Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35966794", "kind": "science-record", "revision": 5, "content_hash": "sha256:4c5bf7206252dfc46322d2c051c60e107cdca107be248b8961634561cddd9c02", "created_at": "2026-11-01T18:15:05Z", "updated_at": "2026-11-01T22:49:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies.", "doi": "10.3389/fnagi.2022.919614", "pmid": "35966794", "authors": ["Yu Tian", "Yaoheng Lu", "Yuze Cao", "Chun Dang", "Na Wang", "Kuo Tian", "Qiqi Luo", "Erliang Guo", "Shanshun Luo", "Lihua Wang", "Qian Li"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Objective: As a chronic neurodegenerative disorder, Alzheimer's disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology.\nMethods: The expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD.\nResults: A total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4+ T memory cells, resting mast cells, memory B cells, and resting/activated dendritic cells may participate in the development of AD. Additionally, all diagnostic signatures presented different degrees of correlation with different infiltrating immune cells.\nConclusion: MAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.", "claims": [{"id": "claim-method", "statement": "After removing batch effects using the “SVA” R package, we analyzed differentially expressed genes (DEGs) between 146 AD brain tissues and 93 healthy controls using the “limma” R package (Ritchie et al., 2015; Varma, 2020).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966794", "locator": "sections[4] \"Differential gene expression analysis\" paragraph 2", "quote": "After removing batch effects using the “SVA” R package, we analyzed differentially expressed genes (DEGs) between 146 AD brain tissues and 93 healthy controls using the “limma” R package (Ritchie et al., 2015; Varma, 2020)."}], "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 in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35966794", "locator": "sections[19] \"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 in the article/supplementary material."}], "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:35966794"], "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:GSE109887"], "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:GSE109887"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35966794", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2022.919614", "sha256": "sha256:9d8171527d0766101471fd619cf580055a0730af8092c1da8320631dd73622ab", "bytes": 39943, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2022.919614", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9372364", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE109887", "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=GSE109887", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE109887", "version": null, "note": "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 in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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Varma, 2020)."}}, {"id": "method-2", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "support vector machine"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE109887", "GSE122063", "GSE28146", "GSE1297", "GSE5281"], "resolved_accessions": ["GSE109887", "GSE122063", "GSE28146", "GSE1297", "GSE5281"], "statements": ["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 in the article/supplementary material."], "basis": "The paper names GSE109887, GSE122063, GSE28146, GSE1297, GSE5281 in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:35966794", "data:geo:GSE109887"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE109887"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:00:06Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12360, "completion_tokens": 2151, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35966794", "type": "paper", "label": "pmid:35966794"}, {"id": "source:pmid:35966794", "type": "manuscript", "label": "PMID 35966794 full text"}, {"id": "data:geo:GSE109887", "type": "dataset", "label": "GEO GSE109887"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35966794", "type": "paper", "label": "pmid:35966794"}, {"id": "source:pmid:35966794", "type": "manuscript", "label": "PMID 35966794 full text"}, {"id": "data:geo:GSE109887", "type": "dataset", "label": "GEO GSE109887"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35966794", "type": "extraction", "used": ["source:pmid:35966794"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE109887", "type": "accession_resolution", "used": ["data:geo:GSE109887"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35966794", "parent_hash": "sha256:2d2ee61e22dce4489643d7b7792e10b31a49f9a9b93b70ff584c1027e290dbbd", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE109887 licence not read", "asset:data:geo:GSE109887 not downloaded", "asset:data:geo:GSE122063 licence not read", "asset:data:geo:GSE122063 not downloaded", "asset:data:geo:GSE1297 licence not read", "asset:data:geo:GSE1297 not downloaded", "asset:data:geo:GSE28146 licence not read", "asset:data:geo:GSE28146 not downloaded", "asset:data:geo:GSE5281 licence not read", "asset:data:geo:GSE5281 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["After removing batch effects using the “SVA” R package, we analyzed differentially expressed genes (DEGs) between 146 AD brain tissues and 93 healthy controls using the “limma” R package (Ritchie et al., 2015; Varma, 2020)."], "order_basis": "analysis_only", "id": "assay:1"}]}