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Gene signature for relapse prediction in Dukes´B colon cancer

ABSTRACT: Purpose: The benefit of postoperative adjuvant chemotherapy in patients with Dukes´ B colorectal cancer is still uncertain and its routine use is not recommended. The five years survival rate is approximately 75% and identification of the patients at high risk of recurrence would represent an important strategy for the use of adjuvant chemotherapy. In this study we identify new prognostic markers for tumor relapse in Dukes´ B colon cancer patients. Patients and Methods: We retrospectively analyzed gene expression profiles in frozen tumor specimens from 16 patients with Dukes´ B colorectal cancer by using high density oligonucleotide microarrays. Data were normalized and subsequently analyzed with two different statistical procedures. The intensity value associated to each spot is the result of subtracting a gaussian function of the noise from the foreground values {Kooperberg, 2002}. After this background subtraction, base 2 logarithms of all data were calculated and genes with more than two missing values were excluded from the analysis. The remaining missing values were replaced by using the KNN imputation method {Troyanskaya, 2001}. The great number of genes present in the microarray allowed us to consider that the overall fluorescence intensity must be the same for all slides, as the vast majority of genes will not change their expression between the two conditions tested. So we used the quantile normalization method {Bolstad, 2003}, which equalizes not only the average intensity but also the range of intensity values between slides. We decided to use two different statistical procedures in the search of significant differences in gene expression between the relapsed and non-relapsed patients. Only the genes selected by both statistical procedures were considered as differentially expressed between relapsed and non-relapsed patients. The first test was a permutation t-test for comparison of two means {Dudoit, 2003} and the second one a variation of the Fisher test based on the work presented by Iizuka et al. {Iizuka, 2003} in which they searched for the optimal number of genes that could differentiate between two groups of samples. Briefly, we used the same algorithms for the calculation of the Fisher criterion, but instead of looking for the optimal subset of genes able to differentiate between groups, what requires a lot of computing capacity, we tried different numbers of candidate genes and selected a number which yielded very good classification results when evaluated by means of Fast ICA {Lee, 2003} and Hierarchical Clustering {Everitt, 1974}. The procedure consisted of selecting the 30 genes with the highest Fisher criterion value in 6 rounds of iteration, each round leaving one sample of the relapsed group and two of the non-relapsed group out of the calculations (a variant of the “leave one out” method). We selected the genes present in at least 3 of the iterations. The intersection of the groups of genes selected by the two statistical procedures was selected as a prognostic signature for relapse in Dukes´stage B colon cancer. Results: Our results show a group of 48 genes differentially expressed between the relapsed and non-relapsed groups with an associated probability below 0,001 in the t test. Another statistical procedure based on the Fisher criterion resulted in 11 genes able to separate both groups. In order to minimize false positives we only considered a good gene signature the 8 genes selected by both statistical procedures. These genes are ribosomal protein S5, chromodomain helicase DNA binding protein 2, lysosomal ATPase V0 subunit A isoform 1, zinc finger protein 148, brain protein I3 (BRI3), hypothetical protein MGC23401 and one unknown clone. In order to verify the obtained results, the differential expression of the first two genes was confirmed by real time PCR. Keywords: repeat sample

ORGANISM(S): Homo sapiens

PROVIDER: GSE2630 | GEO | 2005/05/07

SECONDARY ACCESSION(S): PRJNA92267

REPOSITORIES: GEO

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Project description:This a model from the article: Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways. Part II--kinetic analysis. Martins SI, Van Boekel MA. Carbohydr Res2003 Jul;338(16):1665-78. 12873422, Abstract: A kinetic model for N-(1-deoxy-Image -fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 °C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. The determinant criterion was used as the statistical fit criterion. Model discrimination was performed by both chemical insight and statistical tests (Posterior Probability and Akaike criterion). Kinetic analysis showed that at lower pH DFG 1,2-enolization is favoured whereas with increasing pH 2,3-enolization becomes a more relevant degradation pathway. The lower amount observed of 1-DG is related with its high reactivity. It was shown that acetic acid, a main degradation product from DFG, was mainly formed through 1-DG degradation. Also from the estimated parameters 3-DG was found to be the main precursor in carbohydrate fragments formation, responsible for colour formation. Some indication was given that as the reaction proceeded other compounds besides DFG become reactants themselves with the formation among others of methylglyoxal. The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism. Model was intially tested in Jarnac. The model was recently updated on 9th July 2010. The reference publication has reported two models M1 and M2, where the parameter values are given for conditions A) 100oC, pH5.5, B) 120oC, pH5.5, C) 100oC, pH6.8 and D) 120oC, pH6.8. This model corresponds to the model M2 with condition 100oC, pH6.8 The model reproduces Figure 6 of the reference publication. The curation figure was recently added This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:Two independent chemostat cultivations were performed for both wild type and trxB1 over-expression strain, NZ7602. Three hybridization experiments, all with the same hybridization scheme (see overall design), were performed with samples obtained from these chemostats before and after treatment with hydrogen peroxide (30 minutes 2.5 mM peroxide). Two of these experiments used samples obtained from the same fermentation. In each experiment three arrays were used. Per array two cDNA labeled targets were hybridized on custom designed L. plantarum WCFS1 11K Agilent oligo microarrays (GEO Acc. Nr. GPL4318) using the Agilent 60-mer oligo microarray processing protocol version 4.1. These microarrays contained an average of 3 probes per gene. Dried slides were scanned in the Scan Array Express (PerkinElmer Life Sciences; Packard Bioscience) at 10 microns. Spot intensity data was quantified (average intensity) in ImaGene version 5.0 (BioDiscovery, Inc., El Segundo, CA). Signal intensities of all probes were corrected against background and normalized by fitting a plot of M (= 2log [cy5 intensity/cy3 intensity]) against A (= 0.5 • 2log [cy5 intensity • cy3 intensity]) using the lowess algorithm in BASE [32]. The fold change (FC) is defined as 2M. For the statistical analysis we used microarray analysis of variation (R/maanova) [33] In this maanova test we used three variables: fermentation, treatment, and genotype. We tested the model taking into consideration the interaction between genotype and treatment. Significantly regulated genes were defined as genes whose nominal adjusted pvalues were less than 10% and a FC>1.5. The maanova test resulted only in two sets of interesting data because the interaction effect did not reveal significant changes in transcript levels. One dataset representing the transcripts affected as a result of the overproduction of TR and another set representing the transcripts affected due to oxidative stress. These two datasets were denominated genotype and hydrogen peroxide datasets respectively. Keywords: hydrogen peroxide stress response

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Gene signature for relapse prediction in Dukes´B colon cancer

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