Gene expression arrays as a tool to unravel mechanisms of normal tissue radiation injury and prediction of response

Over the past 5 years there has been a rapid increase in the use of microarray technology in the field of cancer research. The majority of studies use microarray analysis of tumor biopsies for profiling of molecular characteristics in an attempt to produce robust classifi ers for prognosis. There are now several published gene sets that have been shown to predict for aggressive forms of breast cancer, where patients are most likely to benefit from adjuvant chemotherapy and tumors most likely to develop distant metastases, or be resistant to treatment. The number of publications relating to the use of microarrays for analysis of normal tissue damage, after cancer treatment or genotoxic exposure, is much more limited. A PubMed literature search was conducted using the following keywords and combination of terms: radiation, normal tissue, microarray, gene expression profi ling, prediction. With respect to normal tissue radiation injury, microarrays have been used in three ways: (1) to generate gene signatures to identify sensitive and resistant populations (prognosis); (2) to identify sets of biomarker genes for estimating radiation exposure, either accidental or as a result of terrorist attack (diagnosis); (3) to identify genes and pathways involved in tissue response to injury (mechanistic). In this article we will review all (relevant) papers that covered our literature search criteria on microarray technology as it has been applied to normal tissue radiation biology and discuss how successful this has been in defining predisposition markers for radiation sensitivity or how it has helped us to unravel molecular mechanisms leading to acute and late tissue toxicity. We also discuss some of the problems and limitations in application and interpretation of such data.     

Meta- and reanalysis of gene expression profiles of hot and cold thyroid nodules and papillary thyroid carcinoma for gene groups

CONTEXT: There are an increasing number of studies analyzing gene expression profiles in various benign and malignant thyroid tumors. This creates the opportunity to validate results obtained from one microarray study with those from other data sets. This process requires rigorous methods for accurate comparison. OBJECTIVE: The ability to compare data sets derived from different Affymetrix GeneChip generations and the influence of intra- and interindividual comparisons of gene expression data were evaluated to build multigene classifiers of benign thyroid nodules to verify a previously proposed papillary thyroid carcinoma (PTC) classifier and to look for molecular pathways essential for PTC oncogenesis. METHODS: Gene expression profile data sets from autonomously functioning and cold thyroid nodules and from PTC were analyzed by support vector machines. GenMAPP analysis was used for PTC data analysis to examine the expression patterns of biologically relevant gene sets. RESULTS: Only intraindividual reference samples allowed the identification of subtle changes in the expression patterns of relevant signaling cascades, such as the MAPK pathway in PTC. Using an artificial intelligence approach, the autonomously functioning and cold thyroid nodule multigene classifiers were derived and evaluated by cross-comparisons. CONCLUSION: We recommend defining classifiers within one generation of gene chips and subsequently checking them across different array generations. Using this approach, we have demonstrated the specificity of a previously reported PTC classifier on an independent collection of benign tumors. Moreover, we propose multigene classifiers for different types of benign thyroid nodules.     

Mesenchymal stem cells: the fibroblasts’ new clothes?

Mesenchymal stem cells are adherent stromal cells, initially isolated from the bone marrow, characterized by their ability to differentiate into mesenchymal tissues such as bone, cartilage and fat. They have also been shown to suppress immune responses in vitro. Because of these properties, mesenchymal stem cells have recently received a very high profile. Despite the dramatic benefits reported in early phase clinical trials, their functions remain poorly understood. Particularly, several questions remain concerning the origin of mesenchymal stem cells and their relationship to other stromal cells such as fibroblasts. Whereas clear gene expression signatures are imprinted in stromal cells of different anatomical origins, the anti-proliferative effects of mesenchymal stem cells and fibroblasts and their potential to differentiate appear to be common features between these two cell types. In this review, we summarize recent studies in the context of historical and often neglected stromal cell literature, and present the evidence that mesenchymal stem cells and fibroblasts share much more in common than previously recognized.     

大黄抑制鼠疫耶尔森菌的体外转录组学研究

目的应用鼠疫菌全基因组芯片研究大黄抑制鼠疫菌的分子作用机制。方法液体稀释法测定大黄对鼠疫菌的最低抑菌浓度(MIC),以10倍MIC作用鼠疫菌30min,提取鼠疫菌总RNA,逆转录合成cDNA,荧光素标记,与芯片杂交,扫描仪扫描,应用SAM软件分析结果。结果获得了大黄作用鼠疫菌的表达谱。大黄作用鼠疫菌的明显差异基因为498个,其中上调基因358个,下调140个。结论蛋白质合成基因、细胞膜相关基因、转运结合蛋白基因以及部分热休克基因的改变是大黄抑制鼠疫菌的主要作用机制。     

Molecular Signatures of Lymphoma

Hematologic malignancies have historically been characterized by morphologic, immunophenotypic, molecular, and genetic features. However, morphologically identical tumors can have clearly distinct clinical outcomes, suggesting underlying biological heterogeneity. Recent advances in microarray technology have helped the classification of lymphoid malignancies evolve to a new refined level. In addition to the discovery of new disease subclasses defined by unique molecular profiles, gene expression patterns can be correlated with specific genetic abnormalities and prognoses. Furthermore, the discovery of new disease subtypes has provided further insight into lymphoma biology and pathogenesis. Unique gene signatures can highlight key deregulated pathways that are active in molecular disease categories, and in some cases these findings have elucidated new targets for novel therapeutic approaches. This review summarizes the current status of molecular profiling in non-Hodgkin lymphomas. In this review, we have endeavored to include data from multiple investigator groups and tried to cover the breadth of lymphoid tumors, excluding acute and chronic leukemias.     

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.     

Gene expression profiles at different stages of human esophageal squamous cell carcinoma

AIM: To characterize the gene expression profiles in different stages of carcinogenesis of esophageal epithelium. METHODS: A microarray containing 588 cancer related genes was employed to study the gene expression profile at different stages of esophageal squamous cell carcinoma including basal cell hyperplasia, high-grade dysplasia, carcinoma in situ, early and late cancer. Principle component analysis was performed to search the genes which were important in carcinogenesis. RESULTS: More than 100 genes were up or down regulated in esophageal epithelial cells during the stages of basal cell hyperplasia, high-grade dysplasia, carcinoma in situ, early and late cancer. Principle component analysis identified a set of genes which may play important roles in the tumor development. Comparison of expression profiles between these stages showed that some genes, such as P160ROCK, JNK2, were activated and may play an important role in early stages of carcinogenesis. CONCLUSION: These findings provided an esophageal cancer-specific and stage-specific expression profiles, showing that complex alterations of gene expression underlie the development of malignant phenotype of esophageal cancer cells.