Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis
Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen gen...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2023.1190273/full |
| _version_ | 1827383937168572416 |
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| author | Fei Tong Zhijun Sun |
| author_facet | Fei Tong Zhijun Sun |
| author_sort | Fei Tong |
| collection | DOAJ |
| description | Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach.Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA and machine learning algorithms, and the diagnostic accuracy was assessed by the receiver operating characteristic (ROC) curves. GSE41177 was used as the validation set for verification. Subsequently, we identified the specific signaling pathways in which the key biomarkers were involved, using gene set enrichment analysis and reverse prediction of mRNA–miRNA interaction pairs. Finally, we explored the associations between the hub genes and immune microenvironment and immune regulation.Results: Fifty-seven DEGs were identified, and the two hub genes, hypoxia inducible factor 1 subunit alpha inhibitor (HIF1AN) and mitochondrial inner membrane protein MPV17 (MPV17), were screened using WGCNA combined with machine learning algorithms. The areas under the receiver operating characteristic curves for MPV17 and HIF1AN validated that two genes predicted AF development, and the differential expression of the hub genes was verified in the external validation dataset. Enrichment analysis showed that MPV17 and HIF1AN affect mitochondrial dysfunction, oxidative stress, gap junctions, and other signaling pathway functions. Immune cell infiltration and immunomodulatory correlation analyses showed that MPV17 and HIF1AN are strongly correlated with the content of immune cells and significantly correlated with HLA expression.Conclusion: The identification of hub genes associated with AF using WGCNA combined with machine learning algorithms and their correlation with immune cells and immune gene expression can elucidate the molecular mechanisms underlying AF occurrence. This may further identify more accurate and effective biomarkers and therapeutic targets for the diagnosis and treatment of AF. |
| first_indexed | 2024-03-08T14:48:46Z |
| format | Article |
| id | doaj.art-2fdbc323acdb43c0a0d92088c1dc11d2 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-03-08T14:48:46Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-2fdbc323acdb43c0a0d92088c1dc11d22024-01-11T04:52:13ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2024-01-011110.3389/fcell.2023.11902731190273Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysisFei TongZhijun SunBackground: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach.Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA and machine learning algorithms, and the diagnostic accuracy was assessed by the receiver operating characteristic (ROC) curves. GSE41177 was used as the validation set for verification. Subsequently, we identified the specific signaling pathways in which the key biomarkers were involved, using gene set enrichment analysis and reverse prediction of mRNA–miRNA interaction pairs. Finally, we explored the associations between the hub genes and immune microenvironment and immune regulation.Results: Fifty-seven DEGs were identified, and the two hub genes, hypoxia inducible factor 1 subunit alpha inhibitor (HIF1AN) and mitochondrial inner membrane protein MPV17 (MPV17), were screened using WGCNA combined with machine learning algorithms. The areas under the receiver operating characteristic curves for MPV17 and HIF1AN validated that two genes predicted AF development, and the differential expression of the hub genes was verified in the external validation dataset. Enrichment analysis showed that MPV17 and HIF1AN affect mitochondrial dysfunction, oxidative stress, gap junctions, and other signaling pathway functions. Immune cell infiltration and immunomodulatory correlation analyses showed that MPV17 and HIF1AN are strongly correlated with the content of immune cells and significantly correlated with HLA expression.Conclusion: The identification of hub genes associated with AF using WGCNA combined with machine learning algorithms and their correlation with immune cells and immune gene expression can elucidate the molecular mechanisms underlying AF occurrence. This may further identify more accurate and effective biomarkers and therapeutic targets for the diagnosis and treatment of AF.https://www.frontiersin.org/articles/10.3389/fcell.2023.1190273/fullatrial fibrillationbioinformatics analysesMPV17HIF1ANweighted gene co-expression network analysis |
| spellingShingle | Fei Tong Zhijun Sun Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis Frontiers in Cell and Developmental Biology atrial fibrillation bioinformatics analyses MPV17 HIF1AN weighted gene co-expression network analysis |
| title | Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| title_full | Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| title_fullStr | Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| title_full_unstemmed | Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| title_short | Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| title_sort | identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis |
| topic | atrial fibrillation bioinformatics analyses MPV17 HIF1AN weighted gene co-expression network analysis |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2023.1190273/full |
| work_keys_str_mv | AT feitong identificationandvalidationofpotentialbiomarkersforatrialfibrillationbasedonintegratedbioinformaticsanalysis AT zhijunsun identificationandvalidationofpotentialbiomarkersforatrialfibrillationbasedonintegratedbioinformaticsanalysis |