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pmid:35571562

Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA.

Jia Wang · Yuan Yin · Qun Lu · Yan-Rong Zhao · Yu-Jie Hu · Yun-Zhao Hu · Zheng-Yin Wang

Journal of immunology research · 2022 doi:10.1155/2022/4615292

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:35571562
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science-record
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2026-11-01T18:14:27Z
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2026-11-01T21:27:51Z
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paper object · 10 fields
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title
Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA.
doi
10.1155/2022/4615292
pmid
35571562
authors array · 7 items
[0]
Jia Wang
[1]
Yuan Yin
[2]
Qun Lu
[3]
Yan-Rong Zhao
[4]
Yu-Jie Hu
[5]
Yun-Zhao Hu
[6]
Zheng-Yin Wang
journal
Journal of immunology research
year
2022
abstract
Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.
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In order to find differences in gene expression between CKD samples and healthy controls, GEO2R was used [16]. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated.
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sections[4] "2.2. Identification of Differentially Expressed Genes (DEGs)" paragraph 2
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In order to find differences in gene expression between CKD samples and healthy controls, GEO2R was used [16].
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After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated.
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The data used to support the findings of this study are available from the corresponding author upon request.
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These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people.
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These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people.
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A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18.
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These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people.
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Human PBMCs: Healthy vs Diabetic nephropathy vs ESRD
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Whole genome expression in peripheral whole blood of chronic kidney disease patients having uncertain etiology (CKDu) compared to healthy controls
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Development of gene expression profiles in human chronic kidney disease
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35571562", "kind": "science-record", "revision": 3, "content_hash": "sha256:eab041ea0675d035b26e1e2eea9ddb05d27f83a8d3e802309f2dbde3cea90778", "created_at": "2026-11-01T18:14:27Z", "updated_at": "2026-11-01T21:27:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA.", "doi": "10.1155/2022/4615292", "pmid": "35571562", "authors": ["Jia Wang", "Yuan Yin", "Qun Lu", "Yan-Rong Zhao", "Yu-Jie Hu", "Yun-Zhao Hu", "Zheng-Yin Wang"], "journal": "Journal of immunology research", "year": 2022, "abstract": "Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.", "claims": [{"id": "claim-method", "statement": "In order to find differences in gene expression between CKD samples and healthy controls, GEO2R was used [16]. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35571562", "locator": "sections[4] \"2.2. Identification of Differentially Expressed Genes (DEGs)\" paragraph 2", "quote": "In order to find differences in gene expression between CKD samples and healthy controls, GEO2R was used [16]."}, {"asset": "source:pmid:35571562", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The 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:35571562", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35571562", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people."}, {"asset": "source:pmid:35571562", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people.", "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:35571562"], "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:GSE142153"], "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:GSE142153"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35571562", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/4615292", "sha256": 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