Integrative analyses of biomarkers and pathways for heart failure

Fan, Shaowei; Hu, Yuanhui

doi:10.1186/s12920-022-01221-z

Cited by 20 publications

(12 citation statements)

References 80 publications

(66 reference statements)

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“…Heart failure (HF) is a common type of cardiovascular disease, and its morbidity and mortality are increasing yearly ( Castiglione et al, 2022 ). The pathogenesis of HF involves multiple risk factors, especially diabetes, acute myocardial infarction, hypertension, and coronary heart disease ( Fan and Hu, 2022 ). Despite significant improvements in disease diagnosis and treatment, the prognosis of patients with HF remains poor ( Gomes et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Chen

Zhang

et al. 2022

Front. Genet.

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Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.

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Section: Introductionmentioning

confidence: 99%

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Chen

Zhang

et al. 2022

Front. Genet.

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“…Indeed, several genes of the calcineurin pathways were down regulated in these patients who are maintained on calcineurin inhibitors. In addition to identifying relevant immune pathways, cfChIP-seq identified differential gene signals related to extracellular matrix re-organization and fibrosis (Chandra et al 2021; Fan and Hu 2022). Allograft fibrosis is typically observed 5 – 15 years post-transplantation when long-term complications and chronic rejection involving these pathways develop.…”

Section: Discussionmentioning

confidence: 99%

Cell-free Chromatin Immunoprecipitation to detect molecular pathways in Physiological and Disease States

Jang

Markowitz

Andagie

et al. 2023

Preprint

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Patient monitoring is a cornerstone in clinical practice to define disease phenotypes and guide clinical management. Unfortunately, this is often reliant on invasive and/or less sensitive methods that do not provide deep phenotype assessments of disease state to guide treatment. This paper examined plasma cell-free DNA chromatin immunoprecipitation sequencing (cfChIP-seq) to define molecular gene sets in physiological and heart transplant patients taking immunosuppression medications. We show cfChIP-seq reliably detect gene signals that correlate with gene expression. In healthy controls and in heart transplant patients, cfChIP-seq reliably detected housekeeping genes. cfChIP-seq identified differential gene signals of the relevant immune and non-immune molecular pathways that were predominantly downregulated in immunosuppressed heart transplant patients compared to healthy controls. cfChIP-seq also identified tissue sources of cfDNA, detecting greater cell-free DNA from cardiac, hematopoietic, and other non-hematopoietic tissues such as the pulmonary, digestive, and neurological tissues in transplant patients than healthy controls. cfChIP-seq gene signals were reproducible between patient populations and blood collection methods. cfChIP-seq may therefore be a reliable approach to provide dynamic assessments of molecular pathways and tissue injury associated to disease.

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“…Raw sequencing data of GSE57338 (DCM) and GSE1145 (VMC) were downloaded from the GEO database. Preliminary differential expression analysis was performed using the limma R package (version 3.6.3) [47]. Benjamini-Hochberg method was used to correct adj.…”

Section: Differential Expression Analysismentioning

confidence: 99%

The Communal Pathogenesis, Autophagy Mechanism and Potential Therapeutic Targets in Dilated Cardiomyopathy and Viral Myocarditis

Zhu

Cao

et al. 2022

Preprint

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Background: Viral myocarditis (VMC) is an important factor leading to dilated cardiomyopathy (DCM), yet the molecular mechanism is far from elucidated. Autophagy has been proven to be associated with cardiomyopathies, but the role of autophagy in the progression from VMC to DCM is unclear and requires further study. Methods: Common differentially expressed genes (CoDEGs) in DCM and VMC were screened from the related microarray datasets. Enrichment analysis and protein-protein interaction analysis were performed to identify key pathways and Hub Genes. The differentially expressed ARGs were used for receiver operating characteristic analysis to identify potential biomarkers. The expression of these identified genes was further verified in external datasets. Results: A total of 134 CoDEGs were identified and these genes were mainly enriched in the pathways of “inflammatory response”, “response to virus”, “JAK-STAT signaling pathway”, and “PI3K-Akt signaling pathway”. The top 6 hub genes CCND1, STAT3, THBS1, CCL2, POSTN, IFIT2 and 11 Common differentially expressed ARGs BCL2L1, CCL2, CCND1, NAMPT, NRG1, S100A8, S100A9, SESN3, SNCA, STAT3, TUBA1C were identified. These genes had a similar expression pattern in DCM and VMC. Finally, in the external validation dataset, mice showed an enhanced inflammatory response and apoptotic response at the initial stage of coxsackievirus B3 infection and indicated DCM phenotype in the chronic stage of infection. Conclusions: Inflammatory response and autophagy may be the vital biological pathways in the progression from VMC to DCM, and appropriate intervention of these processes may be a novel and potential therapeutic strategy.

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Integrative analyses of biomarkers and pathways for heart failure

Cited by 20 publications

References 80 publications

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Cell-free Chromatin Immunoprecipitation to detect molecular pathways in Physiological and Disease States

The Communal Pathogenesis, Autophagy Mechanism and Potential Therapeutic Targets in Dilated Cardiomyopathy and Viral Myocarditis

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