Research article

Sample entropy analysis of cervical neoplasia gene-expression signatures

Shaleen K Botting¹ , Jerome P Trzeciakowski³ , Michelle F Benoit^1,2 , Salama A Salama¹ and Concepcion R Diaz-Arrastia¹

¹Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA

²Northwest Medical Specialties, PLLC, Tacoma, Washington, USA

³Systems Biology and Translational Medicine, Texas A&M University Health Science Center, College Station, Texas, USA

author email corresponding author email

BMC Bioinformatics 2009, 10:66doi:10.1186/1471-2105-10-66

Published:	20 February 2009

Abstract

Background

We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set.

Results

In the development of a diagnostic gene-expression profile for cervical intraepithelial neoplasia (CIN) and squamous cell carcinoma of the cervix, we identified 208 genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the genetic instability and heterogeneity of malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of these genes on 10 Normal and 20 cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 10 genes of interest is promising (75% sensitivity, 80% specificity for prediction of cervical cancer over CIN3).

Conclusion

The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise.