ArticleDiagnostic pathology2014
Partial least squares based gene expression analysis in renal failure.
Article in Diagnostic pathology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- RNA-binding proteins and their role in kidney disease.Nature reviews. Nephrology · 2022Review
- Characterization of the Fundulus heteroclitus embryo transcriptional response and development of a gene expression-based fingerprint of exposure for the alternative flame retardant, TBPH (bis (2-ethylhexyl)-tetrabromophthalate).Environmental pollution (Barking, Essex : 1987) · 2019Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1450799302127207.
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