Distinct cell subtype composition using gene expression data in oral cancer

유전자 발현 데이터 기반 구강암에서의 세포 조성 차이 분석

Rhee, Je-Keun

  • 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