The availability of complete human genome sequence and powerful methods for systemic profiling of gene expression, in connection with computational strategies for integrating analyses of large-scale data sets, provide a promising opportunity for elucidating the molecular mechanisms of disease. However, we still do not know what is the best way to study genomic networks. Microarray and mass spectrometry (MS), two different technologies used for the analysis of global gene expression, demonstrate both strengths and limitations. Patterns of transcript levels seem to be consistent among tissue samples of the same origin, and therefore, besides many other potential applications, microarrays are useful for molecular fingerprinting and for classification of various stages of a disease. However, its multivariable data are often noisy, which can result in difficulties of data interpretation, and both array quality and choice of analytical processing methods have a major impact on differential expression analysis of microarray data. Although MS is a powerful tool for rapid protein identification and for analysis of protein modifications, also this method reveals significant limitations in profiling cellular proteomes on a genome-wide scale.

It has been proposed recently that gastroesophageal reflux disease patients may be categorized into three distinct groups exhibiting nonerosive reflux disease, erosive reflux disease, and Barrett's esophagus (BE). Genetic predisposition to the development of BE may result from contributing of low penetrated genetic alterations. Understanding of genetic changes that cause BE would have significant diagnostic, prognostic and therapeutic benefits. To better define the differences between esophageal squamous epithelium and metaplastic columnar-lined esophageal epithelium in BE patients, transcript levels were measured using Affymetrix U133A 2.0 microarray and validated by quantitative RT-real-time PCR, while protein expression was examined by gel-free shotgun liquid chromatography tandem mass spectrometry (LC-MS-MS) runs. The methodological aspects, results of gene expression profiling, and prospects and challenges for using integrative genomics to systematically discover entire gene networks that underlie and modify reflux disease will be discussed.

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