Disulfidptosis-related Protein RPN1 may be a Novel Anti-osteoporosis Target of Kaempferol


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Background:Osteoporosis (OP) is an age-related skeletal disease. Kaempferol can regulate bone mesenchymal stem cells (BMSCs) osteogenesis to improve OP, but its mechanism related to disulfidptosis, a newly discovered cell death mechanism, remains unclear.

Objective:The study aimed to investigate the biological function and immune mechanism of disulfidptosis- related ribophorin I (RPN1) in OP and to experimentally confirm that RPN1 is the target for the treatment of OP with kaempferol.

Methods:Differential expression analysis was conducted on disulfide-related genes extracted from the GSE56815 and GSE7158 datasets. Four machine learning algorithms identified disease signature genes, with RPN1 identified as a significant risk factor for OP through the nomogram. Validation of RPN1 differential expression in OP patients was performed using the GSE56116 dataset. The impact of RPN1 on immune alterations and biological processes was explored. Predictive ceRNA regulatory networks associated with RPN1 were generated via miRanda, miRDB, and TargetScan databases. Molecular docking estimated the binding model between kaempferol and RPN1. The targeting mechanism of kaempferol on RPN1 was confirmed through pathological HE staining and immunohistochemistry in ovariectomized (OVX) rats.

Results:RPN1 was abnormally overexpressed in the OP cohort, associated with TNF signaling, hematopoietic cell lineage, and NF-kappa B pathway. Immune infiltration analysis showed a positive correlation between RPN1 expression and CD8+ T cells and resting NK cells, while a negative correlation with CD4+ naive T cells, macrophage M1, T cell gamma delta, T cell follicular helper cells, activated mast cells, NK cells, and dendritic cells, was found. Four miRNAs and 17 lncRNAs associated with RPN1 were identified. Kaempferol exhibited high binding affinity (-7.2 kcal/mol) and good stability towards the RPN1. The experimental results verified that kaempferol could improve bone microstructure destruction and reverse the abnormally high expression of RPN1 in the femur of ovariectomized rats.

Conclusion:RPN1 may be a new diagnostic biomarker in patients with OP, and may serve as a new target for kaempferol to improve OP.

作者简介

Chengzhen Pan

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Chi Zhang

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Zonghan Lin

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Zhou Liang

, Yulin Orthopedic Hospital of Integrated Traditional Chinese and Western Medicine

Email: info@benthamscience.net

Yinhang Cui

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Zhihao Shang

, Nanjing University of Chinese Medicine

Email: info@benthamscience.net

Yuanxun Wei

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Feng Chen

, Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine

编辑信件的主要联系方式.
Email: info@benthamscience.net

参考

  1. Iolascon, G.; de SIRE, A.; Curci, C.; Paoletta, M.; Liguori, S.; Calafiore, D.; Gimigliano, F.; Moretti, A. Osteoporosis guidelines from a rehabilitation perspective: systematic analysis and quality appraisal using AGREE II. Eur. J. Phys. Rehabil. Med., 2021, 57(2), 273-279. doi: 10.23736/S1973-9087.21.06581-3 PMID: 33650841
  2. Noh, J.Y.; Yang, Y.; Jung, H. Molecular mechanisms and emerging therapeutics for osteoporosis. Int. J. Mol. Sci., 2020, 21(20), 7623. doi: 10.3390/ijms21207623 PMID: 33076329
  3. Zou, Z.; Liu, W.; Cao, L.; Liu, Y.; He, T.; Peng, S.; Shuai, C. Advances in the occurrence and biotherapy of osteoporosis. Biochem. Soc. Trans., 2020, 48(4), 1623-1636. doi: 10.1042/BST20200005 PMID: 32627832
  4. Reid, I.R. A broader strategy for osteoporosis interventions. Nat. Rev. Endocrinol., 2020, 16(6), 333-339. doi: 10.1038/s41574-020-0339-7 PMID: 32203407
  5. Shen, Y.; Huang, X.; Wu, J.; Lin, X.; Zhou, X.; Zhu, Z.; Pan, X.; Xu, J.; Qiao, J.; Zhang, T.; Ye, L.; Jiang, H.; Ren, Y.; Shan, P.F. The global burden of osteoporosis, low bone mass, and its related fracture in 204 countries and territories, 1990-2019. Front. Endocrinol., 2022, 13, 882241. doi: 10.3389/fendo.2022.882241 PMID: 35669691
  6. Shen, L.; Luo, K.; Deng, X.; Liu, J.; Yuan, W.Q.; Sun, C.G.; Zhang, Z.S.; Wei, C.; Wang, J.X.; Cummings, S.R.; Xia, W.B.; Wang, S.F.; Zhan, S.Y.; Song, C.L. A commentary on ‘Incidence and cost of vertebral fracture in urban China: A five-year population-based cohort study’. Int. J. Surg., 2023, 109(10), 3203-3204. doi: 10.1097/JS9.0000000000000583 PMID: 37418569
  7. Clynes, M.A.; Harvey, N.C.; Curtis, E.M.; Fuggle, N.R.; Dennison, E.M.; Cooper, C. The epidemiology of osteoporosis. Br. Med. Bull., 2020, 133(1), ldaa005. doi: 10.1093/bmb/ldaa005 PMID: 32282039
  8. Yu, F.; Xia, W. The epidemiology of osteoporosis, associated fragility fractures, and management gap in China. Arch. Osteoporos., 2019, 14(1), 32. doi: 10.1007/s11657-018-0549-y PMID: 30848398
  9. Li, N.; Zheng, B.; Liu, M.; Zhou, H.; Zhao, L.; Cai, H.; Huang, J. Cost-effectiveness of antiosteoporosis strategies for postmenopausal women with osteoporosis in China. Menopause, 2019, 26(8), 906-914. doi: 10.1097/GME.0000000000001339 PMID: 30994577
  10. Alam, W.; Khan, H.; Shah, M.A.; Cauli, O.; Saso, L. Kaempferol as a dietary anti-inflammatory agent: current therapeutic standing. Molecules, 2020, 25(18), 4073. doi: 10.3390/molecules25184073 PMID: 32906577
  11. Kashyap, D.; Sharma, A.; Tuli, H.S.; Sak, K.; Punia, S.; Mukherjee, T.K. Kaempferol – A dietary anticancer molecule with multiple mechanisms of action: Recent trends and advancements. J. Funct. Foods, 2017, 30, 203-219. doi: 10.1016/j.jff.2017.01.022 PMID: 32288791
  12. Ren, J.; Lu, Y.; Qian, Y.; Chen, B.; Wu, T.; Ji, G. Recent progress regarding kaempferol for the treatment of various diseases (Review). Exp. Ther. Med., 2019, 18(4), 2759-2776. doi: 10.3892/etm.2019.7886 PMID: 31572524
  13. Huang, A.Y.; Xiong, Z.; Liu, K.; Chang, Y.; Shu, L.; Gao, G.; Zhang, C. Identification of kaempferol as an OSX upregulator by network pharmacology-based analysis of qianggu Capsule for osteoporosis. Front. Pharmacol., 2022, 13, 1011561. doi: 10.3389/fphar.2022.1011561 PMID: 36210811
  14. Gan, L.; Leng, Y.; Min, J.; Luo, X.M.; Wang, F.; Zhao, J. Kaempferol promotes the osteogenesis in rBMSCs via mediation of SOX2/miR-124-3p/PI3K/Akt/mTOR axis. Eur. J. Pharmacol., 2022, 927, 174954. doi: 10.1016/j.ejphar.2022.174954 PMID: 35421359
  15. Mistry, R.K.; Brewer, A.C. Redox-Dependent Regulation of Sulfur Metabolism in Biomolecules: Implications for Cardiovascular Health. Antioxid. Redox Signal., 2019, 30(7), 972-991. doi: 10.1089/ars.2017.7224 PMID: 28661184
  16. Liu, X.; Nie, L.; Zhang, Y.; Yan, Y.; Wang, C.; Colic, M.; Olszewski, K.; Horbath, A.; Chen, X.; Lei, G.; Mao, C.; Wu, S.; Zhuang, L.; Poyurovsky, M.V.; James You, M.; Hart, T.; Billadeau, D.D.; Chen, J.; Gan, B. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat. Cell Biol., 2023, 25(3), 404-414. doi: 10.1038/s41556-023-01091-2 PMID: 36747082
  17. Law, M.E.; Yaaghubi, E.; Ghilardi, A.F.; Davis, B.J.; Ferreira, R.B.; Koh, J.; Chen, S.; DePeter, S.F.; Schilson, C.M.; Chiang, C.W.; Heldermon, C.D.; Nørgaard, P.; Castellano, R.K.; Law, B.K. Inhibitors of ERp44, PDIA1, and AGR2 induce disulfide-mediated oligomerization of Death Receptors 4 and 5 and cancer cell death. Cancer Lett., 2022, 534, 215604. doi: 10.1016/j.canlet.2022.215604 PMID: 35247515
  18. Nakajima, K.; Ono, M.; Radović, U.; Dizdarević, S.; Tomizawa, S.I.; Kuroha, K.; Nagamatsu, G.; Hoshi, I.; Matsunaga, R.; Shirakawa, T.; Kurosawa, T.; Miyazaki, Y.; Seki, M.; Suzuki, Y.; Koseki, H.; Nakamura, M.; Suda, T.; Ohbo, K. Lack of whey acidic protein (WAP) four-disulfide core domain protease inhibitor 2 (WFDC2) causes neonatal death from respiratory failure in mice. Dis. Model. Mech., 2019, 12(11), dmm040139. doi: 10.1242/dmm.040139 PMID: 31562139
  19. Zhong, Z.X.; Li, X.Z.; Liu, J.T.; Qin, N.; Duan, H.Q.; Duan, X.C. Disulfide bond-based sn38 prodrug nanoassemblies with high drug loading and reduction-triggered drug release for pancreatic cancer therapy. Int. J. Nanomedicine, 2023, 18, 1281-1298. doi: 10.2147/IJN.S404848 PMID: 36945256
  20. Toro-Domínguez, D.; Martorell-Marugán, J.; López-Domínguez, R.; García-Moreno, A.; González-Rumayor, V.; Alarcón-Riquelme, M.E.; Carmona-Sáez, P. ImaGEO: integrative gene expression meta-analysis from GEO database. Bioinformatics, 2019, 35(5), 880-882. doi: 10.1093/bioinformatics/bty721 PMID: 30137226
  21. Halyo, V.; Martin, L. Martin L. Perl (1927–2014). Nature, 2014, 516(7531), 330. doi: 10.1038/516330a PMID: 25519123
  22. Jia, L.; Yao, W.; Jiang, Y.; Li, Y.; Wang, Z.; Li, H.; Huang, F.; Li, J.; Chen, T.; Zhang, H. Development of interactive biological web applications with R/Shiny. Brief. Bioinform., 2022, 23(1), bbab415. doi: 10.1093/bib/bbab415 PMID: 34642739
  23. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7), e47. doi: 10.1093/nar/gkv007 PMID: 25605792
  24. Zhang, H.; Meltzer, P.; Davis, S. RCircos: An R package for Circos 2D track plots. BMC Bioinformatics, 2013, 14(1), 244. doi: 10.1186/1471-2105-14-244 PMID: 23937229
  25. Beck, M.W. NeuralNetTools: Visualization and Analysis Tools for Neural Networks. J. Stat. Softw., 2018, 85(11), 1-20. doi: 10.18637/jss.v085.i11 PMID: 30505247
  26. Buckley, S.J.; Harvey, R.J. Lessons learnt from using the machine learning random forest algorithm to predict virulence in streptococcus pyogenes. Front. Cell. Infect. Microbiol., 2021, 11, 809560. doi: 10.3389/fcimb.2021.809560 PMID: 35004362
  27. Naqvi, A.A.T.; Rizvi, S.A.M.; Hassan, M.I. Pan-cancer analysis of Chromobox (CBX) genes for prognostic significance and cancer classification. Biochim. Biophys. Acta Mol. Basis Dis., 2023, 1869(1), 166561. doi: 10.1016/j.bbadis.2022.166561 PMID: 36183965
  28. Satake, H.; Osugi, T.; Shiraishi, A. Impact of machine learning-associated research strategies on the identification of peptide-receptor interactions in the post-omics era. Neuroendocrinology, 2023, 113(2), 251-261. doi: 10.1159/000518572 PMID: 34348315
  29. Altamimi, A.S.; El-Azab, A.S.; Abdelhamid, S.G.; Alamri, M.A.; Bayoumi, A.H.; Alqahtani, S.M.; Alabbas, A.B.; Altharawi, A.I.; Alossaimi, M.A.; Mohamed, M.A. Synthesis, anticancer screening of some novel trimethoxy quinazolines and vegfr2, egfr tyrosine kinase inhibitors assay; molecular docking studies. Molecules, 2021, 26(10), 2992. doi: 10.3390/molecules26102992 PMID: 34069962
  30. Mahdy, N.E.; Abdel-Baki, P.M.; El-Rashedy, A.A.; Ibrahim, R.M. Modulatory effect of pyrus pyrifolia fruit and its phenolics on key enzymes against metabolic syndrome: bioassay-guided approach, hplc analysis, and in silico study. Plant Foods Hum. Nutr., 2023, 78(2), 383-389. doi: 10.1007/s11130-023-01069-3 PMID: 37219720
  31. Pereira, S.V.; Colombo, F.B.; de Freitas, L.A.P. Ultrasound influence on the solubility of solid dispersions prepared for a poorly soluble drug. Ultrason. Sonochem., 2016, 29, 461-469. doi: 10.1016/j.ultsonch.2015.10.022 PMID: 26548840
  32. Shi, Y.; Chen, X.; Elsasser, S.; Stocks, B.B.; Tian, G.; Lee, B.H.; Shi, Y.; Zhang, N.; de Poot, S.A.H.; Tuebing, F.; Sun, S.; Vannoy, J.; Tarasov, S.G.; Engen, J.R.; Finley, D.; Walters, K.J. Rpn1 provides adjacent receptor sites for substrate binding and deubiquitination by the proteasome. Science, 2016, 351(6275), aad9421. doi: 10.1126/science.aad9421 PMID: 26912900
  33. Cunchillos, C.; Lecointre, G. Early steps of metabolism evolution inferred by cladistic analysis of amino acid catabolic pathways. C. R. Biol., 2002, 325(2), 119-129. doi: 10.1016/S1631-0691(02)01411-7 PMID: 11980173
  34. Ning, K.; Liu, S.; Yang, B.; Wang, R.; Man, G.; Wang, D.; Xu, H. Update on the effects of energy metabolism in bone marrow mesenchymal stem cells differentiation. Mol. Metab., 2022, 58, 101450. doi: 10.1016/j.molmet.2022.101450 PMID: 35121170
  35. Wilson, C.M.; High, S. Ribophorin I acts as a substrate-specific facilitator of N-glycosylation. J. Cell Sci., 2007, 120(4), 648-657. doi: 10.1242/jcs.000729 PMID: 17264154
  36. Ding, J.; Xu, J.; Deng, Q.; Ma, W.; Zhang, R.; He, X.; Liu, S.; Zhang, L. Knockdown of oligosaccharyltransferase subunit ribophorin 1 induces endoplasmic-reticulum-stress-dependent cell apoptosis in breast cancer. Front. Oncol., 2021, 11, 722624. doi: 10.3389/fonc.2021.722624 PMID: 34778038
  37. Cheray, M.; Bessette, B.; Lacroix, A.; Mélin, C.; Jawhari, S.; Pinet, S.; Deluche, E.; Clavère, P.; Durand, K.; Sanchez-Prieto, R.; Jauberteau, M.O.; Battu, S.; Lalloué, F. KLRC 3, a Natural Killer receptor gene, is a key factor involved in glioblastoma tumourigenesis and aggressiveness. J. Cell. Mol. Med., 2017, 21(2), 244-253. doi: 10.1111/jcmm.12960 PMID: 27641066
  38. Gokturk, B.; Keles, S.; Kirac, M.; Artac, H.; Tokgoz, H.; Guner, S.N.; Caliskan, U.; Caliskaner, Z.; van der Burg, M.; van Dongen, J.; Morgan, N.V.; Reisli, I. CD3G gene defects in familial autoimmune thyroiditis. Scand. J. Immunol., 2014, 80(5), 354-361. doi: 10.1111/sji.12200 PMID: 24910257
  39. Okamoto, K.; Takayanagi, H. Effect of T cells on bone. Bone, 2023, 168, 116675. doi: 10.1016/j.bone.2023.116675 PMID: 36638904
  40. Soysa, N.S.; Alles, N. The role of IL‐3 in bone. J. Cell. Biochem., 2019, 120(5), 6851-6859. doi: 10.1002/jcb.27956 PMID: 30320936
  41. Griffith, J.F.; Yeung, D.K.W.; Ahuja, A.T.; Choy, C.W.Y.; Mei, W.Y.; Lam, S.S.L.; Lam, T.P.; Chen, Z.Y.; Leung, P.C. A study of bone marrow and subcutaneous fatty acid composition in subjects of varying bone mineral density. Bone, 2009, 44(6), 1092-1096. doi: 10.1016/j.bone.2009.02.022 PMID: 19268721
  42. Lundberg, P.; Boström, I.; Mukohyama, H.; Bjurholm, A.; Smans, K.; Lerner, U.H. Neuro-hormonal control of bone metabolism. Regul. Pept., 1999, 85(1), 47-58. doi: 10.1016/S0167-0115(99)00069-5 PMID: 10588449
  43. Zhang, W.; Gao, R.; Rong, X.; Zhu, S.; Cui, Y.; Liu, H.; Li, M. Immunoporosis: Role of immune system in the pathophysiology of different types of osteoporosis. Front. Endocrinol., 2022, 13, 965258. doi: 10.3389/fendo.2022.965258 PMID: 36147571
  44. Yao, Z.; Getting, S.J.; Locke, I.C. Regulation of TNF-Induced osteoclast differentiation. Cells, 2021, 11(1), 132. doi: 10.3390/cells11010132 PMID: 35011694
  45. Zha, L.; He, L.; Liang, Y.; Qin, H.; Yu, B.; Chang, L.; Xue, L. TNF-α contributes to postmenopausal osteoporosis by synergistically promoting RANKL-induced osteoclast formation. Biomed. Pharmacother., 2018, 102, 369-374. doi: 10.1016/j.biopha.2018.03.080 PMID: 29571022
  46. Rachner, T.D.; Link-Rachner, C.S.; Bornhäuser, M.; Hofbauer, L.C. Skeletal health in patients following allogeneic hematopoietic cell transplantation. Bone, 2022, 158, 115684. doi: 10.1016/j.bone.2020.115684 PMID: 33049368
  47. Capece, D.; Verzella, D.; Flati, I.; Arboretto, P.; Cornice, J.; Franzoso, G. NF-κB: blending metabolism, immunity, and inflammation. Trends Immunol., 2022, 43(9), 757-775. doi: 10.1016/j.it.2022.07.004 PMID: 35965153
  48. Mishra, R. Sehring, I.; Cederlund, M.; Mulaw, M.; Weidinger, G. NF-κB Signaling Negatively Regulates Osteoblast Dedifferentiation during Zebrafish Bone Regeneration. Dev. Cell, 2020, 52(2), 167-182.e7. doi: 10.1016/j.devcel.2019.11.016 PMID: 31866203
  49. Zhu, J.; Yamane, H.; Paul, W.E. Differentiation of effector CD4 T cell populations (*). Annu. Rev. Immunol., 2010, 28(1), 445-489. doi: 10.1146/annurev-immunol-030409-101212 PMID: 20192806
  50. Hori, S.; Nomura, T.; Sakaguchi, S. Control of regulatory T cell development by the transcription factor Foxp3. Science, 2003, 299(5609), 1057-1061. doi: 10.1126/science.1079490 PMID: 12522256
  51. Martín-Fontecha, A.; Thomsen, L.L.; Brett, S.; Gerard, C.; Lipp, M.; Lanzavecchia, A.; Sallusto, F. Induced recruitment of NK cells to lymph nodes provides IFN-γ for TH1 priming. Nat. Immunol., 2004, 5(12), 1260-1265. doi: 10.1038/ni1138 PMID: 15531883
  52. Murphy, K.M.; Reiner, S.L. The lineage decisions of helper T cells. Nat. Rev. Immunol., 2002, 2(12), 933-944. doi: 10.1038/nri954 PMID: 12461566
  53. Sato, K.; Suematsu, A.; Okamoto, K.; Yamaguchi, A.; Morishita, Y.; Kadono, Y.; Tanaka, S.; Kodama, T.; Akira, S.; Iwakura, Y.; Cua, D.J.; Takayanagi, H. Th17 functions as an osteoclastogenic helper T cell subset that links T cell activation and bone destruction. J. Exp. Med., 2006, 203(12), 2673-2682. doi: 10.1084/jem.20061775 PMID: 17088434
  54. Dar, H.Y.; Shukla, P.; Mishra, P.K.; Anupam, R.; Mondal, R.K.; Tomar, G.B.; Sharma, V.; Srivastava, R.K. Lactobacillus acidophilus inhibits bone loss and increases bone heterogeneity in osteoporotic mice via modulating Treg-Th17 cell balance. Bone Rep., 2018, 8, 46-56. doi: 10.1016/j.bonr.2018.02.001 PMID: 29955622
  55. Dar, H.Y.; Singh, A.; Shukla, P.; Anupam, R.; Mondal, R.K.; Mishra, P.K.; Srivastava, R.K. High dietary salt intake correlates with modulated Th17-Treg cell balance resulting in enhanced bone loss and impaired bone-microarchitecture in male mice. Sci. Rep., 2018, 8(1), 2503. doi: 10.1038/s41598-018-20896-y PMID: 29410520
  56. Zaiss, M.M.; Axmann, R.; Zwerina, J.; Polzer, K.; Gückel, E.; Skapenko, A.; Schulze-Koops, H.; Horwood, N.; Cope, A.; Schett, G. Treg cells suppress osteoclast formation: A new link between the immune system and bone. Arthritis Rheum., 2007, 56(12), 4104-4112. doi: 10.1002/art.23138 PMID: 18050211
  57. Shashkova, E.V.; Trivedi, J.; Cline-Smith, A.B.; Ferris, C.; Buchwald, Z.S.; Gibbs, J.; Novack, D.; Aurora, R. Osteoclast-Primed Foxp3+ CD8 T Cells Induce T-bet, Eomesodermin, and IFN-γ To Regulate Bone Resorption. J. Immunol., 2016, 197(3), 726-735. doi: 10.4049/jimmunol.1600253 PMID: 27324129
  58. Peng, C.; Guo, Z.; Zhao, Y.; Li, R.; Wang, L.; Gong, W. Effect of lymphocyte subsets on bone density in senile osteoporosis: A retrospective study. J. Immunol. Res., 2022, 2022, 1-11. doi: 10.1155/2022/3337622 PMID: 36339939
  59. Xue, X.; Zhao, X.; Wang, J.; Wang, C.; Ma, C.; Zhang, Y.; Li, Y.; Peng, C. Carthami flos extract against carbon tetrachloride-induced liver fibrosis via alleviating angiogenesis in mice. Phytomedicine, 2023, 108, 154517. doi: 10.1016/j.phymed.2022.154517
  60. Najar, M.; Fayyad-Kazan, M.; Meuleman, N.; Bron, D.; Fayyad-Kazan, H.; Lagneaux, L. Mesenchymal stromal cells of the bone marrow and natural killer cells: Cell interactions and cross modulation. J. Cell Commun. Signal., 2018, 12(4), 673-688. doi: 10.1007/s12079-018-0448-4 PMID: 29350342
  61. Najar, M.; Fayyad-Kazan, M.; Meuleman, N.; Bron, D.; Fayyad-Kazan, H.; Lagneaux, L. Immunomodulatory effects of foreskin mesenchymal stromal cells on natural killer cells. J. Cell. Physiol., 2018, 233(7), 5243-5254. doi: 10.1002/jcp.26305 PMID: 29194614
  62. Bondeson, J.; Blom, A.B.; Wainwright, S.; Hughes, C.; Caterson, B.; van den Berg, W.B. The role of synovial macrophages and macrophage‐produced mediators in driving inflammatory and destructive responses in osteoarthritis. Arthritis Rheum., 2010, 62(3), 647-657. doi: 10.1002/art.27290 PMID: 20187160
  63. Li, C.J.; Xiao, Y.; Yang, M.; Su, T.; Sun, X.; Guo, Q.; Huang, Y.; Luo, X.H. Long noncoding RNA Bmncr regulates mesenchymal stem cell fate during skeletal aging. J. Clin. Invest., 2018, 128(12), 5251-5266. doi: 10.1172/JCI99044 PMID: 30352426
  64. Nicolaidou, V.; Wong, M.M.; Redpath, A.N.; Ersek, A.; Baban, D.F.; Williams, L.M.; Cope, A.P.; Horwood, N.J. Monocytes induce STAT3 activation in human mesenchymal stem cells to promote osteoblast formation. PLoS One, 2012, 7(7), e39871. doi: 10.1371/journal.pone.0039871 PMID: 22802946
  65. Zhang, L.; Liu, T.; Chen, H.; Zhao, Q.; Liu, H. Predicting lncRNA–miRNA interactions based on interactome network and graphlet interaction. Genomics, 2021, 113(3), 874-880. doi: 10.1016/j.ygeno.2021.02.002 PMID: 33588070
  66. Chen, X.; Xie, D.; Zhao, Q.; You, Z.H. MicroRNAs and complex diseases: From experimental results to computational models. Brief. Bioinform., 2019, 20(2), 515-539. doi: 10.1093/bib/bbx130 PMID: 29045685

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