Establishment and validation of diagnostic model in immunoglobulin A nephropathy based on weighted gene co-expression network analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39612439", "kind": "science-record", "revision": 3, "content_hash": "sha256:72c79957f286abddbb5a17a7aca301dec0a68d281510c605de0adc4d4211c95b", "created_at": "2026-11-01T20:29:53Z", "updated_at": "2026-11-01T22:13:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and validation of diagnostic model in immunoglobulin A nephropathy based on weighted gene co-expression network analysis.", "doi": "10.1097/MD.0000000000039930", "pmid": "39612439", "authors": ["Haibo Liu", "Lingling Dai", "Jie Liu", "Kai Duan", "Feng Yi", "Zhuo Li"], "journal": "Medicine", "year": 2024, "abstract": "Bioinformatics analysis helps to understand the underlying mechanisms and adjust diagnostic and treatment strategies for immunoglobulin A nephropathy (IgAN) by screening gene expression datasets. We explored the biological function of IgAN, and established and validated a diagnostic model for IgAN using weighted gene co-expression network analysis. Using the GSE93798 and GSE37460 datasets, we performed differential expression analysis, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein-protein network, and identified hub genes. A diagnostic model was built using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Two Gene Expression Omnibus (GEO) datasets were integrated to screen 38 differentially expressed genes between patients with IgAN and normal kidney donors in glomerular samples. KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the IL-17 and relaxin signaling pathways. We constructed a protein-protein interaction (PPI) network of differentially expressed genes using the STRING database and cross-compared it with the results of weighted gene correlation network analysis to screen out the top 10 key genes: FOS, EGR2, FOSB, NR4A1, BR4A3, FOSL1, NR4A2, ALB, CD53, C3AR1.We also found that the immune infiltration level was remarkably increased in IgAN tissues. We established a 5-gene panel diagnostic model (ACTA2, ALB, AFM, ALDH1L1, and ALDH6A1). The combined diagnostic ability was high, with the area under the curve (AUC) was 0.964. Based on these 5 genes, we also developed a risk-scoring evaluation system for individuals. The calibration plot indicated that the nomogram-predicted probability of nonadherence was highly correlated with actual diagnosed nonadherence, and the decision curve analysis indicated that patients had a relatively good net benefit. The model and gene expression were also validated using an external dataset. Our study provides directions for exploring the potential molecular mechanisms of IgAN as well as diagnostic and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "LASSO regression was performed to identify the model genes, and the optimal number of genes was 5.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39612439", "locator": "sections[17] \"3.5. Establishment and assessment of diagnostic model\" paragraph 2", "quote": "LASSO regression was performed to identify the model genes, and the optimal number of genes was 5."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39612439", "locator": "sections[24] \"fn-group\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined diagnostic ability was high with an AUC of 0.964 (Fig. 5H).", "kind": "quantitative", "target": {"value": 0.964, "unit": "auc", "tolerance": null, "source": "The combined diagnostic ability was high with an AUC of 0.964 (Fig. 5H)."}, "evidence": [{"asset": "source:pmid:39612439", "locator": "sections[17] \"3.5. Establishment and assessment of diagnostic model\" paragraph 2", "quote": "The combined diagnostic ability was high with an AUC of 0.964 (Fig. 5H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The combined diagnostic ability was high with an AUC of 0.964 (Fig. 5H).", "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:39612439"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39612439", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000039930", "sha256": "sha256:52cd42a20e48037b4965beedd6285b541c0fe0e6f59498501ad6296159dae1cf", "bytes": 27968, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1097/MD.0000000000039930", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11608691", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "LASSO regression was performed to identify the model genes, and the optimal number of genes was 5."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-3", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.7, "band": "medium", "quote": "decision curve analysis"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 0.964, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "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": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:39612439"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:40:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8685, "completion_tokens": 1556, "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:39612439", "type": "paper", "label": "pmid:39612439"}, {"id": "source:pmid:39612439", "type": "manuscript", "label": "PMID 39612439 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39612439", "type": "paper", "label": "pmid:39612439"}, {"id": "source:pmid:39612439", "type": "manuscript", "label": "PMID 39612439 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39612439", "type": "extraction", "used": ["source:pmid:39612439"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "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:39612439", "parent_hash": "sha256:4a9cadc40e266c1f282160232d0813b70ca376857166466556389208a6f7efb9", "fork_of": null, "generation": 2}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["LASSO regression was performed to identify the model genes, and the optimal number of genes was 5."], "order_basis": "analysis_only", "id": "assay:1"}]}