Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Introduction: Heart failure (HF) is the most common complication following myocardial infarction (MI) and frequently occurs during the postinfarction recovery phase. Despite the well-established association between HF and MI, the underlying molecular mechanisms driving their transition remain poorly understood.
Methods: The aim of this study was to identify key regulatory genes involved in this transition via advanced computational tools. We conducted a comprehensive analysis of differentially expressed genes (DEGs) via Limma software, leveraging five independent datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE59867, GSE62646, GSE168281, GSE267644, and GSE269269. Our multistep analytical pipeline included weighted gene coexpression network analysis (WGCNA) to map interacting genes, machine learning algorithms for robust classification, functional annotation via Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore biological pathways, CIBERSORT correlation analysis linking hub genes with immune cell states, transcriptional regulation profiling of key hubs, and single-cell sequencing to assess the functional relevance of these hubs.
Results: Our findings revealed that 413 DEGs were significantly different between MI and HF. WGCNA identified 98 genes associated with both conditions. Machine learning filtering further prioritized 10 hub genes: , , , , , , , and . These hubs were significantly associated with immune-related processes, suggesting their potential role in the pathogenesis of HF after MI. Single-cell transcriptomic analysis demonstrated that exhibited the strongest correlation with the transition from MI to HF; using random forest modelling, we validated its predictive value in this context.
Discussion: In conclusion, our study identified as a critical regulator of the transition from MI to HF. Our findings underscore the potential of hub genes as targets for novel therapeutic interventions aimed at mitigating MI-to-HF progression and improving patient outcomes.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229883 | PMC |
http://dx.doi.org/10.3389/fgene.2025.1592985 | DOI Listing |