Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39877037", "kind": "science-record", "revision": 4, "content_hash": "sha256:be621eb30b7258103c748327f908225f8bf907d489711a2d6cd4631c07c457c4", "created_at": "2026-11-01T20:30:09Z", "updated_at": "2026-11-01T22:56:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis.", "doi": "10.1016/j.bbrep.2024.101911", "pmid": "39877037", "authors": ["Can Hou", "Jiayi Xu", "Min Zhou", "Junyu Huo", "Xiaofei Wang", "Wanying Jiang", "Tong Su", "Hui Wang", "Fang Jia"], "journal": "Biochemistry and biophysics reports", "year": 2025, "abstract": "Background: Previous research has established that chronic kidney disease (CKD) and heart failure with preserved ejection fraction (HFpEF) often coexist. Although we have a preliminary understanding of the potential correlation between HFpEF and CKD, the underlying pathophysiological mechanisms remain unclear. This study aimed to elucidate the molecular mechanisms associated with CKD and HFpEF through bioinformatics analysis.\nMethods: Datasets for HFpEF and CKD were obtained from the Gene Expression Omnibus (GEO) database. The R software package \"limma\" was employed to conduct differential expression analysis. Functional annotation was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We conducted weighted gene co-expression network analysis (WGCNA), correlation analysis with autophagy, ferroptosis, and immune-related processes, as well as transcriptional regulation analysis, immune infiltration analysis, and diagnostic performance evaluation. Finally, the diagnostic potential of the identified hub genes for CKD and HFpEF was assessed using ROC curve analysis (GSE37171).\nResults: Differential expression analysis revealed 58 overlapping genes, comprised of 40 up-regulated and 18 down-regulated genes. Both GO and KEGG analyses indicated enriched pathways relevant to both disorders. WGCNA identified 4086 genes associated with CKD. Further comparison with differentially expressed genes (DEGs) identified three hub genes (KLF4, SCD, and SEL1L3) that were linked to autophagy, ferroptosis, and immune processes in both conditions. Additionally, a miRNA-mRNA regulatory network involving 376 miRNAs and 12 transcription factors (TFs) was constructed. ROC curve analysis was performed to evaluate the diagnostic utility of the hub genes for CKD and HFpEF.\nConclusion: This study elucidated shared pathogenic mechanisms and identified diagnostic markers common to both HFpEF and CKD. The identified hub genes show promise as potential tools for early diagnosis and treatment strategies for these conditions.", "claims": [{"id": "claim-method", "statement": "To identify the DEGs between CKD and HFpEF, we utilized the R package “limma (version 3.60.6)” [15,16].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39877037", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "To identify the DEGs between CKD and HFpEF, we utilized the R package “limma (version 3.60.6)” [15,16]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories.\nThe data that has been used is confidential.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39877037", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories."}, {"asset": "source:pmid:39877037", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The data that has been used is confidential."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC values for KLF4, SCD, and SEL1L3 were 0.917, 0.662, and 0.907, respectively (Fig. 8B–D).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39877037", "locator": "sections[19] \"ROC curve analysis for diagnostic efficacy in CKD and HFpEF\" paragraph 2", "quote": "The AUC values for KLF4, SCD, and SEL1L3 were 0.917, 0.662, and 0.907, respectively (Fig. 8B–D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC values for KLF4, SCD, and SEL1L3 were 0.917, 0.662, and 0.907, respectively (Fig. 8B–D).", "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:39877037"], "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:GSE175759"], "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:GSE175759"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39877037", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.bbrep.2024.101911", "sha256": 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