Liquid Biopsy and eHealth in Heart Failure

Bayés‐Genís, Antoni; Lanfear, David

doi:10.1016/j.jacc.2019.01.071

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Liquid biopsy is a powerful technique that could non-invasive detect biomarkers and monitors disease progression by collecting non solid biological tissues, such as blood samples. At present, this technology is widely used in tumor screening and early diagnosis ( Chen and Zhao, 2019 ), and it is also promising in the cardiovascular field ( Bayes-Genis and Lanfear, 2019 ). This result also revealed the potential value and prospect of liquid biopsy in the early diagnosis and progress monitoring of HF.…”

Section: Discussionmentioning

confidence: 99%

Regulatory mechanism of fibrosis-related genes in patients with heart failure

Tao

Gao²,

Da³

et al. 2022

Front. Genet.

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Background: Heart failure (HF) is a complex clinical syndrome characterized by the inability to match cardiac output with metabolic needs. Research on regulatory mechanism of fibrosis-related genes in patients with HF is very limited. In order to understand the mechanism of fibrosis in the development and progression of HF, fibrosis -related hub genes in HF are screened and verified.Methods: RNA sequencing data was obtained from the Gene Expression Omnibus (GEO) cohorts to identify differentially expressed genes (DEGs). Thereafter, fibrosis-related genes were obtained from the GSEA database and that associated with HF were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was carried out to analyze the biological function of fibrosis-related DEGs. The protein-protein interaction (PPI) network of hub genes was constructed via the STRING database. Moreover, the diagnostic value of hub genes for HF was confirmed using ROC curves and expression analysis. Finally, quantitative real time PCR was used to detect the expression levels of mRNAs.Results: A total of 3, 469 DEGs were identified closely related to HF, and 1, 187 fibrosis-related DEGs were obtained and analyzed for GO and KEGG enrichment. The enrichment results of fibrosis-related DEGs were consistent with that of DEGs. A total of 10 hub genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3, CXCL8, CCL2, IL6, IL1β) were selected via the PPI network. Receiver operating characteristic curve analysis was estimated in the test cohort, and 6 genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3) with AUC more than 0.7 were identified as diagnosis genes. Moreover, miRNA-mRNA and TF-mRNA regulatory networks were constructed. Finally, quantitative real time PCR revealed these 6 genes may be used as the potential diagnostic biomarkers of HF.Conclusion: In this study, 10 fibrosis-related hub genes in the HF were identified and 6 of them were demonstrated as potential diagnostic biomarkers for HF.

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Section: Discussionmentioning

confidence: 99%

Regulatory mechanism of fibrosis-related genes in patients with heart failure

Tao

Gao²,

Da³

et al. 2022

Front. Genet.

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“…Another approach where ML might pave the way for cardiovascular medicine is the so called “liquid biopsy”. Liquid biopsy is a minimally invasive technology for detecting molecular biomarkers of a tumor without an invasive biopsy and has been established in oncology [ 8 ]. Liquid biopsy came up as a non-invasive way to characterize circulating biomarkers of tumor origin.…”

Section: Prognostic Value Of Machine Learning In Omicsmentioning

confidence: 99%

A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction

et al. 2023

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A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

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