Abstract
The fundamental rationale for the use of microarray-based gene expression profiling to characterize biological samples is based in part on the principle that cells, tissues, and perturbations applied to them can be characterized on the basis of their relative expression of genes and transcripts. Different biological states, cell types, and influences can be distinguished based on transcriptional profiles and the change in the relative levels of different genes and gene groups. This genomic expression profile-based discovery of biological states and effector-actions represents an essential element of a systems-based whole-genome approach to characterizing cells and tissues, and differs from the characterization of individual gene expression changes in isolation from one another, and has the potential to increase knowledge in all fields of biomedicine. The past two decades have seen a paradigm shift in which medical genetics has moved from being a tool of the basic investigator to play a role in the mainstream of medical practice. Identification of genetic causal agents of common endocrine disorders, deciphering underlying molecular pathophysiology of known conditions, development of new predictive tests for genetic abnormalities, and applications in the field of therapeutics are some of the implications of this shift. Endocrine systems, in particular, offer tremendous opportunities for the use of genomic analyses to understand physiological and pathological responses and effectors without being biased to a particular gene or set of genes. Therefore, the responses of diverse and potentially diversely affected systems can be broadly evaluated, constrained only by the limitation that there may be either a primary or secondary impact on transcript abundance. This emerging concept—endocrinomics—thus has the potential to significantly impact the field of endocrine research and clinical practice. However, advancements in the field are also limited by problems in collecting comprehensive datasets, the inherent complexity of multiple interacting systems, genetic variations between individuals, and some cumbersomeness associated with expression profiling technology and data analysis itself. This chapter discusses some of the issues to be considered in the design and analysis of microarray experiments for the characterization of endocrine-regulated systems.
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Jegga, A.G., Aronow, B.J., Handwerger, S. (2008). Microarray-based Gene Expression Analysis of Endocrine Systems: Principles of Experimental Design and Interpretation. In: Handwerger, S., Aronow, B. (eds) Genomics in Endocrinology. Contemporary Endocrinology. Humana Press. https://doi.org/10.1007/978-1-59745-309-7_1
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