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pmid:34824764

Screening and Analysis of Key Genes in miRNA-mRNA Regulatory Network of Membranous Nephropathy.

Yawei Hou · Yameng Li · Yichuan Wang · Wenpu Li · Zhenwei Xiao

Journal of healthcare engineering · 2021 doi:10.1155/2021/5331948

Paper

complete

Experiment

complete

Data

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Analysis

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pmid:34824764
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title
Screening and Analysis of Key Genes in miRNA-mRNA Regulatory Network of Membranous Nephropathy.
doi
10.1155/2021/5331948
pmid
34824764
authors array · 5 items
[0]
Yawei Hou
[1]
Yameng Li
[2]
Yichuan Wang
[3]
Wenpu Li
[4]
Zhenwei Xiao
journal
Journal of healthcare engineering
year
2021
abstract
Background: MicroRNAs (miRNAs) are confirmed to participate in occurrence, development, and prevention of membranous nephropathy (MN), but their mechanism of action is unclear. Objective: With the GEO database and the use of bioinformatics, miRNA-mRNA regulatory network genes relevant to MN were explored and their potential mechanism of action was explained. Methods: The MN-related miRNA chip data set (GSE51674) and mRNA chip data set (GSE108109) were downloaded from the GEO database. Differential analysis was performed using the GEO2R online tool. TargetScan, miRTarBase, and StarBase databases were used to predict potential downstream target genes regulated by differentially expressed miRNAs, and the intersection with differential genes were taken to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair was clarified and Cytoscape was used to construct a miRNA-mRNA regulatory network. WebGestalt was used to conduct enrichment analysis of the biological process of differential mRNAs in the regulatory network; FunRich analyzes the differential mRNA pathways in the miRNA-mRNA regulatory network. And the STRING database was used to construct a PPI network for candidate target genes, and Cytoscape visually analyzes the PPI network. Results: Experiments were conducted to screen differentially expressed miRNAs and mRNAs. There were 30 differentially expressed miRNAs, including 22 upregulated and 8 downregulated; and 1267 differentially expressed mRNAs, including 536 upregulated and 731 downregulated. Using TargetScan, miRTarBase, and StarBase databases to predict the downstream targets of differentially expressed miRNAs, 2957 downstream target genes coexisting in the 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 175 candidate target genes. Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed. Further analysis revealed that the miRNA regulatory network genes may be involved in the development of membranous nephropathy by mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways. Conclusion: The miRNA regulatory network genes may participate in the regulation of podocyte autophagy, lipid metabolism, and renal fibrosis through mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways, thereby affecting the occurrence and development of membranous nephropathy.
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The online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) from NCBI's GEO online analysis tool was used to obtain differential mRNAs and miRNAs and screen the differential genes. The standard setting is adj. p value <0.01 and |log2 fold change (FC)| > 1.
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sections[7] "2.4.1. Data Processing and Differential Expression Analysis" paragraph 2
quote
The online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) from NCBI's GEO online analysis tool was used to obtain differential mRNAs and miRNAs and screen the differential genes. The standard setting is adj. p value <0.01 and |log2 fold change (FC)| > 1.
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sections[21] "Data Availability" paragraph 2
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The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.
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Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed.
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Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed.
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Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed.
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16
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null
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10656
taxon
Homo sapiens
title
miRNA-27b-3p and miRNA-1228-3p correlate with the progression of Kidney Fibrosis in Diabetic Nephropathy
resolution_checked_at
2026-11-01T17:30:42Z
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The online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) from NCBI's GEO online analysis tool was used to obtain differential mRNAs and miRNAs and screen the differential genes. The standard setting is adj. p value <0.01 and |log2 fold change (FC)| > 1.
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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: ppi_network.
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data object · 6 fields
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The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.
basis
The paper names GSE51674, GSE108109, GSE108113 in GEO (Gene Expression Omnibus) database of NCBI and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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MiniMax-M3.1-Flash
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minimax
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2026-11-01T17:08:55Z
independence
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verbatim
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PMID 34824764 full text
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GEO GSE108109
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pmid:34824764
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source:pmid:34824764
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PMID 34824764 full text
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dataset
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GEO GSE108109
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agent:corpus-assay-engine
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34824764", "kind": "science-record", "revision": 4, "content_hash": "sha256:035ae751c44fdd99c51b26c1802247c9cca555320bb4d8bc9d747f98ac9b60a7", "created_at": "2026-11-01T17:36:31Z", "updated_at": "2026-11-01T22:05:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening and Analysis of Key Genes in miRNA-mRNA Regulatory Network of Membranous Nephropathy.", "doi": "10.1155/2021/5331948", "pmid": "34824764", "authors": ["Yawei Hou", "Yameng Li", "Yichuan Wang", "Wenpu Li", "Zhenwei Xiao"], "journal": "Journal of healthcare engineering", "year": 2021, "abstract": "Background: MicroRNAs (miRNAs) are confirmed to participate in occurrence, development, and prevention of membranous nephropathy (MN), but their mechanism of action is unclear.\nObjective: With the GEO database and the use of bioinformatics, miRNA-mRNA regulatory network genes relevant to MN were explored and their potential mechanism of action was explained.\nMethods: The MN-related miRNA chip data set (GSE51674) and mRNA chip data set (GSE108109) were downloaded from the GEO database. Differential analysis was performed using the GEO2R online tool. TargetScan, miRTarBase, and StarBase databases were used to predict potential downstream target genes regulated by differentially expressed miRNAs, and the intersection with differential genes were taken to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair was clarified and Cytoscape was used to construct a miRNA-mRNA regulatory network. WebGestalt was used to conduct enrichment analysis of the biological process of differential mRNAs in the regulatory network; FunRich analyzes the differential mRNA pathways in the miRNA-mRNA regulatory network. And the STRING database was used to construct a PPI network for candidate target genes, and Cytoscape visually analyzes the PPI network.\nResults: Experiments were conducted to screen differentially expressed miRNAs and mRNAs. There were 30 differentially expressed miRNAs, including 22 upregulated and 8 downregulated; and 1267 differentially expressed mRNAs, including 536 upregulated and 731 downregulated. Using TargetScan, miRTarBase, and StarBase databases to predict the downstream targets of differentially expressed miRNAs, 2957 downstream target genes coexisting in the 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 175 candidate target genes. Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed. Further analysis revealed that the miRNA regulatory network genes may be involved in the development of membranous nephropathy by mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways.\nConclusion: The miRNA regulatory network genes may participate in the regulation of podocyte autophagy, lipid metabolism, and renal fibrosis through mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways, thereby affecting the occurrence and development of membranous nephropathy.", "claims": [{"id": "claim-method", "statement": "The online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) from NCBI's GEO online analysis tool was used to obtain differential mRNAs and miRNAs and screen the differential genes. The standard setting is adj. p value <0.01 and |log2 fold change (FC)| > 1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34824764", "locator": "sections[7] \"2.4.1. 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The standard setting is adj. p value <0.01 and |log2 fold change (FC)| > 1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34824764", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The simulation experiment data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34824764", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed.", "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:34824764"], "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:GSE108109"], "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:GSE108109"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34824764", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/5331948", "sha256": 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