Past Issue

Volume 20, Number 4, Jan-Mar(Winter) 2019, Serial Number: 80 Pages: 569-575

Identification of A Gene Set Associated with Colorectal Cancer in Microarray Data Using The Entropy Method

Fatemeh Bahreini, Ph.D, 1, Ali Reza Soltanian, Ph.D, 2, 3, *,
Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
Modeling of Noncommunicable Diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
*Corresponding Address: P.O.Box: 6517838736 Department of Biostatistics and Epidemiology School of Public Health Hamadan University of Medical Sciences Hamadan Iran



We sought to apply Shannon’s entropy to determine colorectal cancer genes in a microarray dataset.

Materials and Methods

In the retrospective study, 36 samples were analysed, 18 colorectal carcinoma and 18 paired normal tissue samples. After identification of the gene fold-changes, we used the entropy theory to identify an effective gene set. These genes were subsequently categorised into homogenous clusters.


We assessed 36 tissue samples. The entropy theory was used to select a set of 29 genes from 3128 genes that had fold-changes greater than one, which provided the most information on colorectal cancer. This study shows that all genes fall into a cluster, except for the R08183 gene.


This study has identified several genes associated with colon cancer using the entropy method, which were not detected by custom methods. Therefore, we suggest that the entropy theory should be used to identify genes associated with cancers in a microarray dataset.