Key result
Genetically predicted RLF and SMCHD1 expression is linked to higher risk of myocardial infarction.
Why the study?
The diagnosis of MI requires specific diagnostic biomarkers beyond high-sensitivity cardiac troponins, motivating the identification of causal lactylation-regulated genes and their roles in metabolic-immune dysregulation.
Observational (n=171,875)
Should not alter current MI diagnosis or therapy; leaves open human translation of these lactylation-linked causal genes.
Background The diagnosis of Myocardial Infarction (MI) requires the discovery of specific diagnostic biomarkers beyond high-sensitivity cardiac troponins. To identify causal MI-associated genes regulated by lactylation modification and elucidate their roles in metabolic-immune dysregulation. Methods This multi-omics study combined bioinformatic analyses of human MI datasets (GSE60993/GSE61144/GSE66360) with experimental validation to investigate lactylation-related genes (LRGs). Differential expression analysis (limma, P < 0.05, |log 2 FC|>0.585) identified 571 Differentially Expressed Genes (DEGs), which intersected with 2,051 curated lactylation-related genes (LRGs) (PubMed/GeneCards) yielding 56 lactylation-associated DEGs. Mendelian randomization (MR) utilized genetic instruments (P < 5 × 10 −6 ) from Gene eQTL and three MI-GWAS cohorts (43,676 cases/128,199 controls), employing inverse-variance weighted (IVW) regression with sensitivity analyses (MR-Egger/weighted median). Functional enrichment (clusterProfiler) of the 56 DEGs examined GO/KEGG terms (FDR P < 0.05), supplemented by Gene Set Variation Analysis (GSVA) of Rearranged L-myc fusion (RLF) and Structural Maintenance of Chromosomes Hinge Domain Containing 1 (SMCHD1) expression strata and CIBERSORT-based immune infiltration assessment. Experimental validation involved LAD ligation-induced MI modeling in C57BL/6 mice, with RLF/SMCHD1 expression quantified via qPCR and Western blot. Results Integrated transcriptomic analysis of three GEO datasets (73 MI patients, 67 controls) identified 571 DEGs. Cross-referencing these DEGs with 2,051 LRGs yielded 56 Lactylation-associated DEGs. MR analysis using 42,699 instrumental SNPs established RLF (AUC = 0.823) and SMCHD1 (AUC = 0.809) as causal risk genes that were significantly elevated in MI patients. Functional enrichment implicated both genes in metabolic dysregulation (nucleotide metabolism, HIF-1/MAPK signaling) and necroptosis. Immune profiling revealed increased monocytes, neutrophils, and activated CD4 + T cells within MI tissues, all positively correlated with RLF and SMCHD1 expression. Conversely, reduced CD8 + T cell infiltration correlated negatively with RLF expression. Independent validation confirmed significant RLF upregulation in MI. Quantitative analyses revealed significant increases in RLF and SMCHD1 expression—at both transcriptional (mRNA) and translational (protein) levels—in MI-induced mice relative to sham controls. Conclusion This study pioneers the integration of lactylation modification with MR analysis for MI, establishing RLF and SMCHD1 as causal diagnostic biomarkers. Their dual roles in promoting metabolic dysregulation and pro-inflammatory immune infiltration position them as promising therapeutic targets for MI intervention.
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Yuan et al. (2025) conducted an observational in Myocardial Infarction (n=171,875). RLF and SMCHD1 expression vs. Controls was evaluated on Myocardial Infarction (RLF AUC 0.823, SMCHD1 AUC 0.809). Mendelian randomization established RLF (AUC = 0.823) and SMCHD1 (AUC = 0.809) as causal risk genes significantly elevated in myocardial infarction.
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