Bioinformatics analysis of the expression of potential common genes and immune-related genes between atrial fibrillation and chronic kidney disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40078458", "kind": "science-record", "revision": 4, "content_hash": "sha256:b2a871059539f50e225c1b95e9639ccefadf0261c0a694db693a4a346a63ebdc", "created_at": "2026-11-01T20:30:17Z", "updated_at": "2026-11-01T22:56:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of the expression of potential common genes and immune-related genes between atrial fibrillation and chronic kidney disease.", "doi": "10.3389/fcvm.2025.1521722", "pmid": "40078458", "authors": ["Jieying Teng", "Guoxiong Deng"], "journal": "Frontiers in cardiovascular medicine", "year": 2025, "abstract": "Research objective: This study is based on bioinformatics analysis to explore the co-expressed differentially expressed genes (DEGs) between atrial fibrillation (AF) and chronic kidney disease (CKD), identify the biomarkers for the occurrence and development of the two diseases, investigate the potential connections between AF and CKD, and explore the associations with immune cells.\nMethods: We downloaded Two AF gene chip datasets (GSE79768, GSE14975) and two CKD gene chip datasets (GSE37171, GSE120683) from the GEO database. After pre-processing and standardizing the datasets, two DEGs datasets were obtained. The DEGs were screened using R language, and the intersection was taken through Venn diagrams to obtain the co-expressed DEGs of AF and CKD. To obtain the signal pathways where the co-expressed DEGs were significantly enriched, GO/KEGG enrichment analyses were used to analysis the co-expressed DEGs. The Cytoscape software was used to further construct a PPI network and screen key characteristic genes, and the top 15 co-expressed DEGs were screened through the topological algorithm MCC. To further screen key characteristic genes, two machine-learning algorithms, LASSO regression and RF algorithm, were performed to screen key characteristic genes for the two disease datasets respectively to determine the diagnostic values of the characteristic genes in the two diseases. The GeneMANIA online database and Networkanalyst platform were used to construct gene-gene and TFs-gene interaction network diagrams respectively to predict gene functions and find key transcription factors. Finally, the correlation between key genes and immune cell subtypes was performed by Spearman analysis.\nResearch results: A total of 425 DEGs were screened out from the AF dataset, and 4,128 DEGs were screened out from the CKD dataset. After taking the intersection of the two, 82 co-expressed DEGs were obtained. The results of GO enrichment analysis of DEGs showed that the genes were mainly enriched in biological processes such as secretory granule lumen, blood microparticles, complement binding, and antigen binding. KEGG functional enrichment analysis indicated that the genes were mainly enriched in pathways such as the complement coagulation cascade, systemic lupus erythematosus, and Staphylococcus aureus infection. The top 15 DEGs were obtained through the MCC topological algorithm of Cytoscape software. Subsequently, based on LASSO regression and RF algorithm, the key characteristic genes of the 15 co-expressed DEGs of AF and CKD were further screened, and by taking the intersection through Venn diagrams, five key characteristic genes were finally obtained: PPBP, CXCL1, LRRK2, RGS18, RSAD2. ROC curves were constructed to calculate the area under the curve to verify the diagnostic efficacy of the key characteristic genes for diseases. The results showed that RSAD2 had the highest diagnostic value for AF, and the diagnostic values of PPBP, CXCL1, and RSAD2 for CKD were all at a relatively strong verification level. Based on AUC >0.7, co-expressed key genes with strong diagnostic efficacy were obtained: PPBP, CXCL1, RSAD2. The results of the GeneMANIA online database showed that the two biomarkers, BBPB and CXCL1, mainly had functional interactions with cytokine activity, chemokine receptor activity, cell response to chemokines, neutrophil migration, response to chemokines, granulocyte chemotaxis, and granulocyte migration. The TFs-gene regulatory network identified FOXC1, FOXL1, and GATA2 as the main transcription factors of the key characteristic genes. Finally, through immune infiltration analysis, the results indicated that there were various immune cell infiltrations in the development processes of AF and CKD.\nResearch conclusion: PPBP, CXCL1, and RSAD2 are key genes closely related to the occurrence and development processes between AF and CKD. Among them, the CXCLs/CXCR signaling pathway play a crucial role in the development processes of the two diseases likely. In addition, FOXC1, FOXL1, and GATA2 may be potential therapeutic targets for AF combined with CKD, and the development of the diseases is closely related to immune cell infiltration.", "claims": [{"id": "claim-method", "statement": "Use the “limma” package in R software to conduct differential expression analysis on the gene expression profile matrices of AF and CKD respectively. Sequentially, through constructing a comparison matrix, fitting a linear model, and performing Bayesian testing, all the differentially expressed genes (DEGs) of the two diseases are finally obtained.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40078458", "locator": "sections[4] \"Preliminary data processing and DEGs screening\" paragraph 2", "quote": "Use the “limma” package in R software to conduct differential expression analysis on the gene expression profile matrices of AF and CKD respectively. Sequentially, through constructing a comparison matrix, fitting a linear model, and performing Bayesian testing, all the differentially expressed genes (DEGs) of the two diseases are finally obtained."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are publicly available. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc, accession numbers: GSE79768, GSE37171, GSE120683, GSE14975.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40078458", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are publicly available. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc, accession numbers: GSE79768, GSE37171, GSE120683, GSE14975."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PPBP, CXCL1, and RSAD2 are key genes closely related to the occurrence and development processes between AF and CKD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40078458", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "PPBP, CXCL1, and RSAD2 are key genes closely related to the occurrence and development processes between AF and CKD."}, {"asset": "source:pmid:40078458", "locator": "sections[12] \"Screening and identification of DEGs of the two diseases\" paragraph 2", "quote": "82 co expressed DEGs were obtained by taking the intersection of the DEGs of the two diseases through Venn diagrams (Figure 4A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "PPBP, CXCL1, and RSAD2 are key genes closely related to the occurrence and development processes between AF and CKD.", "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:40078458"], "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:GSE120683"], "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:GSE120683"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40078458", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2025.1521722", "sha256": "sha256:a40efd426dce7b39e486e780a11801498a67a11eba8f14465a7991dc3f4ca312", "bytes": 64590, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2025.1521722", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11897265", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE120683", "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=GSE120683", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE120683", "version": null, "note": "The original contributions presented in the study are publicly available. 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Sequentially, through constructing a comparison matrix, fitting a linear model, and performing Bayesian testing, all the differentially expressed genes (DEGs) of the two diseases are finally obtained."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-3", "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": "PPI"}}, {"id": "method-4", "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": "GO enrichment"}}], "targets": [], "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": "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. 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