The diagnostic value investigation of programmed cell death genes in heart failure
Abstract Background We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF). Methods Three HF gene expression data were extracted from the GEO database, including GSE57345 (traini...
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BMC
2024-11-01
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| Series: | BMC Cardiovascular Disorders |
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| Online Access: | https://doi.org/10.1186/s12872-024-04343-7 |
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| author | Qiuyue Chen Su Tu |
| author_facet | Qiuyue Chen Su Tu |
| author_sort | Qiuyue Chen |
| collection | DOAJ |
| description | Abstract Background We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF). Methods Three HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated. Results Twenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis. Conclusions This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers. |
| format | Article |
| id | doaj-art-71618092ccee4b63bda78ba14a30a9f9 |
| institution | Kabale University |
| issn | 1471-2261 |
| language | English |
| publishDate | 2024-11-01 |
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| series | BMC Cardiovascular Disorders |
| spelling | doaj-art-71618092ccee4b63bda78ba14a30a9f92024-11-24T12:09:46ZengBMCBMC Cardiovascular Disorders1471-22612024-11-0124111610.1186/s12872-024-04343-7The diagnostic value investigation of programmed cell death genes in heart failureQiuyue Chen0Su Tu1Department of Emergency, Jiangnan University Medical Center, JUMCDepartment of Emergency, Jiangnan University Medical Center, JUMCAbstract Background We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF). Methods Three HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated. Results Twenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis. Conclusions This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.https://doi.org/10.1186/s12872-024-04343-7Heart failureProgrammed cell death related geneDiagnostic markerDiagnostic modelTranscription regulation network |
| spellingShingle | Qiuyue Chen Su Tu The diagnostic value investigation of programmed cell death genes in heart failure BMC Cardiovascular Disorders Heart failure Programmed cell death related gene Diagnostic marker Diagnostic model Transcription regulation network |
| title | The diagnostic value investigation of programmed cell death genes in heart failure |
| title_full | The diagnostic value investigation of programmed cell death genes in heart failure |
| title_fullStr | The diagnostic value investigation of programmed cell death genes in heart failure |
| title_full_unstemmed | The diagnostic value investigation of programmed cell death genes in heart failure |
| title_short | The diagnostic value investigation of programmed cell death genes in heart failure |
| title_sort | diagnostic value investigation of programmed cell death genes in heart failure |
| topic | Heart failure Programmed cell death related gene Diagnostic marker Diagnostic model Transcription regulation network |
| url | https://doi.org/10.1186/s12872-024-04343-7 |
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