Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39834440", "kind": "science-record", "revision": 4, "content_hash": "sha256:3d7e1e652d6643c5529f8d7d731aeafbbbde69634434b6766b414a40f3b0447c", "created_at": "2026-11-01T20:30:08Z", "updated_at": "2026-11-01T22:56:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia.", "doi": "10.1016/j.heliyon.2024.e41266", "pmid": "39834440", "authors": ["Hind A Alkhatabi", "Peter Natesan Pushparaj"], "journal": "Heliyon", "year": 2025, "abstract": "Objectives: Alzheimer's disease (AD) is a complex neurodegenerative disorder that primarily affects elderly individuals. This study aimed to elucidate the intricate mechanisms underlying AD in elderly patients compared with healthy aged individuals using high-throughput RNA sequencing (RNA-seq) data and next-generation knowledge discovery methods (NGKD), with a focus on identifying potential therapeutic agents.\nMethods: High-throughput RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE104704). These data were derived from healthy and diseased human brains (eight young healthy brains [young], 10 aged healthy brains [Old], and 12 aged diseased brains [AD]). We used NGKD tools such as GEO RNA-seq Experiments Interactive Navigator (GREIN) to obtain differentially expressed genes (DEGs) by comparing the AD versus Old RNA-seq data and further filtered and normalized to obtain differentially regulated Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and Panther pathways using ExpressAnalyst tool. Besides, WebGestalt was used to identify differentially regulated Gene Ontologies (GO) and the pre-ranked Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. The X2K web tool was used to infer upstream regulator networks and X2K Appyter tool for obtaining transcription factors (TFs) and kinase network information. LFW1000 and L1000CDS2 tools were used to identify specific drugs that reverse AD-associated gene signatures in elderly patients.\nResults: Our study revealed significant downregulation of pathways related to neuroactive receptor-ligand interaction, synaptic vesicle cycle, and neuronal system in elderly individuals with AD. GO analysis showed negative enrichment of functions related to cognition, potassium ion transport, receptor-ligand activity, SNARE binding, and primary lysosomes. The transcription factors SUZ12 and REST, along with increased MAPK signaling, were identified as key regulators of downregulated genes. Several drugs and natural products, including dihydroergocristine, mepacrine, gedunin, amlodipine, and disulfiram have been identified as potential therapeutic agents for reversing AD-associated gene signatures.\nConclusions: This comprehensive analysis of AD in elderly individuals using RNA-seq data and NGKD tools revealed multiple differentially regulated pathways, gene signatures, and potential drugs for dementia treatment. These findings highlight the complex molecular mechanisms underlying AD and provide insights into potential therapeutic strategies. Further research is needed to validate these findings and to develop personalized treatment approaches for AD in elderly patients.", "claims": [{"id": "claim-method", "statement": "The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[6] \"ExpressAnalyst analysis\" paragraph 2", "quote": "The normalized data were subsequently used as inputs for the limma-based differential expression analysis method, which employs a log2-counts per million (log CPM) transformation and applied filters, such as the low abundance (cutoff value set at 4) and variance filters, as well as a filter for unannotated genes (cutoff value set at 15) [19,20] (Access Date: Jan 28, 2024)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[15] \"Data availability statement\" paragraph 2", "quote": "The RNAseq dataset used in this study (accession number GSE104704) is openly available in the Gene Expression Omnibus (GEO) for download and reuse."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39834440", "locator": "sections[11] \"Results\" paragraph 2", "quote": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Utilizing the ExpressAnalyst tool and filtered metadata and raw counts from GREIN, we identified 1255 DEGs between the two groups (aged, deceased vs. aged, healthy), as depicted in the volcano plot (Fig. 1A) (Supplementary File 1).", "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:39834440"], "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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