Nanogenomics for Personalized Nanomedicine: An Application to Kidney Transplantation

Chronic Kidney Disease (CKD), defined by reduced glomerular filtration and/or albuminuria persistent for more than three months, is an important Public Health concern, being a real burden for the society. OMICS data generated by high-throughput technologies, converging together into the transpantomics approach, can face this issue, by enabling the discovery of predictive and personalized biomarkers. Using the Leader Gene algorithm, which takes into account gene expression, gene connectivities and biological pathways, we previously identified eight genes (namely, HTATIP/KAT5, c-JUN, TP53, ATF2, MAPK14, ARRB2, XBP1, and NPHS1), that we termed “hub genes”. In the present contribution, we accessed the Gene Expression Omnibus (GEO) database, a public repository of micro-array experiments, looking for the expression profiles of the previously identified Leader Genes. We found that 5 out of 8 genes (62.5%; HTATIP, c-JUN, TP53, ARRB2, and ATF2) are able to distinguish between rejection and tolerance to kidney transplantation, being differentially expressed between the two groups of patients in a statistically significant way. Some of these genes (HTATIP, ARRB2, and ATF2) have been rarely described as predictors of clinical outcome to renal graft in the extant literature.