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Evaluation of Genes and Molecular Pathways Involved in the Progression of Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma: A Systems Biology Approach

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Abstract

Today, Monoclonal Gammopathy of Undetermined Significance (MGUS) is known as a plasma cell malignancy susceptible to evolving into the life-threatening stage, multiple myeloma (MM), without prominent clinical manifestations. Despite the discovery of advanced therapies and multiple pathogenic markers, the complexity of MM development has made it an incurable malignancy. In this study, the microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database and analyzed using the LIMMA package of R-software to determine differentially expressed genes (DEGs) in MGUS and MM compared to the control samples. Enrichment analysis of DEGs was evaluated using the GeneCodis4 software. Protein–protein interaction (PPI) networks were constructed via the GeneMANIA database, and Cytoscape visualized them. The Molecular Complex Detection (MCODE) plugin from Cytoscape was used to identify the key modules from the PPI network. Afterward, the hub genes were recognized using the cytoHubba plug-in in Cytoscape. Eventually, the correlation between hub-DEGs and MM-specific survival was evaluated via the PrognoScan database. A total of 138 (MM-normal) and 136 (MGUS-normal) DEGs were obtained from the datasets, and 62 common DEGs between MGUS and MM diseases (26 up-regulated and 36 down-regulated genes) were screened out for subsequent analyses. Following enrichment analyses and the PPI network's evaluation, FOS, FOSB, JUN, MAFF, and PPP1R15A involved in the progression of MGUS to MM were detected as the hub genes. The survival analysis revealed that FOS, FOSB, and JUN among hub genes were significantly associated with disease-specific survival (DSS) in MM. Identifying the genes involved in the progression of MGUS to MM can help in the design of preventive strategies as well as the treatment of patients. In addition, their evaluation can be effective in the survival of patients.

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Data Availability

This is a review study, and it is not an original. Data availability is corresponding author responsibility.

References

  1. Lomas, O. C., Mouhieddine, T. H., Tahri, S., & Ghobrial, I. M. (2020). Monoclonal gammopathy of undetermined significance (MGUS)—Not so asymptomatic after all. Cancers, 12(6), 1554.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Jin, K., Yan, Y., Chen, M., Wang, J., Pan, X., Liu, X., et al. (2022). Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration. Acta Ophthalmologica, 100(2), e512–e520.

    PubMed  Google Scholar 

  3. Korde, N., Kristinsson, S. Y., & Landgren, O. (2011). Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM): Novel biological insights and development of early treatment strategies. Blood: The Journal of the American Society of Hematology, 117(21), 5573–5581.

    CAS  Google Scholar 

  4. Ng, Y.-F., Chionh, C.-Y., Torres De Guzman, M. R., Nagarajan, C., & Loh, H.-L. (2021). Lambda light chain crystalline proximal tubulopathy with probable light chain cast nephropathy and clonal plasma cell infiltrate—Uncommon manifestations of a rare form of multiple myeloma. Journal of Nephropathology. https://doi.org/10.34172/jnp.2021.08

    Article  Google Scholar 

  5. Yan, J., Yao, Y., Yan, S., Gao, R., Lu, W., & He, W. (2020). Chiral protein supraparticles for tumor suppression and synergistic immunotherapy: An enabling strategy for bioactive supramolecular chirality construction. Nano Letters, 20(8), 5844–5852.

    CAS  PubMed  Google Scholar 

  6. Pang, L., Rajkumar, S. V., Kapoor, P., Buadi, F., Dispenzieri, A., Gertz, M., et al. (2021). Prognosis of young patients with monoclonal gammopathy of undetermined significance (MGUS). Blood Cancer Journal, 11(2), 1–8.

    Google Scholar 

  7. Sekkouri, K. A., Batta, F. Z., Belghiti, K. A., Alaoui, H., Bahae, H. S., Arrayhani, M., et al. (2016). Non-amyloid deposits glomerulopathy with multiple myeloma; a rare presentation. Immunopathologia Persa, 2(1), e08.

    Google Scholar 

  8. Cancarini, G., Terlizzi, V., Garatti, A., Zeni, L., Tonoli, M., Pezzini, E., et al. (2021). Supportive treatment for cast nephropathy in patients with multiple myeloma; a pilot study. Journal of Nephropharmacology, 10(2), e20.

    CAS  Google Scholar 

  9. Landgren, O., Kyle, R. A., Pfeiffer, R. M., Katzmann, J. A., Caporaso, N. E., Hayes, R. B., et al. (2009). Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: A prospective study. Blood: The Journal of the American Society of Hematology, 113(22), 5412–5417.

    CAS  Google Scholar 

  10. Zhuo, Z., Wan, Y., Guan, D., Ni, S., Wang, L., Zhang, Z., et al. (2020). A loop-based and AGO-Incorporated virtual screening model targeting AGO-Mediated miRNA–mRNA interactions for drug discovery to rescue bone phenotype in genetically modified Mice. Advanced Science, 7(13), 1903451.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Choi, Y., Wang, J., Zhu, Y., & Lai, W.-F. (2021). Student’s perception and expectation towards pharmacy education: A qualitative study of pharmacy students in a developing country. Indian Journal of Pharmaceutical Education and Research, 55(1), 63–69.

    Google Scholar 

  12. Borsi, S. H., Raji, H., Dargahi Malamir, M., Nokhostin, F., Kargaran, A. (2021). Rivaroxaban versus enoxaparin for treatment of patients with nonhematologic cancer with venous thromboembolism a randomized clinial trial. Tehran University Medical Journal TUMS Publications, 79(4), 281–289.

    Google Scholar 

  13. Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., et al. (2007). A systems biology approach for pathway level analysis. Genome Research, 17(10), 1537–1545.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Yousefi, S. R., Alshamsi, H. A., Amiri, O., & Salavati-Niasari, M. (2021). Synthesis, characterization and application of Co/Co3O4 nanocomposites as an effective photocatalyst for discoloration of organic dye contaminants in wastewater and antibacterial properties. Journal of Molecular Liquids, 337, 116405.

    CAS  Google Scholar 

  15. Mahdi, M. A., Yousefi, S. R., Jasim, L. S., & Salavati-Niasari, M. (2022). Green synthesis of DyBa2Fe3O7.988/DyFeO3 nanocomposites using almond extract with dual eco-friendly applications: Photocatalytic and antibacterial activities. International Journal of Hydrogen Energy, 47(31), 14319–14330.

    CAS  Google Scholar 

  16. Hou, Q., Huang, J., Xiong, X., Guo, Y., & Zhang, B. (2022). Role of nutrient-sensing receptor GPRC6A in regulating colonic group 3 innate lymphoid cells and inflamed mucosal healing. Journal of Crohn’s and Colitis, 20, 1–13.

    Google Scholar 

  17. Liu, C., Wang, Y., Li, L., He, D., Chi, J., Li, Q., et al. (2022). Engineered extracellular vesicles and their mimetics for cancer immunotherapy. Journal of Controlled Release, 349, 679–698.

    CAS  PubMed  Google Scholar 

  18. Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., et al. (2012). NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Research, 41(D1), D991–D995.

    PubMed  PubMed Central  Google Scholar 

  19. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.

    PubMed  PubMed Central  Google Scholar 

  20. Carmona-Saez, P., Chagoyen, M., Tirado, F., Carazo, J. M., & Pascual-Montano, A. (2007). GENECODIS: A web-based tool for finding significant concurrent annotations in gene lists. Genome Biology, 8(1), 1–8.

    Google Scholar 

  21. Franz, M., Rodriguez, H., Lopes, C., Zuberi, K., Montojo, J., Bader, G. D., et al. (2018). GeneMANIA update 2018. Nucleic Acids Research, 46(W1), W60–W64.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P.-L., & Ideker, T. (2011). Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics, 27(3), 431–432.

    CAS  PubMed  Google Scholar 

  23. Chin, C.-H., Chen, S.-H., Wu, H.-H., Ho, C.-W., Ko, M.-T., & Lin, C.-Y. (2014). cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 8(4), 1–7.

    Google Scholar 

  24. Mizuno, H., Kitada, K., Nakai, K., & Sarai, A. (2009). PrognoScan: A new database for meta-analysis of the prognostic value of genes. BMC Medical Genomics, 2(1), 1–11.

    Google Scholar 

  25. Firth, J. (2019). Haematology: Multiple myeloma. Clinical Medicine (London), 19(1), 58–60.

    Google Scholar 

  26. Dhodapkar, M. V. (2016). MGUS to myeloma: A mysterious gammopathy of underexplored significance. Blood, 128(23), 2599–2606.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Alipanahzadeh, H., Ghulamreza, R., Shokouhian, M., Bagheri, M., & Maleknia, M. (2020). Deep vein thrombosis: A less noticed complication in hematologic malignancies and immunologic disorders. Journal of Thrombosis and Thrombolysis, 50(2), 318–329.

    PubMed  Google Scholar 

  28. Rajkumar, S. V. (2020). Multiple myeloma: 2020 Update on diagnosis, risk-stratification and management. American Journal of Hematology, 95(5), 548–567.

    CAS  PubMed  Google Scholar 

  29. Manier, S., Salem, K. Z., Park, J., Landau, D. A., Getz, G., & Ghobrial, I. M. (2017). Genomic complexity of multiple myeloma and its clinical implications. Nature Reviews Clinical Oncology, 14(2), 100–113.

    CAS  PubMed  Google Scholar 

  30. Bianchi, G., & Munshi, N. C. (2015). Pathogenesis beyond the cancer clone(s) in multiple myeloma. Blood, 125(20), 3049–3058.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Rajan, A. M., & Rajkumar, S. V. (2015). Interpretation of cytogenetic results in multiple myeloma for clinical practice. Blood Cancer Journal, 5(10), e365.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Kuehl, W. M., & Bergsagel, P. L. (2002). Multiple myeloma: Evolving genetic events and host interactions. Nature Reviews Cancer, 2(3), 175–187.

    CAS  PubMed  Google Scholar 

  33. Gholami, F., Ghasemi, A., Bahrami, A. R., Bidkhouri, H. R., Mishkin, H. N., Rad, M. P., et al. (2019). The therapeutic effect of autologous bone marrow mesenchymal stem cells to prevent the progress of chronic allograft nephropathy. Journal of Renal Injury Prevention, 8(1), 1–5.

    CAS  Google Scholar 

  34. Türkmen, S., Binder, A., Gerlach, A., Niehage, S., Theodora Melissari, M., Inandiklioglu, N., et al. (2014). High prevalence of immunoglobulin light chain gene aberrations as revealed by FISH in multiple myeloma and MGUS. Genes, Chromosomes and Cancer, 53(8), 650–656.

    PubMed  Google Scholar 

  35. Chretien, M. L., Corre, J., Lauwers-Cances, V., Magrangeas, F., Cleynen, A., Yon, E., et al. (2015). Understanding the role of hyperdiploidy in myeloma prognosis: Which trisomies really matter? Blood, 126(25), 2713–2719.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Saxe, D., Seo, E. J., Bergeron, M. B., & Han, J. Y. (2019). Recent advances in cytogenetic characterization of multiple myeloma. International Journal of Laboratory Hematology, 41(1), 5–14.

    PubMed  Google Scholar 

  37. Prideaux, S. M., Conway O’Brien, E., & Chevassut, T. J. (2014). The genetic architecture of multiple myeloma. Advances in Hematology, 2014, 864058.

    PubMed  PubMed Central  Google Scholar 

  38. Walker, B. A., Boyle, E. M., Wardell, C. P., Murison, A., Begum, D. B., Dahir, N. M., et al. (2015). Mutational spectrum, copy number changes, and outcome: Results of a sequencing study of patients with newly diagnosed myeloma. Journal of Clinical Oncology, 33(33), 3911–3920.

    CAS  PubMed  Google Scholar 

  39. Moreau, P., & Rajkumar, S. V. (2016). Multiple myeloma—Translation of trial results into reality. Lancet, 388(10040), 111–113.

    PubMed  Google Scholar 

  40. Fan, F., Bashari, M. H., Morelli, E., Tonon, G., Malvestiti, S., Vallet, S., et al. (2017). The AP-1 transcription factor JunB is essential for multiple myeloma cell proliferation and drug resistance in the bone marrow microenvironment. Leukemia, 31(7), 1570–1581.

    CAS  PubMed  Google Scholar 

  41. Shaulian, E. (2010). AP-1—The Jun proteins: Oncogenes or tumor suppressors in disguise? Cellular Signalling, 22(6), 894–899.

    CAS  PubMed  Google Scholar 

  42. Fan, F., & Podar, K. (2021). The role of AP-1 Transcription factors in plasma cell biology and multiple myeloma pathophysiology. Cancers (Basel), 13(10), 2326.

    CAS  PubMed  Google Scholar 

  43. Atsaves, V., Leventaki, V., Rassidakis, G. Z., & Claret, F. X. (2019). AP-1 transcription factors as regulators of immune responses in cancer. Cancers (Basel), 11(7), 1037.

    CAS  PubMed  Google Scholar 

  44. Cannarile, L., Delfino, D. V., Adorisio, S., Riccardi, C., & Ayroldi, E. (2019). Implicating the role of GILZ in glucocorticoid modulation of T-cell activation. Frontiers in Immunology, 10, 1823.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Grötsch, B., Brachs, S., Lang, C., Luther, J., Derer, A., Schlötzer-Schrehardt, U., et al. (2014). The AP-1 transcription factor Fra1 inhibits follicular B cell differentiation into plasma cells. Journal of Experimental Medicine, 211(11), 2199–2212.

    PubMed  PubMed Central  Google Scholar 

  46. Ubieta, K., Garcia, M., Grötsch, B., Uebe, S., Weber, G. F., Stein, M., et al. (2017). Fra-2 regulates B cell development by enhancing IRF4 and Foxo1 transcription. Journal of Experimental Medicine, 214(7), 2059–2071.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Takayanagi, H., Kim, S., Koga, T., Nishina, H., Isshiki, M., Yoshida, H., et al. (2002). Induction and activation of the transcription factor NFATc1 (NFAT2) integrate RANKL signaling in terminal differentiation of osteoclasts. Developmental Cell, 3(6), 889–901.

    CAS  PubMed  Google Scholar 

  48. Li, Z., Teng, M., Jiang, Y., Zhang, L., Luo, X., Liao, Y., et al. (2022). YTHDF1 negatively regulates Treponema pallidum-induced inflammation in THP-1 macrophages by promoting SOCS3 translation in an m6A-dependent manner. Frontiers in Immunology, 13, 857727.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Hedvat, C. V., Comenzo, R. L., Teruya-Feldstein, J., Olshen, A. B., Ely, S. A., Osman, K., et al. (2003). Insights into extramedullary tumour cell growth revealed by expression profiling of human plasmacytomas and multiple myeloma. British Journal of Haematology, 122(5), 728–744.

    CAS  PubMed  Google Scholar 

  50. Shen, Y. J., Mishima, Y., Shi, J., Sklavenitis-Pistofidis, R., Redd, R. A., Moschetta, M., et al. (2021). Progression signature underlies clonal evolution and dissemination of multiple myeloma. Blood, 137(17), 2360–2372.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Biran, N., Siegel, D. S., & Vesole, D. H. (2018). The forgotten class of drugs for multiple myeloma: HDAC inhibitors. Lancet Haematology, 5(12), e604–e605.

    PubMed  Google Scholar 

  52. Kiziltepe, T., Hideshima, T., Catley, L., Raje, N., Yasui, H., Shiraishi, N., et al. (2007). 5-Azacytidine, a DNA methyltransferase inhibitor, induces ATR-mediated DNA double-strand break responses, apoptosis, and synergistic cytotoxicity with doxorubicin and bortezomib against multiple myeloma cells. Molecular Cancer Therapy, 6(6), 1718–1727.

    CAS  Google Scholar 

  53. Fan, F., Malvestiti, S., Vallet, S., Lind, J., Garcia-Manteiga, J. M., Morelli, E., et al. (2021). JunB is a key regulator of multiple myeloma bone marrow angiogenesis. Leukemia, 35(12), 3509–3525.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Podar, K., Raab, M. S., Tonon, G., Sattler, M., Barilà, D., Zhang, J., et al. (2007). Up-regulation of c-Jun inhibits proliferation and induces apoptosis via caspase-triggered c-Abl cleavage in human multiple myeloma. Cancer Research, 67(4), 1680–1688.

    CAS  PubMed  Google Scholar 

  55. Chen, L., Wang, S., Zhou, Y., Wu, X., Entin, I., Epstein, J., et al. (2010). Identification of early growth response protein 1 (EGR-1) as a novel target for JUN-induced apoptosis in multiple myeloma. Blood, 115(1), 61–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Liu, P., Shi, J., & Wang, Z.-A. (2013). Pattern formation of the attraction–repulsion Keller-Segel system. Discrete and Continuous Dynamical Systems Series B, 18(10), 2597.

    Google Scholar 

  57. Carrasco, D. R., Tonon, G., Huang, Y., Zhang, Y., Sinha, R., Feng, B., et al. (2006). High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell, 9(4), 313–325.

    CAS  PubMed  Google Scholar 

  58. Colla, S., Zhan, F., Xiong, W., Wu, X., Xu, H., Stephens, O., et al. (2007). The oxidative stress response regulates DKK1 expression through the JNK signaling cascade in multiple myeloma plasma cells. Blood, 109(10), 4470–4477.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Fan, F., Tonon, G., Bashari, M. H., Vallet, S., Antonini, E., Goldschmidt, H., et al. (2014). Targeting Mcl-1 for multiple myeloma (MM) therapy: Drug-induced generation of Mcl-1 fragment Mcl-1(128–350) triggers MM cell death via c-Jun upregulation. Cancer Letters, 343(2), 286–294.

    CAS  PubMed  Google Scholar 

  60. Annunziata, C. M., Hernandez, L., Davis, R. E., Zingone, A., Lamy, L., Lam, L. T., et al. (2011). A mechanistic rationale for MEK inhibitor therapy in myeloma based on blockade of MAF oncogene expression. Blood, 117(8), 2396–2404.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Walker, B. A., Mavrommatis, K., Wardell, C. P., Ashby, T. C., Bauer, M., Davies, F. E., et al. (2018). Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood, 132(6), 587–597.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Bergsagel, P. L., & Kuehl, W. M. (2001). Chromosome translocations in multiple myeloma. Oncogene, 20(40), 5611–5622.

    CAS  PubMed  Google Scholar 

  63. Lai, W.-F., Tang, R., & Wong, W.-T. (2020). Ionically crosslinked complex gels loaded with oleic acid-containing vesicles for transdermal drug delivery. Pharmaceutics, 12(8), 725.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Pratt, G., & Morris, T. C. (2017). Review of the NICE guidelines for multiple myeloma. International Journal of Laboratory Hematology, 39(1), 3–13.

    CAS  PubMed  Google Scholar 

  65. Qiang, Y. W., Ye, S., Huang, Y., Chen, Y., Van Rhee, F., Epstein, J., et al. (2018). MAFb protein confers intrinsic resistance to proteasome inhibitors in multiple myeloma. BMC Cancer, 18(1), 724.

    PubMed  PubMed Central  Google Scholar 

  66. Hao, P., Li, H., Zhou, L., Sun, H., Han, J., & Zhang, Z. (2022). Serum metal ion-induced cross-linking of photoelectrochemical peptides and circulating proteins for evaluating cardiac ischemia/reperfusion. ACS Sensors, 7(3), 775–783.

    CAS  PubMed  Google Scholar 

  67. Hideshima, T., Chauhan, D., Richardson, P., Mitsiades, C., Mitsiades, N., Hayashi, T., et al. (2002). NF-kappa B as a therapeutic target in multiple myeloma. Journal of Biological Chemistry, 277(19), 16639–16647.

    CAS  PubMed  Google Scholar 

  68. He, X., Zhu, Y., Yang, L., Wang, Z., Wang, Z., Feng, J., et al. (2021). MgFe-LDH nanoparticles: A promising leukemia inhibitory factor replacement for self-renewal and pluripotency maintenance in cultured mouse embryonic stem cells. Advanced Science, 8(9), 2003535.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Olivier, S., Close, P., Castermans, E., de Leval, L., Tabruyn, S., Chariot, A., et al. (2006). Raloxifene-induced myeloma cell apoptosis: A study of nuclear factor-kappaB inhibition and gene expression signature. Molecular Pharmacology, 69(5), 1615–1623.

    CAS  PubMed  Google Scholar 

  70. Jin, H.-Y., & Wang, Z.-A. (2019). Global stabilization of the full attraction–repulsion Keller–Segel system. arXiv preprint. arXiv:190505990

  71. Yang, W., Liu, W., Li, X., Yan, J., & He, W. (2022). Turning chiral peptides into a racemic supraparticle to induce the self-degradation of MDM2. Journal of Advanced Research. https://doi.org/10.1016/j.jare.2022.05.009

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors appreciate and thank the efforts of the Center for the Development of Clinical Researches of the Educational and Therapeutic Research Complex of Hazrat-e Rasool General Hospital.

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HR conceived the manuscript and revised it. MM and BSA wrote the manuscript. RM and AH analysis of data. PK conducted the revise. FF conducted the native English.

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Correspondence to Hadi Rezaeeyan.

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Khalili, P., Maddah, R., Maleknia, M. et al. Evaluation of Genes and Molecular Pathways Involved in the Progression of Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma: A Systems Biology Approach. Mol Biotechnol 65, 1275–1286 (2023). https://doi.org/10.1007/s12033-022-00634-6

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