ArticleInternational journal of clinical and experimental medicine2014
Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles.
Article in International journal of clinical and experimental medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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, 3 citations in OpenAlex.
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- Association Between miR-605A>G, miR-608G>C, miR-631I>D, miR-938C>T, and miR-1302-3C>T Polymorphisms and Risk of Recurrent Implantation Failure.Reproductive sciences (Thousand Oaks, Calif.) · 2019Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRenal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent.
methodsMicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone.
resultsA total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection.
conclusionsWe conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.
Indexed as
Identifiers
25664019PMC4307466W2323214526What OpenQuestion holds
Registered trials
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