RÉSUMÉ
In order to ascertain the relationship between gene expression and colon cancer localization, a classification method based on random gene selection and a self-organizing map network is proposed. Different numbers of genes were selected randomly from 54,675 genes of 53 colon cancer patients in stage union for international cancer control II. These patients were then divided into two sets: a training set of 36 and a validation set of 17 patients. In this study, we randomly selected 1000, 100, 50, 30, 10, 5, and 3 genes, 1000 times, respectively. The minimum misclassification ratio of each gene group was 3/17 to 4/17, and the percentage of gene groups that were less than 0.25 was approximately 1-7%. Moreover, the misclassification ratio of most gene groups (about 82-89%) was lower than 0.4. Through the analysis of these low misclassification ratio gene groups, we found that there were few common genes between them. This revealed that colon cancer localization is not associated with a single gene group but with many gene groups. Furthermore, K-fold cross validation was used to test the reliability of the possible informative genes, and the results indicated that using gene expression to classify colon tumor localization was not feasible.
Sujet(s)
Tumeurs du côlon/classification , Tumeurs du côlon/génétique , Algorithmes , Marqueurs biologiques tumoraux/biosynthèse , Marqueurs biologiques tumoraux/génétique , Tumeurs du côlon/métabolisme , Biologie informatique/méthodes , Bases de données génétiques , Analyse de profil d'expression de gènes/méthodes , Régulation de l'expression des gènes tumoraux , Humains , Modèles génétiques , Séquençage par oligonucléotides en batterie , Pronostic , Reproductibilité des résultatsRÉSUMÉ
Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.