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SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation

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Abstract

Background

Osteoporosis (OP) is characterized by bone mass decrease and bone tissue microarchitectural deterioration in bone tissue. This study identified potential biomarkers for early diagnosis of OP and elucidated the mechanism of OP.

Methods

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) for the GSE56814 dataset. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to identify key modules associated with healthy and OP samples. Functional enrichment analysis was conducted using the R clusterProfiler package for modules to construct the transcriptional regulatory factor networks. We used the “ggpubr” package in R to screen for differentially expressed genes between the two samples. Gene set variation analysis (GSVA) was employed to further validate hub gene expression levels between normal and OP samples using RT-PCR and immunofluorescence to evaluate the potential biological changes in various samples.

Results

There was a distinction between the normal and OP conditions based on the preserved significant module. A total of 100 genes with the highest MM scores were considered key genes. Functional enrichment analysis suggested that the top 10 biological processes, cellular component and molecular functions were enriched. The Toll-like receptor signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, osteoclast differentiation, JAK-STAT signaling pathway, and chemokine signaling pathway were identified by Kyoto Encyclopedia of Genes and Genomes pathway analysis. SIRT1 and ZNF350 were identified by Wilcoxon algorithm as hub differentially expressed transcriptional regulatory factors that promote OP progression by affecting oxidative phosphorylation, apoptosis, PI3K-Akt-mTOR signaling, and p53 pathway. According to RT-PCR and immunostaining results, SIRT1 and ZNF350 levels were significantly higher in OP samples than in normal samples.

Conclusion

SIRT1 and ZNF350 are important transcriptional regulatory factors for the pathogenesis of OP and may be novel biomarkers for OP treatment.

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Data availability

All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analyzed during this study are included in this published article. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BP:

Biological processes

CC:

Cellular components

FC:

Fold change

FGF23:

Fibroblast growth factor 23

GEO:

Gene Expression Omnibus

Gly:

Glycyrrhizic acid

GO:

Gene Ontology

GSVA:

Gene set variation analysis

IL-6:

Interleukin 6

KEGG:

Kyoto Encyclopedia of Genes and Genomes

ME:

Module eigengene

MF:

Molecular function

MM:

Modular association

OP:

Osteoporosis

RANK:

Receptor activator of nuclear factor (NF)-κB

RT-qPCR:

Reverse-transcription quantitative polymerase chain reaction

SIRT1:

Silent mating type information regulation 2 homolog- 1

TNFR:

Tumor necrosis factor receptor

TOM:

Topological overlap matrix

WGCNA:

Weighted gene co-expression network analysis

ZNF350:

Zinc-finger 350

References

  1. Srivastava M, Deal C (2002) Osteoporosis in elderly: prevention and treatment. Clin Geriatr Med 18:529–555. https://doi.org/10.1016/s0749-0690(02)00022-8

    Article  PubMed  Google Scholar 

  2. Link TM, Majumdar S (2003) Osteoporosis imaging. Radiol Clin North Am 41:813–839. https://doi.org/10.1016/s0033-8389(03)00059-9

    Article  PubMed  Google Scholar 

  3. Straka M, Straka-Trapezanlidis M, Deglovic J, Varga I (2015) Periodontitis and osteoporosis. Neuro Endocrinol Lett 36:401–406

    CAS  PubMed  Google Scholar 

  4. Bijelic R, Milicevic S, Balaban J (2017) Risk factors for osteoporosis in Postmenopausal Women. Med Arch 71:25–28. https://doi.org/10.5455/medarh.2017.71.25-28

    Article  PubMed  PubMed Central  Google Scholar 

  5. Feng Y, Wan P, Yin L, Lou X (2020) The inhibition of MicroRNA-139-5p promoted osteoporosis of bone marrow-derived mesenchymal stem cells by targeting Wnt/Beta-Catenin signaling pathway by NOTCH1. J Microbiol Biotechnol 30:448–458. https://doi.org/10.4014/jmb.1908.08036

    Article  CAS  PubMed  Google Scholar 

  6. Wang CG, Hu YH, Su SL, Zhong D (2020) LncRNA DANCR and miR-320a suppressed osteogenic differentiation in osteoporosis by directly inhibiting the Wnt/beta-catenin signaling pathway. Exp Mol Med 52:1310–1325. https://doi.org/10.1038/s12276-020-0475-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Xu L, Zhang L, Zhang H et al (2018) The participation of fibroblast growth factor 23 (FGF23) in the progression of osteoporosis via JAK/STAT pathway. J Cell Biochem 119:3819–3828. https://doi.org/10.1002/jcb.26332

    Article  CAS  PubMed  Google Scholar 

  8. Damerau A, Gaber T, Ohrndorf S, Hoff P (2020) JAK/STAT activation: a general mechanism for Bone Development, Homeostasis, and regeneration. Int J Mol Sci 21. https://doi.org/10.3390/ijms21239004

  9. Wang XL, Liu YM, Zhang ZD et al (2020) Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis. J Cancer Res Ther 16:867–873. https://doi.org/10.4103/0973-1482.204842

    Article  CAS  PubMed  Google Scholar 

  10. Yang Z, Zi Q, Xu K et al (2021) Development of a macrophages-related 4-gene signature and nomogram for the overall survival prediction of hepatocellular carcinoma based on WGCNA and LASSO algorithm. Int Immunopharmacol 90:107238. https://doi.org/10.1016/j.intimp.2020.107238

    Article  CAS  PubMed  Google Scholar 

  11. Tian Z, He W, Tang J et al (2020) Identification of important modules and biomarkers in breast Cancer based on WGCNA. Onco Targets Ther 13:6805–6817. https://doi.org/10.2147/OTT.S258439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Feng T, Li K, Zheng P et al (2019) Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus. Oxid Med Cell Longev 2019:9567641. https://doi.org/10.1155/2019/9567641

  13. Rangaraju S, Dammer EB, Raza SA et al (2018) Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease. Mol Neurodegener 13:24. https://doi.org/10.1186/s13024-018-0254-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tang Y, Ke ZP, Peng YG, Cai PT (2018) Co-expression analysis reveals key gene modules and pathway of human coronary heart disease. J Cell Biochem 119:2102–2109. https://doi.org/10.1002/jcb.26372

    Article  CAS  PubMed  Google Scholar 

  15. Xia B, Li Y, Zhou J et al (2017) Identification of potential pathogenic genes associated with osteoporosis. Bone Joint Res 6:640–648. https://doi.org/10.1302/2046-3758.612.BJR-2017-0102.R1

    Article  CAS  PubMed  Google Scholar 

  16. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559. https://doi.org/10.1186/1471-2105-9-559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhou RH, Chen C, Jin SH et al (2020) Co-expression gene modules involved in cisplatin-induced peripheral neuropathy according to sensitivity, status, and severity. J Peripher Nerv Syst 25:366–376. https://doi.org/10.1111/jns.12407

    Article  CAS  PubMed  Google Scholar 

  18. Han H, Shim H, Shin D et al (2015) TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep 5:11432. https://doi.org/10.1038/srep11432

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Han H, Cho JW, Lee S et al (2018) TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46:D380–D386. https://doi.org/10.1093/nar/gkx1013

    Article  CAS  PubMed  Google Scholar 

  20. Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7. https://doi.org/10.1186/1471-2105-14-7

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang Z, Wang D, Liu Y et al (2021) Mesenchymal stem cell in mice uterine and its therapeutic effect on osteoporosis. Rejuvenation Res 24:139–150. https://doi.org/10.1089/rej.2019.2262

    Article  CAS  PubMed  Google Scholar 

  23. Miller PD (2016) Management of severe osteoporosis. Expert Opin Pharmacother 17:473–488. https://doi.org/10.1517/14656566.2016.1124856

    Article  CAS  PubMed  Google Scholar 

  24. Kaliman P, Vinals F, Testar X et al (1996) Phosphatidylinositol 3-kinase inhibitors block differentiation of skeletal muscle cells. J Biol Chem 271:19146–19151. https://doi.org/10.1074/jbc.271.32.19146

    Article  CAS  PubMed  Google Scholar 

  25. Sakaue H, Ogawa W, Matsumoto M et al (1998) Posttranscriptional control of adipocyte differentiation through activation of phosphoinositide 3-kinase. J Biol Chem 273:28945–28952. https://doi.org/10.1074/jbc.273.44.28945

    Article  CAS  PubMed  Google Scholar 

  26. Ghosh-Choudhury N, Abboud SL, Nishimura R et al (2002) Requirement of BMP-2-induced phosphatidylinositol 3-kinase and akt serine/threonine kinase in osteoblast differentiation and smad-dependent BMP-2 gene transcription. J Biol Chem 277:33361–33368. https://doi.org/10.1074/jbc.M205053200

    Article  CAS  PubMed  Google Scholar 

  27. Xi JC, Zang HY, Guo LX et al (2015) The PI3K/AKT cell signaling pathway is involved in regulation of osteoporosis. J Recept Signal Transduct Res 35:640–645. https://doi.org/10.3109/10799893.2015.1041647

    Article  CAS  PubMed  Google Scholar 

  28. Asagiri M, Takayanagi H (2007) The molecular understanding of osteoclast differentiation. Bone 40:251–264. https://doi.org/10.1016/j.bone.2006.09.023

    Article  CAS  PubMed  Google Scholar 

  29. Cao B, Dai X, Wang W (2019) Knockdown of TRPV4 suppresses osteoclast differentiation and osteoporosis by inhibiting autophagy through ca(2+) -calcineurin-NFATc1 pathway. J Cell Physiol 234:6831–6841. https://doi.org/10.1002/jcp.27432

    Article  CAS  PubMed  Google Scholar 

  30. Yin Z, Zhu W, Wu Q et al (2019) Glycyrrhizic acid suppresses osteoclast differentiation and postmenopausal osteoporosis by modulating the NF-kappaB, ERK, and JNK signaling pathways. Eur J Pharmacol 859:172550. https://doi.org/10.1016/j.ejphar.2019.172550

    Article  CAS  PubMed  Google Scholar 

  31. Zi Z, Cho KH, Sung MH et al (2005) In silico identification of the key components and steps in IFN-gamma induced JAK-STAT signaling pathway. FEBS Lett 579:1101–1108. https://doi.org/10.1016/j.febslet.2005.01.009

    Article  CAS  PubMed  Google Scholar 

  32. Carafa V, Nebbioso A, Altucci L (2012) Sirtuins and disease: the road ahead. Front Pharmacol 3:4. https://doi.org/10.3389/fphar.2012.00004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Godfrin-Valnet M, Khan KA, Guillot X et al (2014) Sirtuin 1 activity in peripheral blood mononuclear cells of patients with osteoporosis. Med Sci Monit Basic Res 20:142–145. https://doi.org/10.12659/MSMBR.891372

    Article  PubMed  PubMed Central  Google Scholar 

  34. Simic P, Zainabadi K, Bell E et al (2013) SIRT1 regulates differentiation of mesenchymal stem cells by deacetylating beta-catenin. EMBO Mol Med 5:430–440. https://doi.org/10.1002/emmm.201201606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Edwards JR, Perrien DS, Fleming N et al (2013) Silent information regulator (Sir)T1 inhibits NF-kappaB signaling to maintain normal skeletal remodeling. J Bone Min Res 28:960–969. https://doi.org/10.1002/jbmr.1824

    Article  CAS  Google Scholar 

  36. Mercken EM, Mitchell SJ, Martin-Montalvo A et al (2014) SRT2104 extends survival of male mice on a standard diet and preserves bone and muscle mass. Aging Cell 13:787–796. https://doi.org/10.1111/acel.12220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Louvet L, Leterme D, Delplace S et al (2020) Sirtuin 1 deficiency decreases bone mass and increases bone marrow adiposity in a mouse model of chronic energy deficiency. Bone 136:115361. https://doi.org/10.1016/j.bone.2020.115361

    Article  CAS  PubMed  Google Scholar 

  38. Ke L, Li Q, Song J et al (2021) The mitochondrial biogenesis signaling pathway is a potential therapeutic target for myasthenia gravis via energy metabolism (review). Exp Ther Med 22:702. https://doi.org/10.3892/etm.2021.10134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lu C, Zhao H, Liu Y et al (2023) Novel role of the SIRT1 in endocrine and metabolic diseases. Int J Biol Sci 19:484–501. https://doi.org/10.7150/ijbs.78654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Toorie AM, Cyr NE, Steger JS et al (2016) The nutrient and energy sensor Sirt1 regulates the hypothalamic-pituitary-adrenal (HPA) Axis by altering the production of the Prohormone Convertase 2 (PC2) essential in the maturation of corticotropin-releasing hormone (CRH) from its prohormone in male rats. J Biol Chem 291:5844–5859. https://doi.org/10.1074/jbc.M115.675264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang X, Chen L, Peng W (2017) Protective effects of resveratrol on osteoporosis via activation of the SIRT1-NF-κB signaling pathway in rats. Exp Ther Med 14:5032–5038. https://doi.org/10.3892/etm.2017.5147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tozzi R, Cipriani F, Masi D et al (2022) Ketone bodies and SIRT1, Synergic Epigenetic Regulators for Metabolic Health: a narrative review. Nutrients 14:3145. https://doi.org/10.3390/nu14153145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nakagawa T, Guarente L (2011) Sirtuins at a glance. J Cell Sci 124:833–838. https://doi.org/10.1242/jcs.081067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cohen-Kfir E, Artsi H, Levin A et al (2011) Sirt1 is a regulator of bone mass and a repressor of Sost encoding for sclerostin, a bone formation inhibitor. Endocrinology 152:4514–4524. https://doi.org/2020071613282569900

    Article  CAS  PubMed  Google Scholar 

  45. Garcia V, Dominguez G, Garcia JM et al (2004) Altered expression of the ZBRK1 gene in human breast carcinomas. J Pathol 202:224–232. https://doi.org/10.1002/path.1513

    Article  CAS  PubMed  Google Scholar 

  46. Garcia V, Garcia JM, Pena C et al (2005) The GADD45, ZBRK1 and BRCA1 pathway: quantitative analysis of mRNA expression in colon carcinomas. J Pathol 206:92–99. https://doi.org/10.1002/path.1751

    Article  CAS  PubMed  Google Scholar 

  47. Lin LF, Chuang CH, Li CF et al (2010) ZBRK1 acts as a metastatic suppressor by directly regulating MMP9 in cervical cancer. Cancer Res 70:192–201. https://doi.org/10.1158/0008-5472.CAN-09-2641

    Article  CAS  PubMed  Google Scholar 

  48. Patnaik S, George SP, Pham E et al (2016) By moonlighting in the nucleus, villin regulates epithelial plasticity. Mol Biol Cell 27:535–548. https://doi.org/10.1091/mbc.E15-06-0453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang Y, Zhang J, Feng D et al (2022) IRF1/ZNF350/GPX4-mediated ferroptosis of renal tubular epithelial cells promote chronic renal allograft interstitial fibrosis. Free Radic Biol Med 193:579–594. https://doi.org/10.1016/j.freeradbiomed.2022.11.002

    Article  CAS  PubMed  Google Scholar 

  50. Lin Y, Gong H, Liu J et al (2023) HECW1 induces NCOA4-regulated ferroptosis in glioma through the ubiquitination and degradation of ZNF350. Cell Death Dis 14:794. https://doi.org/10.1038/s41419-023-06322-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank Editage (www.editage.com) for English language editing.

Funding

This work was supported by the Medical Science Research Project Program of Hebei Provincial Health Commission (No. 20210121) ;Hebei Natural Science Foundation (H2022406038). National Natural Science Foundation of China (82305055).

Author information

Author notes

  1. Naiqiang Zhu and Jingyi Hou equally contributed to this work.

Authors and Affiliations

  1. Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China

    Naiqiang Zhu, Xu Wei & Liguo Zhu

  2. Department of Minimally Invasive Spinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, China

    Naiqiang Zhu & Bin Chen

  3. South Operation Department, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, China

    Jingyuan Si

  4. Central Laboratory, The Affiliated Hospital of Chengde Medical University, Chengde, 067000, China

    Ning Yang

  5. Chengde Medical University, Chengde, 067000, China

    Jingyi Hou

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  1. Naiqiang Zhu
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Contributions

[Xu, Wei] and [Liguo, Zhu] contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Jingyi, Hou], [Jingyuan, Si], [Bin Chen]. The experimental validation was performed by [Ning, Yang]. The first draft of the manuscript was written by [Naiqiang, Zhu] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xu Wei or Liguo Zhu.

Ethics declarations

Ethics approval

Three osteoporotic bone tissues were obtained from patients with osteoporotic fracture surgery. Three normal bone tissues were obtained from patients with traumatic amputation. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of The Affiliated Hospital of Chengde Medical University (2022.06.16/ No. CYFYLL2020240).

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Informed consent was obtained from all individual participants included in the study.

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Conflicts of interest

This study does not increase the medical costs and suffering of the subjects, and research materials and research results are used for scientific purposes without a conflict of interest.

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Supplementary Fig. 1

. Clustering dendrogram of samples based on their Euclidean distance. (A) OP samples; (B) normal samples

Supplementary Fig. 2

. The optimal soft threshold power of the WGCNA was determined by calculating the scale-free topological fit index and the average connectivity. (A) OP samples; (B) normal samples

Supplementary table 1

. The detailed information of patients with OP and normal samples

Supplementary table 2

. Prime sequences for the hub genes

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Zhu, N., Hou, J., Si, J. et al. SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation. Mol Biol Rep 51, 530 (2024). https://doi.org/10.1007/s11033-024-09406-8

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