Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis
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 rema...
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Elsevier
2025-03-01
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| Series: | Biochemistry and Biophysics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405580824002759 |
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| author | Can Hou Jiayi Xu Min Zhou Junyu Huo Xiaofei Wang Wanying Jiang Tong Su Hui Wang Fang Jia |
| author_facet | Can Hou Jiayi Xu Min Zhou Junyu Huo Xiaofei Wang Wanying Jiang Tong Su Hui Wang Fang Jia |
| author_sort | Can Hou |
| collection | DOAJ |
| description | 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. Methods: 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). Results: 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. Conclusion: 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. |
| format | Article |
| id | doaj-art-081b34de2beb43d7a88f573b5f6529be |
| institution | OA Journals |
| issn | 2405-5808 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Biochemistry and Biophysics Reports |
| spelling | doaj-art-081b34de2beb43d7a88f573b5f6529be2025-08-20T02:03:07ZengElsevierBiochemistry and Biophysics Reports2405-58082025-03-014110191110.1016/j.bbrep.2024.101911Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysisCan Hou0Jiayi Xu1Min Zhou2Junyu Huo3Xiaofei Wang4Wanying Jiang5Tong Su6Hui Wang7Fang Jia8Department of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiology, Changzhou Hospital of Traditional Chinese Medicine, Changzhou Hospital Affiliated to Nanjing University of Chinese Medicine, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, ChinaDepartment of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, China; Corresponding author.Department of Cardiovascular Medicine, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, 213000, Changzhou, Jiangsu Province, China; Corresponding author.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. Methods: 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). Results: 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. Conclusion: 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.http://www.sciencedirect.com/science/article/pii/S2405580824002759Heart failure with preserved ejection fraction (HFpEF)Chronic kidney disease (CKD)BioinformaticsImmune infiltration |
| spellingShingle | Can Hou Jiayi Xu Min Zhou Junyu Huo Xiaofei Wang Wanying Jiang Tong Su Hui Wang Fang Jia Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis Biochemistry and Biophysics Reports Heart failure with preserved ejection fraction (HFpEF) Chronic kidney disease (CKD) Bioinformatics Immune infiltration |
| title | Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| title_full | Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| title_fullStr | Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| title_full_unstemmed | Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| title_short | Screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| title_sort | screening of biomarkers for diagnosing chronic kidney disease and heart failure with preserved ejection fraction through bioinformatics analysis |
| topic | Heart failure with preserved ejection fraction (HFpEF) Chronic kidney disease (CKD) Bioinformatics Immune infiltration |
| url | http://www.sciencedirect.com/science/article/pii/S2405580824002759 |
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