Distinct cell subtype composition using gene expression data in oral cancer

유전자 발현 데이터 기반 구강암에서의 세포 조성 차이 분석

  • Rhee, Je-Keun (Department of Life Science in Dentistry, School of Dentistry, Pusan National University)
  • 이제근 (부산대학교 치의학전문대학원 치의생명과학과)
  • Received : 2019.07.15
  • Accepted : 2019.08.20
  • Published : 2019.08.28


There are various subtypes of cells in cancer tissues, but it is hard to confirm their composition experimentally. Here, we estimated the cell composition of each sample from gene expression data by using statistical machine learning approaches, two different regression models and investigated whether the cell composition was different between cancer and normal tissue. As a result, we found that CD8 T cell and Neutrophil were increased in oral cancer tissues compared to normal tissues. In addition, we applied t-SNE, which is one of the unsupervised learning, to verify whether normal tissue and oral cancer tissue can be clustered by the derived cell composition. Moreover, we showed that it is possible to predict oral cancer and normal tissue by several supervised classification algorithms. The study would help to improve the understanding of the immune cell infiltration at oral cancer.


Oral cancer;Machine learning;Gene expression;Bio-IT convergence;Cell subtype composition;Immune cell infiltration


Supported by : Pusan National University


  1. I.. S. Kim. (2016). Gene Polymorphism of $TNF-{\alpha}$ in Korean Generalized Aggressive Periodontitis, Journal of Digital Convergence, 14(1), 321-326. DOI: 10.14400/JDC.2016.14.1.321
  2. B. Y. Choi & S.-C. Cho. (2017). Screening of Natural Compounds for Cancer Prevention by Cytotoxicities and AP-1 Reporter Gene Activities, Journal of Convergence for Information Technology, 7(6), 89-95. DOI: 10.22156/CS4SMB.2017.7.6.089
  3. M. Dougan, G. Dranoff & S. K. Dougan. (2019). Cancer Immunotherapy: Beyond Checkpoint Blockade. Annual Review of Cancer Biology, 3, 55-75 DOI: 10.1146/annurev-cancerbio-030518-055552
  4. V. Thorsson et al. (2018). The Immune Landscape of Cancer. Immunity, 48(4), 812-830. DOI: 10.1016/j.immuni.2018.03.023
  5. A. M. Newman et. al. (2015). Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nature Methods, 12(5), 453. DOI: 10.1038/nmeth.3337
  6. T. Li et. al. (2017). TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Research, 77(21), e108-e110. DOI: 10.1158/0008-5472.CAN-17-0307
  7. H. Lee, S. H. Chung & E. J. Choi, (2016) A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm, Journal of Digital Convergence, 14(2), 245-258. DOI: 10.14400/JDC.2016.14.2.245
  8. J. K. Lee & H. W. Lee. (2018) Meltdown Threat Dynamic Detection Mechanism using Decision-Tree based Machine Learning Method, Journal of Convergence for Information Technology, 8(6), 209-215. DOI: 10.22156/CS4SMB.2018.8.6.209
  9. C. H. Peng et. al. (2011). A Novel Molecular Signature Identified by Systems Genetics Approach Predicts Prognosis in Oral Squamous Cell Carcinoma. PloS One, 6(8), e23452. DOI: 10.1371/journal.pone.0023452
  10. V. D. L. Maaten. (2014). Accelerating t-SNE using Tree-based Algorithms. The Journal of Machine Learning Research, 15(1), 3221-3245.
  11. A. Durgeau, Y. Virk, S. Corgnac & F. Mami-Chouaib. (2018). Recent Advances in Targeting CD8 T-cell Immunity for More Effective Cancer Immunotherapy. Frontiers in immunology, 9, 14. DOI: 10.3389/fimmu.2018.00014
  12. C. Fu & A. Jiang. (2018). Dendritic Cells and CD8 T Cell Immunity in Tumor Microenvironment. Frontiers in immunology, 9, 3059. DOI: 10.3389/fimmu.2018.03059
  13. S. B. Coffelt, M. D. Wellenstein & K. E. de Visser. (2016). Neutrophils in Cancer: Neutral No More. Nature Reviews Cancer, 16(7), 431. DOI: 10.1038/nrc.2016.52
  14. M. E. Shaul & Z. G. Fridlender. (2019). Tumour-associated Neutrophils in Patients with Cancer. Nature Reviews Clinical Oncology, 1. DOI: 10.1038/s41571-019-0222-4
  15. B. Li, J. S. Liu & X. S. Liu. (2017). Revisit Linear Regression-based Deconvolution Methods for Tumor Gene Expression Data. Genome biology, 18(1), 127. DOI:10.1186/s13059-017-1256-5