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Laryngeal cancer diagnosis via miRNA-based decision tree model

  • Laryngology
  • Published:
European Archives of Oto-Rhino-Laryngology Aims and scope Submit manuscript

Abstract

Purpose

Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers.

Methods

In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs.

Results

Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in our model to understand their relationship with cancer proliferation or suppression pathways.

Conclusion

Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as an additional method for diagnosing laryngeal cancer.

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Data availability

All code used for this study is available upon request. All other data are publicly accessible.

References

  1. Koroulakis A, Agarwal M (2022) Laryngeal cancer. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023. https://www.ncbi.nlm.nih.gov/books/NBK526076/. Accessed 23 Jul 2023

  2. Afify AY, Ashry MH, Sadeq MA, Elsaid M (2023) Causes of death after laryngeal cancer diagnosis: a US population-based study. Eur Arch Otorhinolaryngol 280(4):1855–1864

    Article  PubMed  Google Scholar 

  3. National Cancer Institute (2023) Laryngeal Cancer Treatment (PDQ®)–Patient Version. https://www.cancer.gov/types/head-and-neck/patient/adult/laryngeal-treatment-pdq. Accessed 16 Jul 2023

  4. Jovanović MB (2008) Diagnosis of laryngeal carcinoma. Med Pregl 61(11–12):591–595

    Article  PubMed  Google Scholar 

  5. Cohen SM, Kim J, Roy N, Asche C, Courey M (2012) Direct health care costs of laryngeal diseases and disorders. Laryngoscope 122(7):1582–1588

    Article  PubMed  Google Scholar 

  6. Esquela-Kerscher A, Slack FJ (2006) Oncomirs—micrornas with a role in cancer. Nat Rev Cancer 6(4):259–269

    Article  CAS  PubMed  Google Scholar 

  7. Widera C, Gupta SK, Lorenzen JM, Bang C, Bauersachs J, Bethmann K, Kempf T, Wollert KC, Thum T (2011) Diagnostic and prognostic impact of six circulating micrornas in acute coronary syndrome. J Mol Cell Cardiol 51(5):872–875

    Article  CAS  PubMed  Google Scholar 

  8. Hegazy M, Elkady MA, Yehia AM, Elsakka EG, Abulsoud AI, Abdelmak-soud NM, Elshafei A, Abdelghany TM, Elkhawaga SY, Ismail A et al (2023) The role of mirnas in laryngeal cancer pathogenesis and therapeutic resistance-a focus on signaling pathways interplay. Pathol-Res Pract 246:154510

    Article  CAS  PubMed  Google Scholar 

  9. Soifer HS, Rossi JJ, Sætrom P (2007) Micrornas in disease and potential therapeutic applications. Mol Ther 15(12):2070–2079

    Article  CAS  PubMed  Google Scholar 

  10. Gayosso-Gomez LV, Ortiz-Quintero B (2021) Circulating micrornas in blood and other body fluids as biomarkers for diagnosis, prognosis, and therapy response in lung cancer. Diagnostics 11(3):421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Iorio MV, Croce CM (2012) Microrna dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4(3):143–159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Izumchenko E, Chang X, Michailidi C, Kagohara L, Ravi R, Paz K, Brait M, Hoque MO, Ling S, Bedi A et al (2014) The tgfβ–mir200–mig6 pathway orchestrates the emt-associated kinase switch that induces resistance to egfr inhibitors. Can Res 74(14):3995–4005

    Article  CAS  Google Scholar 

  13. Li L, Hu X, Yang Z, Jia Z, Fang M, Zhang L, Zhou Y et al (2014) Establishing reliable mirna-cancer association network based on text-mining method. Comput Math Methods Med 2014:1–8

    ADS  Google Scholar 

  14. Jin D, Lee H (2015) A computational approach to identifying gene-microrna modules in cancer. PLoS Comput Biol 11(1):1004042

    Article  ADS  Google Scholar 

  15. Sultan A, Asa AA, Guimbangunan TM, Serapio ED, Fellizar A, Albano PM, Tomas RC (2023) Machine learning-based prediction of the likelihood of colorectal cancer using mirna expression. Philipp J Sci 152(4):1413–1432

    Article  Google Scholar 

  16. Aicha AB (2020) Conventional machine learning techniques with features engineering for preventive larynx cancer detection. In: 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp 1–5. IEEE

  17. Li Z, Li Z, Chen Q, Zhang J, Dunham ME, McWhorter AJ, Feng JM, Li Y, Yao S, Xu J (2022) Machine-learning-assisted spontaneous raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer. Comput Biol Med 146:105617

    Article  CAS  PubMed  Google Scholar 

  18. Singh VP, Maurya AK (2021) Role of machine learning and texture features for the diagnosis of laryngeal cancer. Machine learning for healthcare applications. Wiley, pp 353–367

    Chapter  Google Scholar 

  19. Ayaz L et al (2013) Differential expression of micrornas in plasma of patients with laryngeal squamous cell carcinoma: potential early-detection markers for laryngeal squamous cell carcinoma. J Cancer Res Clin Oncol 139:1499–1506

    Article  CAS  PubMed  Google Scholar 

  20. Cao P, Zhou L, Zhang J, Zheng F, Wang H, Ma D, Tian J (2013) Comprehensive expression profiling of micrornas in laryngeal squamous cell carcinoma. Head Neck 35(5):720–728

    Article  PubMed  Google Scholar 

  21. Chen L et al (2020) Upregulation of microrna-141 suppresses epithelial-mesenchymal transition and lymph node metastasis in laryngeal cancer through hoxc6-dependent tgf-β signaling pathway. Cell Signal 66:109444

    Article  CAS  PubMed  Google Scholar 

  22. Guo L, Cai X, Hu W, Hua W, Yan W, Lin Y, Yin S, Chen Y (2019) Expression and clinical significance of mirna-145 and mirna-218 in laryngeal cancer. Oncol Lett 18(1):764–770

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hu Y, Liu H (2015) Microrna-10a-5p and microrna-34c-5p in laryngeal epithelial premalignant lesions: differential expression and clinicopathological correlation. Eur Arch Otorhinolaryngol 272:391–399

    Article  PubMed  Google Scholar 

  24. Huang Y, Gu M, Tang Y, Sun Z, Luo J, Li Z (2021) Systematic review and meta-analysis of prognostic microrna biomarkers for survival outcome in laryngeal squamous cell cancer. Cancer Cell Int 21(1):1–14

    Article  Google Scholar 

  25. Li P, Liu H, Wang Z, He F, Wang H, Shi Z, Yang A, Ye J (2016) Micrornas in laryngeal cancer: implications for diagnosis, prognosis and therapy. Am J Transl Res 8(5):1935

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Takeuchi T, Kawasaki H, Luce A, Cossu AM, Misso G, Scrima M, Bocchetti M, Ricciardiello F, Caraglia M, Zappavigna S (2020) Insight toward the microrna profiling of laryngeal cancers: biological role and clinical impact. Int J Mol Sci 21(10):3693

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) mirbase: from microrna sequences to function. Nucleic Acids Res 47(D1):155–162

    Article  Google Scholar 

  28. Kang W, Kouznetsova VL, Tsigelny IF (2022) mirna in machine-learning-based diagnostics of cancers. Cancer Screen Prev 1(1):32–38

    Article  Google Scholar 

  29. MacFarlane L-A, Murphy RP (2010) Microrna: biogenesis, function and role in cancer. Curr Genomics 11(7):537–561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Peng Y, Croce CM (2016) The role of micrornas in human cancer. Signal Transduct Target Ther 1(1):1–9

    Google Scholar 

  31. Chen Y, Wang X (2020) mirdb: an online database for prediction of functional microrna targets. Nucleic Acids Res 48(D1):127–131

    Article  CAS  Google Scholar 

  32. Liu W, Wang X (2019) Prediction of functional microrna targets by integrative modeling of microrna binding and target expression data. Genome Biol 20:1–10

    Article  Google Scholar 

  33. Reddy KB (2015) Microrna (mirna) in cancer. Cancer Cell Int 15(1):1–6

    Article  ADS  MathSciNet  Google Scholar 

  34. Li Z et al (2023) Ncpath: a novel platform for visualization and enrichment analysis of human non-coding rna and kegg signaling pathways. Bioinformatics 39(1):812

    Article  Google Scholar 

  35. Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with java implementations. ACM SIGMOD Rec 31(1):76–77

    Article  Google Scholar 

  36. Kanehisa M (2019) Toward understanding the origin and evolution of cellular organisms. Protein Sci 28(11):1947–1951

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M (2021) Kegg: integrating viruses and cellular organisms. Nucleic Acids Res 49(D1):545–551

    Article  Google Scholar 

  38. Kanehisa M, Goto S (2000) Kegg: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113–1120

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hu K, Lin R, Zhang Z, Chen H, Rao X (2020) Impact of prior cancer history on the survival of patients with larynx cancer. BMC Cancer 20:1–11

    Google Scholar 

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Acknowledgements

The results published here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga

Funding

None.

Author information

Authors and Affiliations

  1. REHS Program, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA

    Aarav Arora

  2. San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA

    Igor F. Tsigelny & Valentina L. Kouznetsova

  3. BiAna, La Jolla, CA, USA

    Igor F. Tsigelny & Valentina L. Kouznetsova

  4. Department of Neurosciences, UC San Diego, La Jolla, CA, USA

    Igor F. Tsigelny

  5. CureScience, San Diego, CA, USA

    Igor F. Tsigelny & Valentina L. Kouznetsova

Authors
  1. Aarav Arora
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  2. Igor F. Tsigelny
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  3. Valentina L. Kouznetsova
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Contributions

VK and IFT proposed the development of a machine-learning system that uses miRNAs, gene targets, and miRNA pathway processes to diagnose cancer. AA found and processed the data, trained the model, and wrote the article. VK and IFT assisted with development of the machine-learning system and edited the article.

Corresponding author

Correspondence to Igor F. Tsigelny.

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Arora, A., Tsigelny, I.F. & Kouznetsova, V.L. Laryngeal cancer diagnosis via miRNA-based decision tree model. Eur Arch Otorhinolaryngol 281, 1391–1399 (2024). https://doi.org/10.1007/s00405-023-08383-1

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  • DOI: https://doi.org/10.1007/s00405-023-08383-1

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