Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction

Yao, Yinhui; Zhao, Jingyi; Zhou, Xiaohui; Hu, Junhui; Wang, Ying

doi:10.1080/21655979.2021.1938498

Cited by 16 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, we used the LASSO regression and SVM-RFE algorithm to screen for the four genes. SVM-RFE is a powerful feature selection algorithm [ 18 ] that has been used in the bioinformatics research of cardiovascular diseases [ 14 ], tumours [ 19 ], and Alzheimer's disease [ 20 ]. When there are many features, SVM-RFE is a good choice to avoid overfitting.…”

Section: Discussionmentioning

confidence: 99%

“…The genes obtained by LASSO and SVM-RFE were intersected to obtain a diagnosis-related gene signature set associated with paediatric sepsis. The receiver operating characteristic (ROC) curve, C-index, and principal component analysis (PCA) were used to evaluate the diagnostic value of the gene signatures [ 13 , 14 ]. Further, “ROCR,” “Hmisc,” and “ggplot2” packages were used by ROC, C index, and PCA, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Identification of a Four‐Gene Signature for Diagnosing Paediatric Sepsis

Yao

Zhao

et al. 2022

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Aim. Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database. Methods. We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis. Results. A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively. Conclusion. The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

[Retracted] Identification of a Four‐Gene Signature for Diagnosing Paediatric Sepsis

Yao

Zhao

et al. 2022

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, LASSO logistic regression and SVM-RFE were used to select the most significant characteristic genes in this study. The subset intersection of the genes screened was also used to screen feature genes using multivariate logistic regression, with the criterion of p < 0.05 [ 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Chen

Zhao

et al. 2022

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Aims. This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis. Methods. Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined. Results. A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways. Conclusion. This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

show abstract

“…Based on the gene expression profiling data of AMI, Bowers’ LAPP method [ 14 ] was used to construct a first-order logic network (also known as a directed network). Using NCCM, 17 differential control capability genes (DCCGs) related to AMI were identified, 14 of which ( NR4A3 [ 15 ], THBS1 [ 16 , 17 ], CXCL3 [ 18 ], ITLN1 [ 19 , 20 ], CLEC4D [ 21 ], LRG1 [ 22 ], IRAK3 [ 23 , 24 ], HBEGF [ 25 ], MMP9 [ 26 , 27 ], NLRP3 [ 28 , 29 , 30 ], EDN1 [ 31 ], VNN3 [ 24 ], and PDK4 [ 32 ]) were significantly related to the growth, proliferation, and repair of AMI/MI cells, and BCL6 [ 33 ] was indirectly related. The enrichment analysis of DCCGs showed that AMI was significantly related to the positive regulation of smooth muscle cell proliferation and the regulation of cytokine production involved in the immune response.…”

Section: Introductionmentioning

confidence: 99%

Identifying Genes Related to Acute Myocardial Infarction Based on Network Control Capability

Wang

Xian

2022

Genes

View full text Add to dashboard Cite

Identifying genes significantly related to diseases is a focus in the study of disease mechanisms. In this paper, from the perspective of integrated analysis and dynamic control, a method for identifying genes significantly related to diseases based on logic networks constructed by the LAPP method, referred to as NCCM, is proposed and applied to the study of the mechanism of acute myocardial infarction (AMI). It is found that 82.35% of 17 differential control capability genes (DCCGs) identified by NCCM are significantly correlated with AMI/MI in the literature and DISEASES database. The enrichment analysis of DCCGs shows that AMI is closely related to the positive regulation of vascular-associated smooth muscle cell proliferation and regulation of cytokine production involved in the immune response, in which HBEGF, THBS1, NR4A3, NLRP3, EDN1, and MMP9 play a crucial role. In addition, although the expression levels of CNOT6L and ACYP1 are not significantly different between the control group and the AMI group, NCCM shows that they are significantly associated with AMI. Although this result still needs further verification, it shows that the method can not only identify genes with large differences in expression but also identify genes that are associated with diseases but with small changes in expression.

show abstract

Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction

Cited by 16 publications

References 44 publications

[Retracted] Identification of a Four‐Gene Signature for Diagnosing Paediatric Sepsis

[Retracted] Identification of a Four‐Gene Signature for Diagnosing Paediatric Sepsis

[Retracted] Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis

Identifying Genes Related to Acute Myocardial Infarction Based on Network Control Capability

Contact Info

Product

Resources

About