Immune Regulation and ECM-Related Pathway Enrichment Reveal ATP2A3 as a Prognostic Biomarker for Nonspecific Orbital Inflammation: An Integrated Machine Learning and Mendelian Randomization Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40548296", "kind": "science-record", "revision": 4, "content_hash": "sha256:359c2c149d0f4a319c8fe6921ff56c23d70abd4ec4970af471413e9f525586ac", "created_at": "2026-11-01T20:30:38Z", "updated_at": "2026-11-01T22:56:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune Regulation and ECM-Related Pathway Enrichment Reveal ATP2A3 as a Prognostic Biomarker for Nonspecific Orbital Inflammation: An Integrated Machine Learning and Mendelian Randomization Analysis.", "doi": "10.1155/mi/7061507", "pmid": "40548296", "authors": ["Zixuan Wu", "Xi Long", "Kang Tan", "Xiaolei Yao", "Qinghua Peng"], "journal": "Mediators of inflammation", "year": 2025, "abstract": "Background: Nonspecific orbital inflammation (NSOI) is a heterogeneous inflammatory disorder of the orbit with an unclear etiology. ATP2A3, a key regulator of calcium homeostasis in the endoplasmic reticulum (ER), may play a pivotal role in NSOI pathogenesis. Its potential as a diagnostic biomarker merits thorough investigation. Methods: Differentially expressed genes (DEGs) common to two GEO datasets (GSE58331 and GSE105149) were intersected with immune-related genes from the ImmPort database, yielding 89 candidates. ATP2A3 was prioritized using machine learning (ML) approaches, including LASSO, support vector machine (SVM)-RFE, and weighted gene coexpression network analysis (WGCNA). Functional enrichment was assessed using GSEA and GSVA based on genes co-expressed with ATP2A3. Immune microenvironment characteristics were evaluated using CIBERSORT and ESTIMATE. Expression of ATP2A3 was validated in GSE105149. Results: Fifteen hub genes were identified, with ATP2A3 strongly linked to immune-related pathways. Genes positively correlated with ATP2A3 were enriched in sensory perception and extracellular matrix (ECM) organization. Immune infiltration analysis revealed a positive association between ATP2A3 expression and memory B cells, M2 macrophages, resting mast cells, monocytes, and regulatory T cells (Tregs), while naive B cells and plasma cells were negatively associated. ATP2A3 exhibited significant diagnostic potential for distinguishing NSOI. Conclusions: In the context of NSOI, we identify ATP2A3 as a novel contributor to immune-driven pathogenesis. Its significant dysregulation in NSOI tissues relative to healthy controls underscores its potential as a prognostic marker within the inflammatory microenvironment.", "claims": [{"id": "claim-method", "statement": "ATP2A3 was prioritized using machine learning (ML) approaches, including LASSO, support vector machine (SVM)-RFE, and weighted gene coexpression network analysis (WGCNA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40548296", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "ATP2A3 was prioritized using machine learning (ML) approaches, including LASSO, support vector machine (SVM)-RFE, and weighted gene coexpression network analysis (WGCNA)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40548296", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ATP2A3 showed diagnostic potential for distinguishing NSOI, with an AUC of 0.743.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40548296", "locator": "sections[12] \"3.3. Identification of Model Gene\" paragraph 2", "quote": "The ROC analysis of the 15 hub genes further confirmed their high predictive accuracy (AUC): ASS1-0.719, ATP2A3-0.743, CEBPA-0.728, CSTA-0.748, FAM46C-0.698, HSD11B1-0.679, IRX5-0.716, KCNN4-0.711, MMD-0.752, MPPED2-0.783, PHGDH-0.707, SFRP1-0.668, SIX1-0.809, TMEM30B-0.869, and WFDC2-0.732."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ATP2A3 showed diagnostic potential for distinguishing NSOI, with an AUC of 0.743.", "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:40548296"], "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:GSE105149"], "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:GSE105149"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40548296", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/mi/7061507", "sha256": "sha256:abb8be54560ba1d6fb0d08db732b47c0d2e94c0c07e46313553779a8c7acfc27", "bytes": 58333, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/mi/7061507", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12181670", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE105149", "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=GSE105149", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE105149", "version": null, "note": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2823303", "GSM2823304", "GSM2823305", "GSM2823306", "GSM2823307", "GSM2823308", "GSM2823309", "GSM2823310", "GSM2823311", "GSM2823312", "GSM2823313", "GSM2823314", "GSM2823315", "GSM2823316", "GSM2823317", "GSM2823318", "GSM2823319", "GSM2823320", "GSM2823321", "GSM2823322", "GSM2823323", "GSM2823324", "GSM2823325", "GSM2823326", "GSM2823327", "GSM2823328", "GSM2823329", "GSM2823330", "GSM2823331", "GSM2823332", "GSM2823333", "GSM2823334", "GSM2823335", "GSM2823336", "GSM2823337", "GSM2823338", "GSM2823339", "GSM2823340", "GSM2823341", "GSM2823342", "GSM2823343", "GSM2823344", "GSM2823345", "GSM2823346", "GSM2823347", "GSM2823348", "GSM2823349", "GSM2823350", "GSM2823351"], "sample_count": 49, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene Expression in the Human Lacrimal Gland", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE58331", "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=GSE58331", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE58331", "version": null, "note": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1407182", "GSM1407183", "GSM1407184", "GSM1407185", "GSM1407186", "GSM1407187", "GSM1407188", "GSM1407189", "GSM1407190", "GSM1407191", "GSM1407192", "GSM1407193", "GSM1407194", "GSM1407195", "GSM1407196", "GSM1407197", "GSM1407198", "GSM1407199", "GSM1407200", "GSM1407201", "GSM1407202", "GSM1407203", "GSM1407204", "GSM1407205", "GSM1407206", "GSM1407207", "GSM1407208", "GSM1407209", "GSM1407210", "GSM1407211", "GSM1407212", "GSM1407213", "GSM1407214", "GSM1407215", "GSM1407216", "GSM1407217", "GSM1407218", "GSM1407219", "GSM1407220", "GSM1407221", "GSM1407222", "GSM1407223", "GSM1407224", "GSM1407225", "GSM1407226", "GSM1407227", "GSM1407228", "GSM1407229", "GSM1407230", "GSM1407231", "GSM1407232", "GSM1407233", "GSM1407234", "GSM1407235", "GSM1407236", "GSM1407237", "GSM1407238", "GSM1407239", "GSM1407240", "GSM1407241", "GSM1407242", "GSM1407243", "GSM1407244", "GSM1407245", "GSM1407246", "GSM1407247", "GSM1407248", "GSM1407249", "GSM1407250", "GSM1407251", "GSM1407252", "GSM1407253", "GSM1407254", "GSM1407255", "GSM1407256", "GSM1407257", "GSM1407258", "GSM1407259", "GSM1407260", "GSM1407261", "GSM1407262", "GSM1407263", "GSM1407264", "GSM1407265", "GSM1407266", "GSM1407267", "GSM1407268", "GSM1407269", "GSM1407270", "GSM1407271", "GSM1407272", "GSM1407273", "GSM1407274", "GSM1407275", "GSM1407276", "GSM1407277", "GSM1407278", "GSM1407279", "GSM1407280", "GSM1407281", "GSM1407282", "GSM1407283", "GSM1407284", "GSM1407285", "GSM1407286", "GSM1407287", "GSM1407288", "GSM1407289", "GSM1407290", "GSM1407291", "GSM1407292", "GSM1407293", "GSM1407294", "GSM1407295", "GSM1407296", "GSM1407297", "GSM1407298", "GSM1407299", "GSM1407300", "GSM1407301", "GSM1407302", "GSM1407303", "GSM1407304", "GSM1407305", "GSM1407306", "GSM1407307", "GSM1407308", "GSM1407309", "GSM1407310", "GSM1407311", "GSM1407312", "GSM1407313", "GSM1407314", "GSM1407315", "GSM1407316", "GSM1407317", "GSM1407318", "GSM1407319", "GSM1407320", "GSM1407321", "GSM1407322", "GSM1407323", "GSM1407324", "GSM1407325", "GSM1407326", "GSM1407327", "GSM1407328", "GSM1407329", "GSM1407330", "GSM1407331", "GSM1407332", "GSM1407333", "GSM1407334", "GSM1407335", "GSM1407336", "GSM1407337", "GSM1407338", "GSM1407339", "GSM1407340", "GSM1407341", "GSM1407342", "GSM1407343", "GSM1407344", "GSM1407345", "GSM1407346", "GSM1407347", "GSM1407348", "GSM1407349", "GSM1407350", "GSM1407351", "GSM1407352", "GSM1407353", "GSM1407354", "GSM1407355", "GSM1407356"], "sample_count": 175, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene expression in human orbit", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE105149 not downloaded", "asset:data:geo:GSE105149 licence not read", "asset:data:geo:GSE58331 not downloaded", "asset:data:geo:GSE58331 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "ATP2A3 was prioritized using machine learning (ML) approaches, including LASSO, support vector machine (SVM)-RFE, and weighted gene coexpression network analysis (WGCNA)."}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Pathway Enrichment"}}, {"id": "method-3", "family": "genetic_association", "specific": "mendelian_randomisation", "label": "Genetic association (GWAS / linkage / MR)", "dsl_operations": ["mr_estimate"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["genotype_phenotype_table"], "evidence": {"score": 0.7, "band": "medium", "quote": "Mendelian Randomization"}}], "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": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "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": ["GSE58331", "GSE105149"], "resolved_accessions": ["GSE58331", "GSE105149"], "statements": ["The datasets generated during and/or analyzed during the current study are available in the supporting information. The citation guidelines: www.kegg.jp/kegg/kegg1.html."], "basis": "The paper names GSE58331, GSE105149 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:40548296", "data:geo:GSE105149"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE105149"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:54:38Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15266, "completion_tokens": 2065, "cached_tokens": 128}}, "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:40548296", "type": "paper", "label": "pmid:40548296"}, {"id": "source:pmid:40548296", "type": "manuscript", "label": "PMID 40548296 full text"}, {"id": "data:geo:GSE105149", "type": "dataset", "label": "GEO GSE105149"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40548296", "type": "paper", "label": "pmid:40548296"}, {"id": "source:pmid:40548296", "type": "manuscript", "label": "PMID 40548296 full text"}, {"id": "data:geo:GSE105149", "type": "dataset", "label": "GEO GSE105149"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40548296", "type": "extraction", "used": ["source:pmid:40548296"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE105149", "type": "accession_resolution", "used": ["data:geo:GSE105149"], "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:40548296", "parent_hash": "sha256:49698377bb60453c94bb1a3b89c2355f0ea22a5df1d9a00eac50cb7cf82f0443", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE105149 licence not read", "asset:data:geo:GSE105149 not downloaded", "asset:data:geo:GSE58331 licence not read", "asset:data:geo:GSE58331 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["ATP2A3 was prioritized using machine learning (ML) approaches, including LASSO, support vector machine (SVM)-RFE, and weighted gene coexpression network analysis (WGCNA)."], "order_basis": "analysis_only", "id": "assay:1"}]}