Identification of Key Biomarkers and Pathways for Maintaining Cognitively Normal Brain Aging Based on Integrated Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35356296", "kind": "science-record", "revision": 5, "content_hash": "sha256:021555864d80e7b82aa9cd77ff5f2737d4095fd6206d6a0b44ae4edef9c16423", "created_at": "2026-11-01T18:14:09Z", "updated_at": "2026-11-01T22:48:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Key Biomarkers and Pathways for Maintaining Cognitively Normal Brain Aging Based on Integrated Bioinformatics Analysis.", "doi": "10.3389/fnagi.2022.833402", "pmid": "35356296", "authors": ["Jinling Xu", "Hui Zhou", "Guangda Xiang"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Background: Given the arrival of the aging population has caused a series of social and economic problems, we aimed to explore the key genes underlying cognitively normal brain aging and its potential molecular mechanisms.\nMethods: GSE11882 was downloaded from Gene Expression Omnibus (GEO). The data from different brain regions were divided into aged and young groups for analysis. Co-expressed differentially expressed genes (DEGs) were screened. Functional analysis, protein-protein interaction (PPI) network, microRNA (miRNA)-gene, and transcription factor (TF)-gene networks were performed to identify hub genes and related molecular mechanisms. AlzData database was used to elucidate the expression of DEGs and hub genes in the aging brain. Animal studies were conducted to validate the hub genes.\nResults: Co-expressed DEGs contained 7 upregulated and 87 downregulated genes. The enrichment analysis indicated DEGs were mainly involved in biological processes and pathways related to immune-inflammatory responses. From the PPI network, 10 hub genes were identified: C1QC, C1QA, C1QB, CD163, FCER1G, VSIG4, CD93, CD14, VWF, and CD44. CD44 and CD93 were the most targeted DEGs in the miRNA-gene network, and TIMP1, HLA-DRA, VWF, and FGF2 were the top four targeted DEGs in the TF-gene network. In AlzData database, the levels of CD44, CD93, and CD163 in patients with Alzheimer's disease (AD) were significantly increased than those in normal controls. Meanwhile, in the brain tissues of cognitively normal mice, the expression of CD44, CD93, and CD163 in the aged group was significantly lower than those in the young group.\nConclusion: The underlying molecular mechanisms for maintaining healthy brain aging are related to the decline of immune-inflammatory responses. CD44, CD93, and CD 163 are considered as potential biomarkers. This study provides more molecular evidence for maintaining cognitively normal brain aging.", "claims": [{"id": "claim-method", "statement": "To identify DEGs, normalized log-expression data in the aged and young groups were analyzed by limma (linear models for microarray data) V3.46.0 package in the R/Bioconductor platform. Benjamini–Hochberg’s approach was used to adjust the false discovery rate (FDR) and genes with the adjusted P-value < 0.05 and | Log2 fold-change (log2FC)| > 1 were selected as DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35356296", "locator": "sections[4] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "To identify DEGs, normalized log-expression data in the aged and young groups were analyzed by limma (linear models for microarray data) V3.46.0 package in the R/Bioconductor platform. Benjamini–Hochberg’s approach was used to adjust the false discovery rate (FDR) and genes with the adjusted P-value < 0.05 and | Log2 fold-change (log2FC)| > 1 were selected as DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35356296", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Co-expressed DEGs contained 7 upregulated and 87 downregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35356296", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Co-expressed DEGs contained 7 upregulated and 87 downregulated genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Co-expressed DEGs contained 7 upregulated and 87 downregulated genes.", "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:35356296"], "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:GSE11882"], "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": {"fdr_threshold": "with Benjamini–Hochberg false discovery rate adjustment"}, "inputs": ["data:geo:GSE11882"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35356296", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2022.833402", "sha256": "sha256:18a1e85c4f0f08384e6fe637179d0c58076650ddb6b3f7766254d42217036311", "bytes": 39071, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2022.833402", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8959911", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE11882", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11882", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE11882", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The original microarray dataset of GSE11882 was downloaded from the"], "order_basis": "paper_text_order:2071", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To identify DEGs, normalized log-expression data in the aged and young groups were analyzed by limma (linear models for microarray data) V3.46.0 package in the R/Bioconductor platform. Benjamini–Hochberg’s approach was used to adjust the false discovery rate (FDR) and genes with the adjusted P-value < 0.05 and | Log2 fold-change (log2FC)| > 1 were selected as DEGs."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}