A robust, reliable, quick and cost effective gene expression analyzing Title Loaded From File method which can be suitable for daily diagnostic utilization in the future. Traditional histology may suffer from sampling bias due to biopsy orientation problems, therefore, critical areas including aberrant crypt foci, dysplastic areas or in situ carcinoma may remain hidden. Molecular based discrimination using mRNA expression can represent the whole sample to avoid this bias and support pathologists in coping with their growing workload of early cancer screening. Furthermore, mRNA expression can reveal functional information beyond microscopy related to the biological behavior, tumor invasion, metastasic spread and therapeutic target expression in colorectal cancer. In this study, we applied whole genomic microarray analysis in order to identify gene expression profile alterations focusing on the dysplastic adenoma-carcinoma transition. Our aims were to identify characteristic transcript sets in order to develop diagnostic mRNA expression patterns for objective classification of benign and malignant colorectal Title Loaded From File diseases and to test the classificatory power of these markers on an independent sample set.6000 Pico Kit (Agilent Inc, Santa Clara, US). Biotinylated cRNA probes were synthesized from 4,8260,60 mg total RNA and fragmented using the One-Cycle Target Labeling and Control Kit (http://www.affymetrix.com/support/downloads/manuals/ expression_analysis_technical_manual.pdf) according to the Affymetrix description. Ten mg of each fragmented cRNA sample were hybridized into HGU133 Plus2.0 array (Affymetrix) at 45uC for 16 hours. The slides were washed and stained using Fluidics Station 450 and an antibody amplification staining method according to the manufacturer’s instructions. The fluorescent signals were detected by a GeneChip Scanner 3000.Statistical evaluation of mRNA expression profilesQuality 1531364 control analyses were performed according to the suggestions of the Tumour Analysis Best Practices Working Group [16]. Scanned images were inspected for artifacts, percentage of present calls (.25 ) and control of the RNA degradation were evaluated. Based on the evaluation criteria all biopsy measurements fulfilled the minimal quality requirements. The Affymetrix expression arrays were pre-processed by gcRMA with quantile normalization and median polish summarization. The datasets are available in the Gene Expression Omnibus databank for further analysis (http://www.ncbi.nlm.nih.gov/geo/), series accession numbers: GSE4183, GSE10714). Differentially expressed genes were identified by Significance Analysis of microarrays (SAM) method between different diagnostic groups. The nearest shrunken centroid method (Prediction Analysis for miroarrays ?PAM) was applied for sample classification from gene expression data. The pre-processing, data mining and statistical steps were performed using R-environment with Bioconductor libraries. Hierarchical cluster analysis represents on each comparisons of correlation. Logistic regression was applied to analyze dependence of binary diagnostic variables (represented 0 as control, 1 as disease). Discriminant and principal component analysis were also performed. In the discriminant analysis, leave-one out classification was applied for crossvalidation.Materials and Methods Patients and samplesAfter informed consent of untreated patients, colon biopsy samples were taken during endoscopic intervention and stored in RNALater Reagent (Qiagen I.A robust, reliable, quick and cost effective gene expression analyzing method which can be suitable for daily diagnostic utilization in the future. Traditional histology may suffer from sampling bias due to biopsy orientation problems, therefore, critical areas including aberrant crypt foci, dysplastic areas or in situ carcinoma may remain hidden. Molecular based discrimination using mRNA expression can represent the whole sample to avoid this bias and support pathologists in coping with their growing workload of early cancer screening. Furthermore, mRNA expression can reveal functional information beyond microscopy related to the biological behavior, tumor invasion, metastasic spread and therapeutic target expression in colorectal cancer. In this study, we applied whole genomic microarray analysis in order to identify gene expression profile alterations focusing on the dysplastic adenoma-carcinoma transition. Our aims were to identify characteristic transcript sets in order to develop diagnostic mRNA expression patterns for objective classification of benign and malignant colorectal diseases and to test the classificatory power of these markers on an independent sample set.6000 Pico Kit (Agilent Inc, Santa Clara, US). Biotinylated cRNA probes were synthesized from 4,8260,60 mg total RNA and fragmented using the One-Cycle Target Labeling and Control Kit (http://www.affymetrix.com/support/downloads/manuals/ expression_analysis_technical_manual.pdf) according to the Affymetrix description. Ten mg of each fragmented cRNA sample were hybridized into HGU133 Plus2.0 array (Affymetrix) at 45uC for 16 hours. The slides were washed and stained using Fluidics Station 450 and an antibody amplification staining method according to the manufacturer’s instructions. The fluorescent signals were detected by a GeneChip Scanner 3000.Statistical evaluation of mRNA expression profilesQuality 1531364 control analyses were performed according to the suggestions of the Tumour Analysis Best Practices Working Group [16]. Scanned images were inspected for artifacts, percentage of present calls (.25 ) and control of the RNA degradation were evaluated. Based on the evaluation criteria all biopsy measurements fulfilled the minimal quality requirements. The Affymetrix expression arrays were pre-processed by gcRMA with quantile normalization and median polish summarization. The datasets are available in the Gene Expression Omnibus databank for further analysis (http://www.ncbi.nlm.nih.gov/geo/), series accession numbers: GSE4183, GSE10714). Differentially expressed genes were identified by Significance Analysis of microarrays (SAM) method between different diagnostic groups. The nearest shrunken centroid method (Prediction Analysis for miroarrays ?PAM) was applied for sample classification from gene expression data. The pre-processing, data mining and statistical steps were performed using R-environment with Bioconductor libraries. Hierarchical cluster analysis represents on each comparisons of correlation. Logistic regression was applied to analyze dependence of binary diagnostic variables (represented 0 as control, 1 as disease). Discriminant and principal component analysis were also performed. In the discriminant analysis, leave-one out classification was applied for crossvalidation.Materials and Methods Patients and samplesAfter informed consent of untreated patients, colon biopsy samples were taken during endoscopic intervention and stored in RNALater Reagent (Qiagen I.
