Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34367470", "kind": "science-record", "revision": 8, "content_hash": "sha256:10046f5609de08a288de32da11d6d9f4cd9fd1a0ee7114684ed63f51dc8b0277", "created_at": "2026-11-01T15:41:47Z", "updated_at": "2026-11-01T22:47:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease.", "doi": "10.1155/2021/9918498", "pmid": "34367470", "authors": ["Tingting Zhang", "Nanyang Liu", "Wei Wei", "Zhen Zhang", "Hao Li"], "journal": "Oxidative medicine and cellular longevity", "year": 2021, "abstract": "BACKGROUND: Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD.\nMETHODS: In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve.\nRESULTS: In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e - 20), and it was visualized by establishing a protein-protein interaction network. Moreover, this module was particularly enriched in \"pathways of neurodegeneration-multiple diseases,\" \"Alzheimer disease,\" \"oxidative phosphorylation,\" and \"proteasome.\" Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940).\nCONCLUSIONS: Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.", "claims": [{"id": "claim-method", "statement": "The processed data were subjected to normalisation and log base 2 transformation using the GSE5281 dataset, which was screened for differential expression analysis between AD and healthy tissues, which was performed using the “Limma” package in R software version 3.6.0.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34367470", "locator": "sections[5] \"2.3. Identification of DEGs\" paragraph 2", "quote": "The processed data were subjected to normalisation and log base 2 transformation using the GSE5281 dataset, which was screened for differential expression analysis between AD and healthy tissues, which was performed using the “Limma” package in R software version 3.6.0."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data is public and can be downloaded from the GEO database for free.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34367470", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The data is public and can be downloaded from the GEO database for free."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34367470"], "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. 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