A data mining and semantic analysis reveals novel insights into the genetic characteristics of the glucocorticoid receptor interactome

The availability of single nucleotide polymorphisms (SNPs) that have been identified in a given gene and have been related to several diseases provides the opportunity to study complex biological pathways and can assist researchers in better associating genes and disease‑linked terms in the areas of genetics, genomics and epigenetics. The study of the glucocorticoid receptor (GR) interactome through SNP observations in 'key‑player' genes will provide researcher with the opportunity to draw the 'genomic grammar' of the complex biological mechanisms associated with GR function and will open new horizons in biology, medicine, pharmacology and even extend to personalized medicine. The GR interactome is extensive, and is involved in several physiological and pathological processes of the organism. Glucocorticoids are the final product of the hypothalamic‑pituitary‑adrenal axis and an inextricable part of the stress system. These hormones are implicated in various critical systems and processes for the human organism, such as the immune system, development, metabolism and several others. GR is the protein that mediates their actions and is involved in several interactions with specific genes and proteins. In the present study, in order to unravel new beneficial knowledge on genetic targets regarding the GR interactome, a data mining and semantic pipeline was performed using the available literature. More specifically, through bioinformatics tools and methods, the most relevant SNPs and genes connected to the GR interactome were extracted. Subsequently, the outcome SNPs were filtered, annotated, classified and evaluated in order to create the 'genomic grammar' and identify the related disease with the interactome of GR. Genomic background and heredity play a significant role in the GR interactome. A more in‑depth understanding of the biological pathways and complex actions of the GR may lead to the design and development of more effective treatments for inflammatory and autoimmune diseases, as well as cancers.