Development and validation of a 6-gene signature derived from RNA modification-associated genes for the diagnosis of Acute Stanford Type A Aortic Dissection.

Background: Acute Stanford Type A Aortic Dissection (ATAAD) is a critical medical emergency characterized by significant morbidity and mortality. This study aims to identify specific gene expression patterns and RNA modification associated with ATAAD.

Methods: The GSE153434 dataset was obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted to identify differential expression genes (DEGs) associated with ATAAD. To validate the involvement of RNA modification in ATAAD, RNA modification-related genes (M6A, M1A, M5C, APA, A-to-I) were acquired from GeneCards, following by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. A gene prediction signature consisting of key genes was established, and Real-time PCR was used to validate the gene expression in clinical samples. The patients were then divided into high and low-risk groups, and subsequent enrichment analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and assessments of immune infiltration. A co-expression network analysis (WGCNA) was performed to explore gene-phenotype relationships and identify key genes.

Results: A total of 45 RNA modification genes were acquired. Six gene signatures (YTHDC1, WTAP, CFI, ADARB1, ADARB2, TET3) were developed for ATAAD diagnosis and risk stratification. Enrichment analysis suggested the potential involvement of inflammation and extracellular matrix pathways in the progression of ATAAD. The incorporation of pertinent genes from the GSE147026 dataset into the six-gene signature further validated the model's effectiveness. A significant upregulation in WTAP, ADARB2, and TET3 expression, whereas YTHDC1 exhibited a noteworthy downregulation in the ATAAD group.

Conclusion: Six-gene signature could serve as an efficient model for predicting the diagnosis of ATAAD.