ArticleFrontiers in cardiovascular medicine2026
Succinylation-annotated genes in AMI: multi-omics and single-cell prioritization of ASGR2 and NPL.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Current diagnostic and prognostic biomarkers for acute myocardial infarction (AMI) remain limited. Protein succinylation may provide novel biomarker candidates for AMI. Methods: Weighted gene co-expression network analysis (WGCNA) was applied to GSE66360 to identify AMI-related modules, and succinylation-annotated genes were retrieved from GeneCards. Using GSE66360 as the training set and GSE48060, GSE60993, and GSE59867 as validation sets, we evaluated 107 predefined machine-learning pipelines and assessed hub genes by differential expression and ROC analysis. Immune infiltration and gene-cell correlations were assessed with CIBERSORT. Single-cell transcriptomics examined hub-gene expression across monocyte subsets in plaque rupture (PR) and non-plaque rupture (NPR) cases, and exploratory pseudotime analysis assessed monocyte-state heterogeneity. ELISA was used to measure circulating protein levels. Results: Integrating WGCNA with GeneCards yielded 18 succinylation-annotated AMI genes. Among the evaluated pipelines, Stepglm[both] + plsRglm and Stepglm[backward] + plsRglm showed relatively favorable external validation performance. ROC and differential expression analyses prioritized ASGR2 and NPL as exploratory candidate biomarkers. Both genes correlated positively with monocytes, particularly classical monocytes. Classical monocytes were more abundant in NPR than PR samples. ASGR2 and NPL showed exploratory expression trends along an inferred pseudotime axis. ELISA showed elevated plasma levels of ASGR2 and NPL in AMI patients compared with control individuals. Conclusion: ASGR2 and NPL were identified as hypothesis-generating candidate biomarkers associated with acute myocardial infarction. Given the limited training sample size and the high number of evaluated machine-learning pipelines, these findings remain exploratory and require independent prospective validation before clinical translation. Their enrichment in monocytes, particularly classical monocytes, suggests a potential association with monocyte-related inflammatory remodeling.
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