Partial least squares based gene expression analysis in renal failure

Abstract

BackgroundPreventive and therapeutic options for renal failure are still limited. Gene expression profile analysis is powerful in the identification of biological differences between end stage renal failure patients and healthy controls. Previous studies mainly used variance/regression analysis without considering various biological, environmental factors. The purpose of this study is to investigate the gene expression difference between end stage renal failure patients and healthy controls with partial least squares (PLS) based analysis.MethodsWith gene expression data from the Gene Expression Omnibus database, we performed PLS analysis to identify differentially expressed genes. Enrichment and network analyses were also carried out to capture the molecular signatures of renal failure.ResultsWe acquired 573 differentially expressed genes. Pathway and Gene Ontology items enrichment analysis revealed over-representation of dysregulated genes in various biological processes. Network analysis identified seven hub genes with degrees higher than 10, including CAND1, CDK2, TP53, SMURF1, YWHAE, SRSF1, and RELA. Proteins encoded by CDK2, TP53, and RELA have been associated with the progression of renal failure in previous studies.ConclusionsOur findings shed light on expression character of renal failure patients with the hope to offer potential targets for future therapeutic studies.Virtual SlidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1450799302127207