Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

Cao, Jing; Liu, Zhaoya; Liu, Jie; Li, Chan; Zhang, Guogang; Shi, Ruizheng

doi:10.2147/ijgm.s329980

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…In our study, SERPINA3 and FCN3 were down-regulated in bioinformatics analysis, the external validation data, and the functional experiment of the ICM rat. Jing et al ( 40 ) found that SERPINA3 and FCN3 were identified as the potential biomarkers in ICM using bioinformatics analysis. The validation results of SERPINA3 and FCN3 in myocardial tissue of ICM patients were consistent with the results of our study.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Wang

Xie

Cheng

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveInflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.Materials and methodsThe microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).Results64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.ConclusionSERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

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Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Wang

Xie

Cheng

et al. 2022

Front. Cardiovasc. Med.

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“…[ 18 ] The expression of MXRA5 mRNA has been observed to be upregulated in cardiac tissue in ischemic cardiomyopathy, and may play an important role in the development and progression of HF. [ 19 ] The MAPK pathway plays a key role in the regulation of cell proliferation, survival, differentiation and apoptosis, and its activation promotes myocardial fibrosis and reduces cardiac function in HF rats. [ 20 ] It has been shown that MXRA5 is closely related to the MAPK pathway, and that MXRA5 promotes cell proliferation and apoptosis through the MAPK pathway.…”

Section: Discussionmentioning

confidence: 99%

Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network

Boyang

Yiping

et al. 2022

Medicine

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Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an artificial neural network and validated its diagnostic efficacy in a public dataset. We calculated heart failure-related differentially expressed genes and obtained 24 candidate genes by random forest classification, and selected the top 6 genes as important genes for subsequent analysis. The prediction weights of the genes of interest were determined by the neural network model and the model scores were evaluated in 2 independent sample datasets (GSE16499 and GSE57338 datasets). Since the weights of RNA-seq predictions for constructing neural network models were theoretically more suitable for disease classification of RNA-seq data, the GSE57338 dataset had the best performance in the validation results. The diagnostic model derived from our study can be of clinical value in determining the likelihood of HF occurring through cardiac biopsy. In the meantime, we need to further investigate the accuracy of the diagnostic model based on the results of our study.

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“…The Database for Metascape ( https://metascape.org/gp/index.html ) is applied to achieve the aim of performing KEGG pathway enrichment analysis and GO, which provides a biologist-oriented resource for the investigation of systems-level datasets. Enriched GO terms and pathways are dealed by utilizing the WeiShengXin tools ( https://www.bioinformatics.com.cn ) which is an online platform free of cost for database analyzing [ 46 – 49 ] (Figures 7 and 8 ).…”

Section: Methodsmentioning

confidence: 99%

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach

Lang

Yang

Cai

et al. 2022

Computational Intelligence and Neuroscience

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Objectives. The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology. Methods. All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results. The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers. Conclusions. That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

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Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

Cited by 15 publications

References 35 publications

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Construction and analysis of heart failure diagnosis model based on random forest and artificial neural network

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach

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