Discussion
Bone is responsive to dynamic mechanical loading; a suitable dynamic mechanical loading maintains the dynamic balance of bone formation (29,30). Osteoblasts, which are located on the surface of bone tissue, are bone-forming cells, which are usually stimulated by dynamic mechanical strain in vivo (31). Using a four-point-bend loading device, MC3T3-E1 osteoblastic cells were stimulated with a mechanical tensile strain of 2,500 µε at 0.5 Hz and were simultaneously treated with PC3 CM, after which the effects of the CM on MC3T3-E1 cells were studied. Treatment with the PC-3 CM inhibited osteoblast differentiation of MC3T3-E1 cells as indicated by the down-regulated expression markers of osteogenic differentiation, and the differentially expressed miRNAs and mRNAs of MC3T3-E1 cells treated with PC-3CM were potentially involved in bone metabolism during prostate cancer progression and bone metastasis.
In the present study, ALP activity, the mRNA expression levels of Col I and OCN, calcium deposition and the protein expression levels of Runx-2 in MC3T3-E1 cells were all decreased in the pathology group, which was treated with the PC-3 CM. These results indicated that the PC-3 CM inhibited the osteogenic differentiation of MC3T3-E1 cells in vitro, suggesting that the secretions of PC-3 cells impeded osteogenic differentiation. Subsequently, next generation sequencing was used to analyze the miRNA and mRNA expression profiles of the pathological and physiological groups. The results showed that, compared with in the physiological group, there were 51 differentially expressed miRNAs and 153 differentially expressed mRNAs in the pathological group.
Furthermore, bioinformatics analysis identified 23 miRNAs and 28 mRNAs from the differential expression profiles and verified them with RT-qPCR. Compared with in the physiological group, there were seven upregulated and 12 downregulated miRNAs, and 10 upregulated and 11 downregulated mRNAs, which were consistent with the results of next generation sequencing. The predicted target mRNAs of the differentially expressed miRNAs implied potential regulatory mechanisms of the miRNA targets in osteoblastic differentiation resulting from the treatment of PC-3 CM.
Moreover, the differentially expressed mRNAs and miRNA-predicted ‘target’ genes were matched to strengthen the significance of the selected genes and miRNAs, these genes and miRNAs mediated osteoblastic differentiation which was the result of treatment of PC-3 CM. In addition, nine potential pathways, which may be involved in bone metabolism during prostate cancer progression and bone metastasis, were revealed, as follows: ‘Wnt signaling pathway’, ‘MAPK signaling pathway’, ‘Calcium signaling pathway’, ‘Osteoclast differentiation’, ‘Pathways in cancer’, ‘TNF signaling pathway’, ‘HIF-1 signaling pathway’, ‘MicroRNAs in cancer’, ‘Rheumatoid arthritis’. Relevant literature have demonstrated that these nine pathways are all associated with cancer progression or osteoblastic differentiation (21-28,32,33).
At present, evidence has supported the ‘seed and soil’ model by Paget, which proposes a specific and strong interaction between metastatic prostate cancer cells and the bone microenvironment (34). Notably, cancer-derived exosomes serve an important role in microenvironment transformation (35). Cancer cells have been reported to secrete a large number of exosomes, which are rich in miRNAs and mRNAs that can enter the target cells and affect their physiological functions (36). The present study generated a model of prostate cancer bone metastasis under mechanical loading, and the differentially expressed miRNAs and mRNAs were detected in osteoblasts. These differentially expressed miRNAs and mRNAs may be mediated by exosomes secreted by prostate cancer.
Currently, widely used cell lines for prostate cancer research include PC3, DU145 or LNCaP cells (37). hFOB 1.19 and MC3T3-E1 cell lines are widely used for osteogenic differentiation studies (38). Since the MC3T3 cell line was used in a previous study of osteogenic differentiation under a physiologically dynamic environment, the present study also used the MC3T3 cell line (17). The present study did not use multiple prostate cancer cell lines, multiple osteoblast cell lines or cell lines of human origin, which was a limitation. The present study does not fully reflect the bone metastasis of prostate cancer, but it does provide ideas for the study of the mechanism of bone metastasis in prostate cancer.
The present findings suggested that genetic interactions (miRNA-gene-signaling pathway) may be associated with the bone metastasis of prostate cancer. Molecular alterations in gene expression may therefore represent a novel signature in pathological osteoblastic differentiation of prostate cancer, which could be used to develop diagnostic and therapeutic strategies for patients with prostate cancer and pathological osteoblastic differentiation caused by bone metastasis. However, these target genes and miRNAs require further verification in subsequent studies, and the mechanism underlying the involvement of differentially expressed miRNAs and genes in the osteoblasts after bone metastasis of prostate cancer also need further investigation.
Acknowledgements
Not applicable.
Funding
Funding: The work was supported by the National Nature Science Foundation of China (grant nos. 31660261 and 12202113) and the Natural Science Foundation of Guangxi (grant nos. 2018GXNSFAA281357 and 2016GXNSFAA380322).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The sequencing datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211413).
Authors' contributions
YG designed the study, analyzed the data and participated in the bioinformatics analysis. ZC performed all the RT-qPCR analyses at Guangzhou RiboBio Co., Ltd. (experimental equipment and most of the reagents were provided by Guangzhou RiboBio Co., Ltd.). ZXY performed ALP activity and Alizarin red-S staining. JHW performed western blotting. HY, BH and JTG performed the bioinformatics analysis. YG and ZC wrote and revised the manuscript. YG and ZC confirm the authenticity of all the raw data. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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