Development and validation of a 6-gene signature derived from RNA modification-associated genes for the diagnosis of Acute Stanford Type A Aortic Dissection
Ting-Ting ZHANG1
Qun-Gen LI2
Zi-Peng LI1
Wei CHEN3
Chang LIU4
Hai TIAN3
Jun-Bo CHUAI3
1.Department of Cardiovascular Surgery,the Second Affiliated Hospital of Harbin Medical University,Harbin,Hei-longjiang Province,China2.Department of Cardiothoracic surgery,Heilongjiang Provincial Hospital,Harbin,Hei-longjiang Province,China3.Department of Cardiovascular Surgery,the Second Affiliated Hospital of Harbin Medical University,Harbin,Hei-longjiang Province,China;Future Medical laboratory,the Second Affiliated Hospital of Harbin Medical University,Harbin 150001,Heilongjiang,China4.Future Medical laboratory,the Second Affiliated Hospital of Harbin Medical University,Harbin 150001,Heilongjiang,China
摘要：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 consist-ing 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 En-cyclopedia of Genes and Genomes(KEGG),Gene Set Enrichment Analysis(GSEA),Gene Set Variation Analysis(GSVA),and as-sessments of immune infiltration.A co-expression network analysis(WGCNA)was performed to explore gene-phenotype rela-tionships and identify key genes.
Results A total of 45 RNA modification genes were acquired.Six gene signatures(YTHDC1,WTAP,CFI,AD ARB1,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.
机标关键词：signaturedevelopmentstanfordgenesfrom6-geneacuteaortic
论文发表日期：2024-09-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：15( 884-898 )