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pmid:34225819
Identification of potential biomarkers for pathogenesis of Alzheimer's disease.
Huimin Wang · Xiujiang Han · Sheng Gao
Hereditas · 2021 doi:10.1186/s41065-021-00187-9
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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This study aimed to explore potential biomarkers for AD.\nMETHODS: GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes.\nRESULTS: There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms.\nCONCLUSIONS: In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.", "claims": [{"id": "claim-method", "statement": "The differentially expressed genes (DEG) between AD and control groups were analyzed using the limma software package [25] in R language.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34225819", "locator": "sections[4] \"Differential expression analysis\" paragraph 2", "quote": "The differentially expressed genes (DEG) between AD and control groups were analyzed using the limma software package [25] in R language."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in (GEO) at (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34225819", "locator": "sections[22] \"notes\" paragraph 1", "quote": "The data that support the findings of this study are available in (GEO) at (https://www.ncbi.nlm.nih.gov/geo/)."}], "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:34225819"], "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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