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pmid:31700890

Identification of CD2, CCL5 and CCR5 as potential therapeutic target genes for renal interstitial fibrosis.

Chuanjie Zhang · Xin Hu · Feng Qi · Jun Luo · Xiao Li

Annals of translational medicine · 2019 doi:10.21037/atm.2019.08.62

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Experiment

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Data

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title
Identification of CD2, CCL5 and CCR5 as potential therapeutic target genes for renal interstitial fibrosis.
doi
10.21037/atm.2019.08.62
pmid
31700890
authors array · 5 items
[0]
Chuanjie Zhang
[1]
Xin Hu
[2]
Feng Qi
[3]
Jun Luo
[4]
Xiao Li
journal
Annals of translational medicine
year
2019
abstract
BACKGROUND: We aimed to explore potential gene biomarkers of renal interstitial fibrosis (RIF) due to a lack of effective and non-invasive methods for diagnosis. METHODS: Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database. We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by 'edgeR' package to identify the differentially expressed genes (DEGs). We generated heat maps with using heatmap package in R software. Function annotation of genes was performed by Gene Ontology (GO) enrichment analysis. STRING (Search Tool for the Retrieval of Interacting Genes) database was employed to construct the protein-protein interaction (PPI) network and the results were visualized by Cytoscape 3.6.1. At last, we applied Graphpad Prism 7.0. to explore the correlation between three hub genes and pathological degrees of RIF. RESULTS: By applying the "edgeR" package in R, we detected 116 DEGs with three data sets. These genes were enriched in 19 GO biological process categories. Three main hub genes (CD2, CCL5 and CCR5) were identified after construction of PPI network. In Pearson correlation coefficient, CD2, CCL5 and CCR5 was found to hold higher expression patterns in RIF samples based on independent data set GSE57731. Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38). CONCLUSIONS: CD2, CCL5 and CCR5 might serve as potential early biomarkers of RIF. The mechanism between these genes and RIF remains to be further studied.
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We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs).
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We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs).
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Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database.
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The gene expression levels of CD2, CCL5 and CCR5 were significantly positively correlated with the degree of RIF.
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Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38).
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Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38).
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The gene expression levels of CD2, CCL5 and CCR5 were significantly positively correlated with the degree of RIF.
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274
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null
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13158
taxon
Homo sapiens
title
Gene Expression in Biopsies of Acute Rejection and Interstitial Fibrosis/Tubular Atrophy Reveals Highly Shared Mechanisms that Correlate with Worse Long-term Outcomes
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2026-11-01T17:30:42Z
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identifier_resolution_only_no_download_no_licence_read
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We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs).
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The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
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candidate
Automated Assays Laravel application and simulated adapters
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: edger.
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database.
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The paper names GSE22459, GSE76882, GSE57731 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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minimax
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minimax
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single_reader
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verbatim
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PMID 31700890 full text
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We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs).
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31700890", "kind": "science-record", "revision": 5, "content_hash": "sha256:dc3646d02e960c231a95df9212c5deea58de3fc8a27829768034d2b7128c1031", "created_at": "2026-11-01T17:33:10Z", "updated_at": "2026-11-01T22:44:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of CD2, CCL5 and CCR5 as potential therapeutic target genes for renal interstitial fibrosis.", "doi": "10.21037/atm.2019.08.62", "pmid": "31700890", "authors": ["Chuanjie Zhang", "Xin Hu", "Feng Qi", "Jun Luo", "Xiao Li"], "journal": "Annals of translational medicine", "year": 2019, "abstract": "BACKGROUND: We aimed to explore potential gene biomarkers of renal interstitial fibrosis (RIF) due to a lack of effective and non-invasive methods for diagnosis.\nMETHODS: Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database. We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by 'edgeR' package to identify the differentially expressed genes (DEGs). We generated heat maps with using heatmap package in R software. Function annotation of genes was performed by Gene Ontology (GO) enrichment analysis. STRING (Search Tool for the Retrieval of Interacting Genes) database was employed to construct the protein-protein interaction (PPI) network and the results were visualized by Cytoscape 3.6.1. At last, we applied Graphpad Prism 7.0. to explore the correlation between three hub genes and pathological degrees of RIF.\nRESULTS: By applying the \"edgeR\" package in R, we detected 116 DEGs with three data sets. These genes were enriched in 19 GO biological process categories. Three main hub genes (CD2, CCL5 and CCR5) were identified after construction of PPI network. In Pearson correlation coefficient, CD2, CCL5 and CCR5 was found to hold higher expression patterns in RIF samples based on independent data set GSE57731. Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38).\nCONCLUSIONS: CD2, CCL5 and CCR5 might serve as potential early biomarkers of RIF. The mechanism between these genes and RIF remains to be further studied.", "claims": [{"id": "claim-method", "statement": "We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31700890", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31700890", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The gene expression levels of CD2, CCL5 and CCR5 were significantly positively correlated with the degree of RIF.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38)."}, "evidence": [{"asset": "source:pmid:31700890", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The gene expression levels of CD2, CCL5 and CCR5 were significantly positively correlated with the degree of RIF.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31700890"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE22459"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE22459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31700890", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm.2019.08.62", "sha256": 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