Transcriptomic profiling and bioinformatic insights into myocardial injury following aneurysmal subarachnoid hemorrhage.

Background: Myocardial injury is a common complication of aneurysmal subarachnoid hemorrhage (aSAH) and is associated with poor outcomes. While RNA plays a critical role in pathophysiological processes, its expression patterns and functions in myocardial injury after aSAH (aSAH-MI) remain poorly understood.

Objective: To construct the RNA expression profile of aSAH-MI patients, explore their biological functions, and establish a gene expression regulatory network for aSAH-MI. These findings provide a theoretical basis for understanding the RNA-level mechanisms underlying aSAH-MI.

Methods: This study included 12 patients, comprising 6 aSAH-MI patients and 6 aSAH-nonMI patients (aSAH patients without myocardial injury). RNA sequencing was performed on three patients from each group to construct an RNA expression matrix. Differentially expressed genes (lncRNAs, miRNAs, mRNAs) were identified using the limma package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. miRNA, lncRNA, and mRNA interactions were predicted using miRanda and RNAhybrid. An lncRNA-miRNA-mRNA interaction network was constructed with Cytoscape, and qRT-PCR validated selected genes in an additional six patients.

Results: In aSAH-MI patients, 617 lncRNAs, 20 miRNAs, and 510 mRNAs were significantly differentially expressed, with 258, 13, and 244 being upregulated, and 359, 7, and 266 being downregulated, respectively ( < 0.05). Bioinformatic analysis revealed that the differentially expressed mRNAs were involved in biological processes such as ion transport, immune regulation, and myocardial contraction, and were associated with pathways related to vasodilation, nerve conduction, and cardiac function regulation. ceRNA analysis identified hsa-miR-4707-3p and hsa-miR-25-5p as potential network hubs. The lncRNAs with the highest connectivity were CELSR1-204, SLCO2B1-212, AEN-204, PPFIA4-205, and MIAT-219, while the mRNAs with the highest connectivity were CHI3L1, ADORA2A, PAX8, VWA3B, and KCNE1. These findings suggest these differentially expressed genes may serve as key regulators in mediating aSAH-MI. Validation through qRT-PCR in an additional cohort of six subjects confirmed the differential expression of selected genes.

Conclusions: This study successfully constructed the RNA expression profiles in the blood of patients with aSAH-MI through transcriptome sequencing, identifying significant differentially expressed miRNAs, mRNAs, and lncRNAs. Bioinformatic analysis suggests these genes may play critical roles in the pathogenesis of aSAH-MI.