Connectivity Analysis of Atrial Fibrillation Related Stroke Based on Co-Expression Structure Network

Atrial fibrillation (AF) significantly increases stroke risk, but the risk factors and predictors of AF-stroke are rarely discovered. Hence, it is necessary to identify novel biomarker and therapeutic targets. Our study aimed to find effective pathogenic targets and elucidate the underlying molecular mechanism. First, we conducted weighted co-expression network analysis (WGCNA) in stroke-related dataset and AF-related dataset. The pink module that was highly associated with stroke (r = 0.78, p = 2 × 10–20) and the red module that was correlated with AF (r = 0.71, p = 4 × 10–5) was identified. The male-specific lethal homolog 3 (MSL3) was selected by taking the intersection between top 5 genes in stroke-related dataset and top 5 genes in AF-related dataset. Next, the expression of key gene and the receiver operating characteristic curve (ROC) analysis were also validated in other two datasets. Single sample gene set enrichment analysis (ssGSEA), single sample gene set variation analysis (ssGSVA) and correlation between genes in the key modules were exploited to investigate the function of MSL3. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune cells infiltration were conducted in key modules. Eventually, 10 small molecular drugs that have the potential to treat AF-stroke were filtered. In conclusion, our research find MSL3 can be as a novel biomarker and several candidate molecular drugs for treating AF-stroke.