Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36561136", "kind": "science-record", "revision": 4, "content_hash": "sha256:35de3a84972b50015dfb925af72cc83f263ce928463c6066c861357c5f143597", "created_at": "2026-11-01T19:01:06Z", "updated_at": "2026-11-01T22:50:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors.", "doi": "10.3389/fnagi.2022.1069606", "pmid": "36561136", "authors": ["Qi Zhang", "Ping Yang", "Xinping Pang", "Wenbo Guo", "Yue Sun", "Yanyu Wei", "Chaoyang Pang"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Background: Alzheimer's disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear.\nMethods: The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the \"R\" language \"limma\" package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the \"R\" package \"survivation\" was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database.\nResults: GO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington's disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs.\nConclusion: In conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.", "claims": [{"id": "claim-method", "statement": "The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the “R” language “limma” package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Finally, for the GSE150693 dataset, the “R” package “survivation” was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36561136", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the “R” language “limma” package."}, {"asset": "source:pmid:36561136", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected."}, {"asset": "source:pmid:36561136", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Finally, for the GSE150693 dataset, the “R” package “survivation” was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method."}, {"asset": "source:pmid:36561136", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36561136", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified a potentially relevant miRNA/transcription-factor-gene network for Alzheimer’s disease, with TBP, CDK7, GRM5 and GRIA1 as core downregulated genes and hsa-miR-425-5p and hsa-miR-186-5p as potential target miRNAs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36561136", "locator": "sections[23] \"Conclusion\" paragraph 2", "quote": "Our research identified a miRNA/TF-gene network that is potentially relevant for AD."}, {"asset": "source:pmid:36561136", "locator": "sections[23] \"Conclusion\" paragraph 2", "quote": "The four hub genes (including TBP, CDK7 GRIA1, and GRM5) were markedly downregulated, which may have a critical influence on the pathophysiological mechanism of AD."}, {"asset": "source:pmid:36561136", "locator": "sections[23] \"Conclusion\" paragraph 2", "quote": "Two potential target miRNAs (hsa-miR-425-5p and hsa-miR-186-5p) were furthermore forecast."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified a potentially relevant miRNA/transcription-factor-gene network for Alzheimer’s disease, with TBP, CDK7, GRM5 and GRIA1 as core downregulated genes and hsa-miR-425-5p and hsa-miR-186-5p as potential target miRNAs.", "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:36561136"], "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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The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Finally, for the GSE150693 dataset, the “R” package “survivation” was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE5281", "GSE1297", "GSE122063", "GSE150693"], "resolved_accessions": ["GSE5281", "GSE1297", "GSE122063", "GSE150693"], "statements": ["The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE5281, GSE1297, GSE122063, GSE150693 in gene expression omnibus (GEO) database and the accession resolves in the repository. 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