Identification of feature autophagy-related genes in patients with acute myocardial infarction based on bioinformatics analyses

Du, Yajuan; Zhao, Enfa; Zhang, Yushun

doi:10.1042/bsr20200790

Cited by 5 publications

(6 citation statements)

References 36 publications

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“…The FoxO transcription factors are regulated by upstream signaling molecules such as PI3K, AMP-kinase or SIRT genes, and regulate the downstream transcription of proteins such as calcineurin, muscle RING finger1, muscle atrophy F-box, pyruvate dehydrogenase kinase 4 and coactivator-1α [ 16 ]. This has a direct impact on the regulation of protein synthesis, apoptosis and autophagy, finally leading to structural changes of the myocardium, such as MF [ 17 , 18 ]. We found a significantly upregulated expression of both upstream and downstream interaction partners of FoxO molecular effectors in these MF models, confirming the important role of FoxO signaling in the development and progression of MF.…”

Section: Discussionmentioning

confidence: 99%

Identification of Gene Expression Signatures for Phenotype-Specific Drug Targeting of Cardiac Fibrosis

Lukovic

Hašimbegović

Winkler

et al. 2023

IJMS

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We have designed translational animal models to investigate cardiac profibrotic gene signatures. Domestic pigs were treated with cardiotoxic drugs (doxorubicin, DOX, n = 5 or Myocet®, MYO, n = 5) to induce replacement fibrosis via cardiotoxicity. Reactive interstitial fibrosis was triggered by LV pressure overload by artificial isthmus stenosis with stepwise developing myocardial hypertrophy and final fibrosis (Hyper, n = 3) or by LV volume overload in the adverse remodeled LV after myocardial infarction (RemoLV, n = 3). Sham interventions served as controls and healthy animals (Control, n = 3) served as a reference in sequencing study. Myocardial samples from the LV of each group were subjected to RNA sequencing. RNA-seq analysis revealed a clear distinction between the transcriptomes of myocardial fibrosis (MF) models. Cardiotoxic drugs activated the TNF-alpha and adrenergic signaling pathways. Pressure or volume overload led to the activation of FoxO pathway. Significant upregulation of pathway components enabled the identification of potential drug candidates used for the treatment of heart failure, such as ACE inhibitors, ARB, ß-blockers, statins and diuretics specific to the distinct MF models. We identified candidate drugs in the groups of channel blockers, thiostrepton that targets the FOXM1-regulated ACE conversion to ACE2, tyrosine kinases or peroxisome proliferator-activated receptor inhibitors. Our study identified different gene targets involved in the development of distinct preclinical MF protocols enabling tailoring expression signature-based approach for the treatment of MF.

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Section: Discussionmentioning

confidence: 99%

Identification of Gene Expression Signatures for Phenotype-Specific Drug Targeting of Cardiac Fibrosis

Lukovic

Hašimbegović

Winkler

et al. 2023

IJMS

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“…ARGs have been shown to be of great value in the diagnosis and/or prognostic evaluation of diseases such as cancer, dilated cardiomyopathy, acute myocardial infarction, diabetic nephropathy, pulmonary hypertension, dermatomyositis, and rheumatoid arthritis ( Du et al, 2020 ; Tong et al, 2021 ; Bai et al, 2022 ; Fan et al, 2022 ; Wang et al, 2022 ; Yang et al, 2022 ; Zhang et al, 2022 ). Therefore, ROC curve and survival analysis were performed on key genes to explore their diagnostic efficacy and prognostic value in sepsis.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures

Yang,

Zhou,

et al. 2023

Front. Cell Dev. Biol.

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Background: Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers.Methods: Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR.Results: 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (p-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4+ T cell, CD8+ T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8+ T cells, resting memory CD4+ T cells, M1 macrophages and naive B cells.Conclusion: We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis.

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“…Bo's group identified functional variants of autophagy-related genes, for instance, ATG5 [ 40 ], ATG7 [ 41 ], ATG16L1 [ 42 ], and LC3B [ 43 ] in AMI patients. It has been identified that 7 DEARGs (WDFY3, TP53INP2, GABARAPL1, CDKN1A, DDIT3, NAMPT, and FOS) in AMI based on a feature selection algorithm, known as support vector machine-recursive feature elimination (SVM-RFE), which can be used to diagnose AMI as a potential biological marker [ 44 ]. On the other hand, it has been demonstrated in numerous studies that immune cells play an essential function in the development of AMI and postinfarction cardiac repair and remodeling [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis and Validation of Autophagy-Related Genes and Immune Infiltration in Acute Myocardial Infarction

Ding

Wang

Guo

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Acute myocardial infarction (AMI) is one of the most critical conditions of coronary heart disease with many uncertainties regarding reduction of ischemia/reperfusion injury, medical treatment strategies, and other aspects. The inflammatory immune response has a bidirectional regulatory role in AMI and plays an essential role in myocardial remodeling after AMI. The purpose of our research was tantamount to explore possible mechanisms of AMI and to analyze the relationship with the immune microenvironment. Methods. We firstly analyzed the expression profile of GSE61144 and HADb to identify differentially expressed autophagy-related genes (DEARGs). Then, we performed GO, functional enrichment analysis, and constructed PPI network by Metascape. A lncRNA-miRNA-mRNA ceRNA network was built, and hub genes were extracted by Cytoscape. After that, we used CIBERSORT algorithm to estimate the proportion of immunocytes, followed by correlation analysis to find relationships between hub DEARGs and immunocyte subsets. Finally, we verified those hub genes in another dataset and cellular experiments qPCR. Results. Compared with controls, we identified 44 DEARGs and then filtered the genes of MCODE by constructing PPI network for further analysis. A total of 45 lncRNAs, 24 miRNAs, 19 mRNAs, 162 lncRNA-miRNA pairs, and 37 mRNA-miRNA pairs were used to construct a ceRNA network, and 4 hub DEARGs (BCL2, MAPK1, RAF1, and PRKAR1A) were extracted. We then estimated 5 classes of immunocytes that differed between AMI and controls. According to the results of correlation analysis, these 4 hub DEARGs may play modulatory effects in immune infiltrating cells, notably in CD8+ T cells and neutrophils. Finally, the same results were verified in GSE60993 and qPCR experiments. Conclusion. Our findings suggest that those hub DEARGs (BCL2, MAPK1, RAF1, and PRKAR1A) and immunocytes probably play functions in the progression of AMI, providing potential diagnostic markers and new perspectives for treatment of AMI.

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Identification of feature autophagy-related genes in patients with acute myocardial infarction based on bioinformatics analyses

Cited by 5 publications

References 36 publications

Identification of Gene Expression Signatures for Phenotype-Specific Drug Targeting of Cardiac Fibrosis

Identification of Gene Expression Signatures for Phenotype-Specific Drug Targeting of Cardiac Fibrosis

Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures

Integrated Analysis and Validation of Autophagy-Related Genes and Immune Infiltration in Acute Myocardial Infarction

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