Identification of biomarkers for esophageal squamous cell carcinoma using feature selection and decision tree methods

Chun Wei Tung, Ming Tsang Wu, Yu Kuei Chen, Chun Chieh Wu, Wei Chung Chen, Hsien Pin Li, Shah Hwa Chou, Deng Chyang Wu, I. Chen Wu

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Esophageal squamous cell cancer (ESCC) is one of the most common fatal human cancers. The identification of biomarkers for early detection could be a promising strategy to decrease mortality. Previous studies utilized microarray techniques to identify more than one hundred genes; however, it is desirable to identify a small set of biomarkers for clinical use. This study proposes a sequential forward feature selection algorithm to design decision tree models for discriminating ESCC from normal tissues. Two potential biomarkers of RUVBL1 and CNIH were identified and validated based on two public available microarray datasets. To test the discrimination ability of the two biomarkers, 17 pairs of expression profiles of ESCC and normal tissues from Taiwanese male patients were measured by using microarray techniques. The classification accuracies of the two biomarkers in all three datasets were higher than 90%. Interpretable decision tree models were constructed to analyze expression patterns of the two biomarkers. RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas.

Original languageEnglish
Article number782031
JournalThe Scientific World Journal
Publication statusPublished - Dec 1 2013
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Environmental Science


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