Screening and Bioinformatics Analysis of Crucial Gene of Heart Failure and Atrial Fibrillation Based on GEO Database

Zhuang, Yuansong; Qiao, Zhentao; Bi, Xuanye; Han, Dongjian; Jiang, Qingjiao; Zhang, Yi; Wang, Fuhang; Liu, Miaomiao; An, Quanxu; Shangguan, Jiahong; Shen, Deliang

doi:10.3390/medicina58101319

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Zhuang et al found that five genes, FRZB , SFRP4 , ETNPPL , AQP4 , and C1orf105 , were highly co‐expressed in AF and HF patients through transcriptome data analysis. 22 In this study, we found four new biomarkers highly correlated with AF and HF, namely, GLUL , NCF2 , S100A12 , and SRGN , which is different from the results of Zhuang et al Proteins encoded by GLUL belong to the glutamine synthase family and play a role in acid–base homeostasis, cell signalling, and cell proliferation. The DEGs associated with HF identified by Boyang et al through analysis of the Omnibus database included GLUL .…”

Section: Discussioncontrasting

confidence: 99%

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

Zhang,

Ding,

et al. 2024

ESC Heart Failure

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AimsAtrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co‐existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies.Methods and resultsThe training set and validation set data of AF and HF patient samples were downloaded from the GEO database, ‘limma’ was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R‐packet ‘CIBERSORT’. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r = 0.21, P value = 0.09) and MEbrown (r = 0.62, P value = 8e‐12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents.ConclusionsWe identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

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Section: Discussioncontrasting

confidence: 99%

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

Zhang,

Ding,

et al. 2024

ESC Heart Failure

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“…The enrichment of the functions of the selected DEGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis with the cluster profiler package in R ( 22 ).…”

Section: Methodsmentioning

confidence: 99%

Heart failure–related genes associated with oxidative stress and the immune landscape in lung cancer

Duan

et al. 2023

Front. Immunol.

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BackgroundLung cancer is a common comorbidity of heart failure (HF). The early identification of the risk factors for lung cancer in patients with HF is crucial to early diagnosis and prognosis. Furthermore, oxidative stress and immune responses are the two critical biological processes shared by HF and lung cancer. Therefore, our study aimed to select the core genes in HF and then investigate the potential mechanisms underlying HF and lung cancer, including oxidative stress and immune responses through the selected genes.MethodsDifferentially expressed genes (DEGs) were analyzed for HF using datasets extracted from the Gene Expression Omnibus database. Functional enrichment analysis was subsequently performed. Next, weighted gene co-expression network analysis was performed to select the core gene modules. Support vector machine models, the random forest method, and the least absolute shrinkage and selection operator (LASSO) algorithm were applied to construct a multigene signature. The diagnostic values of the signature genes were measured using receiver operating characteristic curves. Functional analysis of the signature genes and immune landscape was performed using single-sample gene set enrichment analysis. Finally, the oxidative stress–related genes in these signature genes were identified and validated in vitro in lung cancer cell lines.ResultsThe DEGs in the GSE57338 dataset were screened, and this dataset was then clustered into six modules using weighted gene co-expression network analysis; MEblue was significantly associated with HF (cor = −0.72, p < 0.001). Signature genes including extracellular matrix protein 2 (ECM2), methyltransferase-like 7B (METTL7B), meiosis-specific nuclear structural 1 (MNS1), and secreted frizzled-related protein 4 (SFRP4) were selected using support vector machine models, the LASSO algorithm, and the random forest method. The respective areas under the curve of the receiver operating characteristic curves of ECM2, METTL7B, MNS1, and SFRP4 were 0.939, 0.854, 0.941, and 0.926, respectively. Single-sample gene set enrichment analysis revealed significant differences in the immune landscape of the patients with HF and healthy subjects. Functional analysis also suggested that these signature genes may be involved in oxidative stress. In particular, METTL7B was highly expressed in lung cancer cell lines. Meanwhile, the correlation between METTL7B and oxidative stress was further verified using flow cytometry.ConclusionWe identified that ECM2, METTL7B, MNS1, and SFRP4 exhibit remarkable diagnostic performance in patients with HF. Of note, METTL7B may be involved in the co-occurrence of HF and lung cancer by affecting the oxidative stress immune responses.

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Molecular remodeling in comorbidities associated with heart failure: a current update

Appunni,

Rubens,

Ramamoorthy

et al. 2024

Mol Biol Rep

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Recent advances in genomics and proteomics have helped in understanding the molecular mechanisms and pathways of comorbidities and heart failure. In this narrative review, we reviewed molecular alterations in common comorbidities associated with heart failure such as obesity, diabetes mellitus, systemic hypertension, pulmonary hypertension, coronary artery disease, hypercholesteremia and lipoprotein abnormalities, chronic kidney disease, and atrial fibrillation. We searched the electronic databases, PubMed, Ovid, EMBASE, Google Scholar, CINAHL, and PhysioNet for articles without time restriction. Although the association between comorbidities and heart failure is already well established, recent studies have explored the molecular pathways in much detail. These molecular pathways demonstrate how novels drugs for heart failure works with respect to the pathways associated with comorbidities. Understanding the altered molecular milieu in heart failure and associated comorbidities could help to develop newer medications and targeted therapies that incorporate these molecular alterations as well as key molecular variations across individuals to improve therapeutic outcomes. The molecular alterations described in this study could be targeted for novel and personalized therapeutic approaches in the future. This knowledge is also critical for developing precision medicine strategies to improve the outcomes for patients living with these conditions.

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Screening and Bioinformatics Analysis of Crucial Gene of Heart Failure and Atrial Fibrillation Based on GEO Database

Cited by 4 publications

References 37 publications

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

Heart failure–related genes associated with oxidative stress and the immune landscape in lung cancer

Molecular remodeling in comorbidities associated with heart failure: a current update

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