Potential biomarker detection for liver cancer stem cell by machine learning approach


  • Ali Farzane Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
  • Maryam Akbarzadeh Department of Biochemistry, Erasmus University Medical Center, Rotterdam, Netherlands.
  • Reza Ferdousi Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Daneshgah St, Tabriz, Iran.
  • Mohammadreza Rashidi Stem Cell and Regenerative Medicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Reza Safdari Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.




Liver cancer, Cancer stem cell, Machine learning, biomarker, Gene expression


Objectives: In this study, we aimed to identify putative biomarkers for identification and characterization of these cells in liver cancer.

Methods: We employed a supervised machine learning method, XGBoost, to data from 13 GEO data series to classify samples using gene expression data.

Results.  Across the 376 samples (129 CSCs and 247 non-CSCs cases), XGBoost displayed high performance in the classification of data. XGBoost feature importance scores and SHAP (Shapley Additive explanation) values were used for the interpretation of results and analysis of individual gene importance. We confirmed that expression levels of a 10-gene set (PTGER3, AURKB, C15orf40, IDI2, OR8D1, NACA2, SERPINB6, L1CAM, SMC1A, and RASGRF1) were predictive. The results showed that these 10 genes can detect CSCs robustly with accuracy, sensitivity, and specificity of 97 %, 100 %, and 95 %, respectively.

Conclusions. We suggest that the ten-gene set may be used as a biomarker set for detecting and characterizing CSCs using gene expression data.


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How to Cite

Farzane, A., Akbarzadeh, M., Ferdousi, R., Rashidi, M., & Safdari, R. (2020). Potential biomarker detection for liver cancer stem cell by machine learning approach. Journal of Contemporary Medical Sciences, 6(6), 306–312. https://doi.org/10.22317/jcms.v6i6.898