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• W2729696303 abstract "B21 Genomic heterogeneity is a characteristic feature of nearly all solid tumors, appearing early in tumor progression. Over time, early genomic instability evolutionarily leads to a molecularly heterogeneous population of cells naturally selected for their abilities to proliferate and invade, while simultaneously evading host defenses. The selection process, influenced by the unique to the tumor host environment, results in a further diverse intrapatient tumor population which is responsible for the clinical heterogeneity of the disease. We propose that encompassing of this heterogeneity in the data analysis will aid in capturing the diversity within the cancer subgroups and in the identification of subgroup specific gene expression profiles.
 We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is traditional utilized in analysis of temporal patterns. It is uniquely 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. These genes can then be used to classify an unknown biopsy into one of these groups based on the calculated entropy of the sample.
 Methods: RNA extracted from cervical biopsies was used for analysis by cDNA microarrays. The gene expression patterns generated were analyzed using Approximate Sample Entropy Analysis (ApSE). Briefly, we will establish a normal baseline of unchanging genes in the normal cervical biopsies. This set of genes will be further limited to those which display the maximum deviation from the normal baseline and the greatest probability to differentiate between normal cervix and invasive carcinoma as calculated by approximate sample entropy. Ten genes, from the best two subsets were chosen for quantitative real-time validation of their expression patterns and the subsets classification potential.
 Results: We have identified 208 possible genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the progression of genetic heterogeneity in malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of the genes of interest on 10 Normal and 20 independent cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 11 genes of interest is promising and anticipate that expanding array test matrix will provide the calculation with more statistical power.
 Conclusions: 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" @default.
• W2729696303 created "2017-07-14" @default.
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• W2729696303 date "2008-08-01" @default.
• W2729696303 modified "2023-09-23" @default.
• W2729696303 title "Approximate sample entropy analysis: A novel approach for the development of cervical neoplasia gene-expression signatures." @default.
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