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.
