Viral microRNA in HPV16-associated cervical cancer: expression, diagnostic potential, and biological functions

Nadezhda V. Elkina , Mariya D. Fedorova , Radik S. Faskhutdinov , Iuliia O. Iurchenko , Kirill I. Zhordania , Ekaterina A. Mustafina , Larisa S. Pavlova , Svetlana V. Vinokurova

Russian Journal of Oncology ›› 2024, Vol. 29 ›› Issue (3) : 183 -194.

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Russian Journal of Oncology ›› 2024, Vol. 29 ›› Issue (3) : 183 -194. DOI: 10.17816/onco637133
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Viral microRNA in HPV16-associated cervical cancer: expression, diagnostic potential, and biological functions

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Abstract

BACKGROUND: Cervical cancer (CC) is the fourth most common cancer among women worldwide in terms of incidence and mortality. High-risk human papillomaviruses (HPVs) are etiologic factor of CC in more than 90% of cases, with type 16 HPV (HPV16) revealed in >50% of cancers. Dysregulation of expression of viral oncogenes E6 and E7 is the main cause of malignant transformation in infected cervical epithelial cells. The mechanisms of impaired expression of these genes are still underexplored. The dysfunction of viral microRNAs may be among the underlying factors.

AIM: To analyze the expression of HPV16-associated microRNA-H1 and microRNA-H2 in cervical cancer specimens, evaluate the correlation of their expression to viral load and overall patient survival, and analyze in silico their potential viral and cellular targets.

MATERIALS AND METHODS: The expression of HPV16 microRNA-H1 and HPV16 microRNA-H2 was evaluated in the real-time polymerase chain reaction. With this purpose, small RNAs were isolated from 36 specimens of HPV16-positive squamous cell carcinomas of the cervix. Further, the viral load was assessed after calculating the value of HPV16 DNA copies per cell. The association between microRNA expression and the viral load was evaluated using the nonparametric Spearman’s correlation coefficient. Kaplan-Meier curves were plotted to analyze the dependence of 5-year overall survival on the level of viral microRNA expression. The miRanda algorithm and online services mirDB, MR-microT and TargetScan Custom 5.2 were used for in silico search of theoretical microRNA targets.

RESULTS: MicroRNA-H1 expression was revealed in 33 of 38 specimens (86.8%), microRNA-H2 was detected in 37 of 38 specimens (97.4%) of HPV16-positive cervical cancer. There was a positive correlation between both microRNA-H1 (r=0.36, p=0.042) and microRNA-H2 (r=0.51, p=0.001) expression and HPV16 viral load. Higher level of expression of viral microRNA-H1 and microRNA-H2 tended to correlate with better overall patient survival. The theoretical microRNA-H1 (E7, E2, E5, L2 and URR) and microRNA-H2 (E1, E2, E5, L2, L1, URR) targets in the HPV16 genome were identified in silico, as well as theoretical cellular targets indicating possible regulation of cellular signaling pathways by means of viral microRNAs, both controlling normal viral cycle and promoting tumor transformation.

CONCLUSION: The results of this study demonstrate promising further investigation of the functions of viral microRNAs in relation with the infectious process and virus-induced malignant transformation, and their potential importance in the diagnosis of HPV16-associated cancers.

Keywords

HPV16 / epigenetics / viral microRNAs / HPV16-microRNA-H1 / HPV16-microRNA-H2 / cervical cancer

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Nadezhda V. Elkina, Mariya D. Fedorova, Radik S. Faskhutdinov, Iuliia O. Iurchenko, Kirill I. Zhordania, Ekaterina A. Mustafina, Larisa S. Pavlova, Svetlana V. Vinokurova. Viral microRNA in HPV16-associated cervical cancer: expression, diagnostic potential, and biological functions. Russian Journal of Oncology, 2024, 29(3): 183-194 DOI:10.17816/onco637133

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References

[1]

Sung H, Ferlay J, Siegel R, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–249. doi: 10.3322/caac.21660

[2]

Sung H., Ferlay J., Siegel R., et l. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries // CA Cancer J Clin. 2021. Vol. 71, N 3. P. 209–249. doi: 10.3322/caac.21660

[3]

Mesri E, Feitelson M, Munger K. Human viral oncogenesis: a cancer hallmarks analysis. Cell Host Microbe. 2014;15(3):266–82. doi: 10.1016/j.chom.2014.02.011

[4]

Mesri E., Feitelson M., Munger K. Human viral oncogenesis: a cancer hallmarks analysis // Cell Host Microbe. 2014. Vol. 15, N 3. P. 266–282. doi: 10.1016/j.chom.2014.02.011

[5]

MacLennan S, Marra M. Oncogenic Viruses and the Epigenome: How Viruses Hijack Epigenetic Mechanisms to Drive Cancer. Int J Mol Sci. 2023;24(11):9543. doi: 10.3390/ijms24119543

[6]

MacLennan S., Marra M. Oncogenic Viruses and the Epigenome: How Viruses Hijack Epigenetic Mechanisms to Drive Cancer // Int J Mol Sci. 2023. Vol. 24, N 11. P. 9543 doi: 10.3390/ijms24119543

[7]

O’Brien J, Hayder H, Zayed Y, Peng C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front Endocrinol (Lausanne). 2018;9:402. doi: 10.3389/fendo.2018.00402

[8]

O’Brien J., Hayder H., Zayed Y., Peng C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation // Front Endocrinol (Lausanne). 2018. Vol. 9. P. 402. doi: 10.3389/fendo.2018.00402

[9]

Jorge A, Pereira E, Oliveira C, et al. MicroRNAs: understanding their role in gene expression and cancer. Einstein (Sao Paulo). 2021;19:eRB5996. doi: 10.31744/einstein_journal/2021RB5996

[10]

Jorge A., Pereira E., Oliveira C., et al. MicroRNAs: understanding their role in gene expression and cancer // Einstein (Sao Paulo). 2021. Vol. 19. P. eRB5996. doi: 10.31744/einstein_journal/2021RB5996

[11]

Pfeffer S, Zavolan M, Grasser F, et al. Identification of virus-encoded microRNAs. Science. 2004;304(5671):734–736. doi: 10.1126/science.1096781

[12]

Pfeffer S., Zavolan M., Grasser F., et al. Identification of virus-encoded microRNAs // Science. 2004. Vol. 304, N 5671. P. 734–736. doi: 10.1126/science.1096781

[13]

Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47(D1):D155-D62. doi: 10.1093/nar/gky1141

[14]

Kozomara A., Birgaoanu M., Griffiths-Jones S. miRBase: from microRNA sequences to function // Nucleic Acids Res. 2019. Vol. 47, N D1. P. D155-D162. doi: 10.1093/nar/gky1141

[15]

Yang X, Li H, Sun H, et al. Hepatitis B Virus-Encoded MicroRNA Controls Viral Replication. J Virol. 2017;91(10):e01919-16. doi: 10.1128/JVI.01919-16

[16]

Yang X., Li H., Sun H., et al. Hepatitis B Virus-Encoded MicroRNA Controls Viral Replication // J Virol. 2017. Vol. 91, N 10. P. e01919-16doi: 10.1128/JVI.01919-16

[17]

Vojtechova Z, Tachezy R. The Role of miRNAs in Virus-Mediated Oncogenesis. Int J Mol Sci. 2018;19(4). doi: 10.3390/ijms19041217

[18]

Vojtechova Z., Tachezy R. The Role of miRNAs in Virus-Mediated Oncogenesis // Int J Mol Sci. 2018. Vol. 19, N 4. doi: 10.3390/ijms19041217

[19]

Kandeel M. Oncogenic Viruses-Encoded microRNAs and Their Role in the Progression of Cancer: Emerging Targets for Antiviral and Anticancer Therapies. Pharmaceuticals (Basel). 2023;16(4):485. doi: 10.3390/ph16040485

[20]

Kandeel M. Oncogenic Viruses-Encoded microRNAs and Their Role in the Progression of Cancer: Emerging Targets for Antiviral and Anticancer Therapies // Pharmaceuticals (Basel). 2023. Vol. 16, N 4. P. 485. doi: 10.3390/ph16040485

[21]

Bruni L, Albero G, Mena M, et al. ICO/IARC Information Centre on HPV and Cancer (HPV Information Centre). Human papillomavirus and related diseases in the world. Summary Report 10 March 2023. Available from: https://hpvcentre.net/. Accessed 10 october 2024

[22]

Bruni L., Albero G., Mena M., et al. ICO/IARC Information Centre on HPV and Cancer (HPV Information Centre). Human papillomavirus and related diseases in the world // Summary Report 10 March 2023. Режим доступа: https://hpvcentre.net/ Дата обращения: 10 октября 2024

[23]

Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. doi: 10.3322/caac.21834

[24]

Bray F., Laversanne M., Sung H., et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries // CA Cancer J Clin. 2024. Vol. 74, N 3. P. 229–263. doi: 10.3322/caac.21834

[25]

de Martel C, Georges D, Bray F, et al. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis. Lancet Glob Health. 2020;8(2):e180–e90. doi: 10.1016/S2214-109X(19)30488-7

[26]

de Martel C., Georges D., Bray F., et al. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis // Lancet Glob Health. 2020. Vol. 8, N 2. P. e180–e190. doi: 10.1016/S2214-109X(19)30488-7

[27]

Qian K, Pietila T, Ronty M, et al. Identification and validation of human papillomavirus encoded microRNAs. PLoS One. 2013;8(7):e70202. doi: 10.1371/journal.pone.0070202

[28]

Qian K., Pietila T., Ronty M., et al. Identification and validation of human papillomavirus encoded microRNAs // PLoS One. 2013. Vol. 8, N 7. P. e70202. doi: 10.1371/journal.pone.0070202

[29]

Virtanen E, Pietila T, Nieminen P, et al. Low expression levels of putative HPV encoded microRNAs in cervical samples. Springerplus. 2016;5(1):1856. doi: 10.1186/s40064-016-3524-3

[30]

Virtanen E., Pietila T., Nieminen P., et al. Low expression levels of putative HPV encoded microRNAs in cervical samples // Springerplus. 2016. Vol. 5, N 1. P. 1856. doi: 10.1186/s40064-016-3524-3

[31]

Chen C, Ridzon D, Broomer A, et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005;33(20):e179. doi: 10.1093/nar/gni178

[32]

Chen C., Ridzon D., Broomer A., et al. Real-time quantification of microRNAs by stem-loop RT-PCR // Nucleic Acids Res. 2005. Vol. 33, N 20. P. e179. doi: 10.1093/nar/gni178

[33]

Enright A, John B, Gaul U, et al. MicroRNA targets in Drosophila. Genome Biol. 2003;5(1):R1. doi: 10.1186/gb-2003-5-1-r1

[34]

Enright A., John B., Gaul U., et al. MicroRNA targets in Drosophila // Genome Biol. 2003. Vol. 5, N 1. C. R1. doi: 10.1186/gb-2003-5-1-r1

[35]

Tang D, Chen M, Huang X, et al. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. doi: 10.1371/journal.pone.0294236

[36]

Tang D., Chen M., Huang X., et al. SRplot: A free online platform for data visualization and graphing // PLoS One. 2023. Vol. 18, N 11. P. e0294236. doi: 10.1371/journal.pone.0294236

[37]

Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020;48(D1):D127–D31. doi: 10.1093/nar/gkz757

[38]

Chen Y., Wang X. miRDB: an online database for prediction of functional microRNA targets // Nucleic Acids Res. 2020. Vol. 48, N D1. P. D127–D131. doi: 10.1093/nar/gkz757

[39]

Liu W, Wang X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 2019;20(1):18. doi: 10.1186/s13059-019-1629-z

[40]

Liu W., Wang X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data // Genome Biol. 2019. Vol. 20, N 1. P. 18. doi: 10.1186/s13059-019-1629-z

[41]

Lewis B, Burge C, Bartel D. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120(1):15–20. doi: 10.1016/j.cell.2004.12.035

[42]

Lewis B., Burge C., Bartel D. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets // Cell. 2005. Vol. 120, N 1. P. 15–20. doi: 10.1016/j.cell.2004.12.035

[43]

Kanellos I, Vergoulis T, Sacharidis D, et al. MR-microT: a MapReduce-based MicroRNA target prediction method. Proceedings of the 26th International Conference on Scientific and Statistical Database Management; 2014. doi: 10.1145/2618243.2618289

[44]

Kanellos I., Vergoulis T., Sacharidis D., et al. MR-microT: a MapReduce-based MicroRNA target prediction method // Proceedings of the 26th International Conference on Scientific and Statistical Database Management, 2014. P. 1–4. doi: 10.1145/2618243.2618289

[45]

Reczko M, Maragkakis M, Alexiou P, et al. Functional microRNA targets in protein coding sequences. Bioinformatics. 2012;28(6):771–776. doi: 10.1093/bioinformatics/bts043

[46]

Reczko M., Maragkakis M., Alexiou P., et al. Functional microRNA targets in protein coding sequences // Bioinformatics. 2012. Vol. 28, N 6. C. 771–776. doi: 10.1093/bioinformatics/bts043

[47]

Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6

[48]

Zhou Y., Zhou B., Pache L., et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets // Nat Commun. 2019. Vol. 10, N 1. P. 1523. doi: 10.1038/s41467-019-09234-6

[49]

Rao X, Huang X, Zhou Z, Lin X. An improvement of the 2^(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. Biostat Bioinforma Biomath. 2013;3(3):71–85.

[50]

Rao X., Huang X., Zhou Z., Lin X. An improvement of the 2^(-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis // Biostat Bioinforma Biomath. 2013. Vol. 3, N 3. P. 71–85.

[51]

López-Ratón M, Rodríguez-Álvarez M, Cadarso-Suárez C, Gude-Sampedro F. OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests. Journal of statistical software. 2014;61:1–36. doi: 10.18637/jss.v061.i08

[52]

Lopez-Raton M., Rodriguez-Álvarez M., Cadarso-Suarez C., Gude-Sampedro F. OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests // Journal of statistical software. 2014. Vol. 61. P. 1–36. doi: 10.18637/jss.v061.i08

[53]

Fobian S, Mei X, Crezee J, et al. Increased human papillomavirus viral load is correlated to higher severity of cervical disease and poorer clinical outcome: A systematic review. J Med Virol. 2024;96(6):e29741. doi: 10.1002/jmv.29741

[54]

Fobian S., Mei X., Crezee J., et al. Increased human papillomavirus viral load is correlated to higher severity of cervical disease and poorer clinical outcome: A systematic review // J Med Virol. 2024. Vol. 96, N 6. P. e29741. doi: 10.1002/jmv.29741

[55]

Zhou Y, Shi X, Liu J, Zhang L. Correlation between human papillomavirus viral load and cervical lesions classification: A review of current research. Front Med (Lausanne). 2023;10:1111269. doi: 10.3389/fmed.2023.1111269

[56]

Zhou Y., Shi X., Liu J., Zhang L. Correlation between human papillomavirus viral load and cervical lesions classification: A review of current research // Front Med (Lausanne). 2023. Vol. 10. P. 1111269. doi: 10.3389/fmed.2023.1111269

[57]

Baron C, Henry M, Tamalet C, et al. Relationship between HPV 16, 18, 31, 33, 45 DNA detection and quantitation and E6/E7 mRNA detection among a series of cervical specimens with various degrees of histological lesions. J Med Virol. 2015;87(8):1389–1396. doi: 10.1002/jmv.24157

[58]

Baron C., Henry M., Tamalet C., et al. Relationship between HPV 16, 18, 31, 33, 45 DNA detection and quantitation and E6/E7 mRNA detection among a series of cervical specimens with various degrees of histological lesions // J Med Virol. 2015. Vol. 87, N 8. P. 1389–1396. doi: 10.1002/jmv.24157

[59]

Camus C, Vitale S, Loubatier C, et al. Quantification of HPV16 E6/E7 mRNA Spliced Isoforms Viral Load as a Novel Diagnostic Tool for Improving Cervical Cancer Screening. J Clin Med. 2018;7(12):530. doi: 10.3390/jcm7120530

[60]

Camus C., Vitale S., Loubatier C., et al. Quantification of HPV16 E6/E7 mRNA Spliced Isoforms Viral Load as a Novel Diagnostic Tool for Improving Cervical Cancer Screening // J Clin Med. 2018. Vol. 7, N 12. P. 530. doi: 10.3390/jcm7120530

[61]

Lin X, Liang D, He Z, et al. miR-K12-7-5p encoded by Kaposi’s sarcoma-associated herpesvirus stabilizes the latent state by targeting viral ORF50/RTA. PLoS One. 2011;6(1):e16224. doi: 10.1371/journal.pone.0016224

[62]

Lin X., Liang D., He Z., et al. miR-K12-7-5p encoded by Kaposi’s sarcoma-associated herpesvirus stabilizes the latent state by targeting viral ORF50/RTA // PLoS One. 2011. Vol. 6, N 1. P. e16224. doi: 10.1371/journal.pone.0016224

[63]

Seo G, Chen C, Sullivan C. Merkel cell polyomavirus encodes a microRNA with the ability to autoregulate viral gene expression. Virology. 2009;383(2):183–187. doi: 10.1016/j.virol.2008.11.001

[64]

Seo G., Chen C., Sullivan C. Merkel cell polyomavirus encodes a microRNA with the ability to autoregulate viral gene expression // Virology. 2009. Vol. 383, N 2. P. 183–187. doi: 10.1016/j.virol.2008.11.001

[65]

Theiss J, Gunther T, Alawi M, et al. A Comprehensive Analysis of Replicating Merkel Cell Polyomavirus Genomes Delineates the Viral Transcription Program and Suggests a Role for mcv-miR-M1 in Episomal Persistence. PLoS Pathog. 2015;11(7):e1004974. doi: 10.1371/journal.ppat.1004974

[66]

Theiss J., Gunther T., Alawi M., et al. A Comprehensive Analysis of Replicating Merkel Cell Polyomavirus Genomes Delineates the Viral Transcription Program and Suggests a Role for mcv-miR-M1 in Episomal Persistence // PLoS Pathog. 2015. Vol. 11, N 7. P. e1004974. doi: 10.1371/journal.ppat.1004974

[67]

Zhang J, Pu X, Xiong Y. kshv-mir-k12-1-5p promotes cell growth and metastasis by targeting SOCS6 in Kaposi’s sarcoma cells. Cancer Manag Res. 2019;11:4985–4995. doi: 10.2147/CMAR.S198411

[68]

Zhang J., Pu X., Xiong Y. kshv-mir-k12-1-5p promotes cell growth and metastasis by targeting SOCS6 in Kaposi’s sarcoma cells // Cancer Manag Res. 2019. Vol. 11. P. 4985–4995. doi: 10.2147/CMAR.S198411

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