ArticleFrontiers in immunology2022
PRKAR1A and SDCBP Serve as Potential Predictors of Heart Failure Following Acute Myocardial Infarction.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 40 citations in OpenAlex.
- Targeting MDA-9/syntenin-1 (SDCBP) as a strategy to eliminate head and neck squamous cell carcinoma stem cells.Cancer letters · 2026Article
- Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.Clinical and experimental medicine · 2026Article
- Article
- Integrative Single-Cell RNA Sequencing and Machine Learning Reveals Candidate Plasma Protein-Associated Gene Signatures for Osteoporosis: A Preliminary Exploratory in Silico Study.International journal of general medicine · 2026Article
- Construction of a Prognostic Model and Subgroup Characteristics Related to Hypoxia-Immune Evasion in T Cells of Hepatocellular Carcinoma Based on Single-Cell and Bulk RNA Analysis.Journal of hepatocellular carcinoma · 2026Article
- Bioinformatics Identification and Experimental Validation of Ferroptosis- and Immune Infiltration-Associated Biomarkers in Ischemic Stroke.Current issues in molecular biology · 2025Article
- Knockdown of TGFB2 Attenuates Ischemic Heart Failure by Inhibiting Apoptosis.Cardiovascular toxicology · 2025Article
- Extracellular matrix gene set and microRNA network in intestinal ischemia-reperfusion injury: Insights from RNA sequencing for diagnosis and therapy.World journal of gastrointestinal surgery · 2025Review
- Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.Frontiers in immunology · 2025Article
- CLU, FOS, and CXCL8 as diagnostic biomarkers for heart failure progression post-acute myocardial infarction: an integrated RNA-Seq and multi-machine learning study.Frontiers in cardiovascular medicine · 2025Article
- Factors Influencing Cardiac Rehabilitation Compliance in Elderly Myocardial Infarction Patients and the Development of a Nomogram Prediction Model.Patient preference and adherence · 2025Article
- Liver fibrosis indices predict malignant cerebral edema after endovascular therapy for anterior circulation large vessel occlusion stroke: a retrospective cohort study.Frontiers in neurology · 2025Article
- Quercetin in Shengxian Decoction exhibits anti-ferroptosis protective roles in a myocardial infarction modelFrontiers in pharmacology · 2025Article
- snRNA-seq of human ovaries reveals heat shock proteins are associated with obesity related cancer risk.Journal of translational medicine · 2024Article
- The diagnostic value investigation of programmed cell death genes in heart failure.BMC cardiovascular disorders · 2024Article
- Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine.Genes & diseases · 2024Review
- From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Identification of transcription factor-lipid droplet-related gene biomarkers for the prognosis of post-acute myocardial infarction-induced heart failure.Frontiers in cardiovascular medicine · 2024Article
- Differentially expressed platelet activation-related genes in dogs with stage B2 myxomatous mitral valve disease.BMC veterinary research · 2023Article
- Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objectives: Early diagnosis of patients with acute myocardial infarction (AMI) who are at a high risk of heart failure (HF) progression remains controversial. This study aimed at identifying new predictive biomarkers of post-AMI HF and at revealing the pathogenesis of HF involving these marker genes. Methods and Results: A transcriptomic dataset of whole blood cells from AMI patients with HF progression (post-AMI HF, n = 16) and without progression (post-AMI non-HF, n = 16) was analyzed using the weighted gene co-expression network analysis (WGCNA). The results indicated that one module consisting of 720 hub genes was significantly correlated with post-AMI HF. The hub genes were validated in another transcriptomic dataset of peripheral blood mononuclear cells (post-AMI HF, n = 9; post-AMI non-HF, n = 8). PRKAR1A, SDCBP, SPRED2, and VAMP3 were upregulated in the two datasets. Based on a single-cell RNA sequencing dataset of leukocytes from heart tissues of normal and infarcted mice, PRKAR1A was further verified to be upregulated in monocytes/macrophages on day 2, while SDCBP was highly expressed in neutrophils on day 2 and in monocytes/macrophages on day 3 after AMI. Cell-cell communication analysis Conclusion: This study identified that PRKAR1A and SDCBP may serve as novel biomarkers for the early diagnosis of post-AMI HF and also revealed their potentially regulatory mechanism during HF progression.
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