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pmid:35480323
Biological Characteristics and Predictive Model of Biopsy-Proven Acute Rejection (BPAR) After Kidney Transplantation: Evidences of Multi-Omics Analysis.
Qianguang Han · Xiang Zhang · Xiaohan Ren · Zhou Hang · Yu Yin · Zijie Wang · Hao Chen · Li Sun · Jun Tao · Zhijian Han · Ruoyun Tan · Min Gu · Xiaobing Ju
Frontiers in genetics · 2022 doi:10.3389/fgene.2022.844709
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Complete file
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In this study, we are aimed to explore the biological characteristics of biopsy-proven acute rejection (BPAR) and establish a predictive model. Methods: Gene expression matrix of the renal allograft samples in the GEO database were screened and included, using Limma R package to identify differentially expressed transcripts between BPAR and No-BPAR groups. Then a predictive model of BPAR was established based on logistic regression of which key transcripts involved in the predictive model were further explored using functional enrichment analyses including Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Results: A total of four studies (GSE129166, GSE48581, GSE36059, and GSE98320) were included for extensive analysis of differential expression. 32 differential expressed transcripts were observed to be significant between two groups after the pooled analysis. Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902-0.939). Results of functional enrichment analysis showed that The functions of differential genes are mainly manifested in chemokine receptor binding, chemokine activity, G protein-coupled receptor binding, etc. while the immune infiltration analysis indicated that immune cells mainly related to acute rejection include Macrophages. M1, T cells gamma delta, T cells CD4 memory activated, eosinophils, etc. Conclusion: We have identified a total of 32 differential expressed transcripts and based on that, a predictive model with five significant transcripts was established, which was suggested as a highly recommended tool for the prediction of BPAR after kidney transplantation. However, an extensive study should be performed for the evaluation of the predictive model and mechanism involved.", "claims": [{"id": "claim-method", "statement": "The differential genes in the data set are extracted by us, through the “limma” package in the R language, with logFoldChange = 0.5, adjustP = 0.05 as the filter value, then take the intersection of the differential genes of these four gene sets, and find the common differential genes for the next step of the analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35480323", "locator": "sections[4] \"Differential Gene Analysis\" paragraph 2", "quote": "The differential genes in the data set are extracted by us, through the “limma” package in the R language, with logFoldChange = 0.5, adjustP = 0.05 as the filter value, then take the intersection of the differential genes of these four gene sets, and find the common differential genes for the next step of the analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35480323", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a five-transcript predictive model for biopsy-proven acute rejection after kidney transplantation with an AUC of 0.919 (95% CI 0.902–0.939).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902–0.939)."}, "evidence": [{"asset": "source:pmid:35480323", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902–0.939)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a five-transcript predictive model for biopsy-proven acute rejection after kidney transplantation with an AUC of 0.919 (95% CI 0.902–0.939).", "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:35480323"], "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. 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"sample_count": 1208, "group_sizes": null, "group_sizes_basis": null, "platform": "15207", "taxon": "Homo sapiens", "title": "Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE129166 not downloaded", "asset:data:geo:GSE129166 licence not read", "asset:data:geo:GSE36059 not downloaded", "asset:data:geo:GSE36059 licence not read", "asset:data:geo:GSE48581 not downloaded", "asset:data:geo:GSE48581 licence not read", "asset:data:geo:GSE98320 not downloaded", "asset:data:geo:GSE98320 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", 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"expected_value": 95.0, "unit": "per cent", "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."}, {"id": "target-published-estimate-auc", "claim_id": "claim-target-result", "operation": "differential_expression_fit", "expected_value": 0.919, "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": []}, "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": ["GSE129166", "GSE48581", "GSE36059", "GSE98320"], "resolved_accessions": ["GSE129166", "GSE48581", "GSE36059", "GSE98320"], "statements": ["The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE129166, GSE48581, GSE36059, GSE98320 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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:35480323", "data:geo:GSE129166"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE129166"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:44:02Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12409, "completion_tokens": 1906, "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:35480323", "type": "paper", "label": "pmid:35480323"}, {"id": "source:pmid:35480323", "type": "manuscript", "label": "PMID 35480323 full text"}, {"id": "data:geo:GSE129166", "type": "dataset", "label": "GEO GSE129166"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35480323", "type": "paper", "label": "pmid:35480323"}, {"id": "source:pmid:35480323", "type": "manuscript", "label": "PMID 35480323 full text"}, {"id": "data:geo:GSE129166", "type": "dataset", "label": "GEO GSE129166"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35480323", "type": "extraction", "used": ["source:pmid:35480323"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE129166", "type": "accession_resolution", "used": ["data:geo:GSE129166"], "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:35480323", "parent_hash": "sha256:df85a3a300bad2aeace76c83425db68c7bd717bde07a0fb9166183bf57a92a98", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE129166 licence not read", "asset:data:geo:GSE129166 not downloaded", "asset:data:geo:GSE36059 licence not read", "asset:data:geo:GSE36059 not downloaded", "asset:data:geo:GSE48581 licence not read", "asset:data:geo:GSE48581 not downloaded", "asset:data:geo:GSE98320 licence not read", "asset:data:geo:GSE98320 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": ["The differential genes in the data set are extracted by us, through the “limma” package in the R language, with logFoldChange = 0.5, adjustP = 0.05 as the filter value, then take the intersection of the differential genes of these four gene sets, and find the common differential genes for the next step of the analysis."], "order_basis": "analysis_only", "id": "assay:1"}]}