Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36195955", "kind": "science-record", "revision": 5, "content_hash": "sha256:1864e8f612a3d6ee7a84ba87252d304b07255d7b75056d1a44da329560243502", "created_at": "2026-11-01T18:15:30Z", "updated_at": "2026-11-01T22:50:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm.", "doi": "10.1186/s41065-022-00252-x", "pmid": "36195955", "authors": ["Wencan Ji", "Ke An", "Canjun Wang", "Shaohua Wang"], "journal": "Hereditas", "year": 2022, "abstract": "BACKGROUND: The prevalence of Alzheimer's disease (AD) varies based on gender. Due to the lack of early stage biomarkers, most of them are diagnosed at the terminal stage. This study aimed to explore sex-specific signaling pathways and identify diagnostic biomarkers of AD.\nMETHODS: Microarray dataset for blood was obtained from the Gene Expression Omnibus (GEO) database of GSE63060 to conduct differentially expressed genes (DEGs) analysis by R software limma. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene set enrichment analysis (GSEA) were conducted. Immune checkpoint gene expression was compared between females and males. Using CytoHubba, we identified hub genes in a protein-protein interaction network (PPI). Then, we evaluated their distinct effectiveness using unsupervised hierarchical clustering. Support vector machine (SVM) and ten-fold cross-validation were used to further verify these biomarkers. Lastly, we confirmed our findings by using another independent dataset.\nRESULTS: A total of 37 female-specific DEGs and 27 male-specific DEGs were identified from GSE63060 datasets. Analyses of enrichment showed that female-specific DEGs primarily focused on energy metabolism, while male-specific DEGs mostly involved in immune regulation. Three immune-checkpoint-relevant genes dysregulated in males. In females, however, these eight genes were not differentially expressed. SNRPG, RPS27A, COX7A2, ATP5PO, LSM3, COX7C, PFDN5, HINT1, PSMA6, RPS3A and RPL31 were regarded as hub genes for females, while SNRPG, RPL31, COX7C, RPS27A, RPL35A, RPS3A, RPS20 and PFDN5 were regarded as hub genes for males. Thirteen hub genes mentioned above was significantly lower in both AD and mild cognitive impairment (MCI). The diagnostic model of 15-marker panel (13 hub genes with sex and age) was developed. Both the training dataset and the independent validation dataset have area under the curve (AUC) with a high value (0.919, 95%CI 0.901-0.929 and 0.803, 95%CI 0.789-0.826). Based on GSEA for hub genes, they were associated with some aspects of AD pathogenesis.\nCONCLUSION: DEGs in males and females contribute differently to AD pathogenesis. Algorithms combining blood-based biomarkers may improve AD diagnostic accuracy, but large validation studies are needed.", "claims": [{"id": "claim-method", "statement": "R package Limma (version: 3.40.2) [28] was used to analyze the mRNA differential expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36195955", "locator": "sections[4] \"Data normalization and identification of DEGs in males and females\" paragraph 2", "quote": "R package Limma (version: 3.40.2) [28] was used to analyze the mRNA differential expression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the [GSE63060] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060], in [GSE63061] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63061].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36195955", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets generated and/or analyzed during the current study are available in the [GSE63060] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060], in [GSE63061] repository [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63061]."}], "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:36195955"], "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:GSE63060"], "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": {}, "inputs": ["data:geo:GSE63060"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36195955", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s41065-022-00252-x", "sha256": 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