Identification of Intracranial Aneurysm Inflammatory Hub Genes by Machine Learning

Abstract Rupture of intracranial aneurysm (IA) has a very high mortality and disability rate, so it is very important to explore the mechanism of IA formation and to find potential drug therapy targets. Inflammatory responses have now been shown to play an important role in the formation and development of IA. The diagnosis, therapeutic target and prognostic marker research of various diseases through machine learning has superior performance, but it has not been reported in IA. In this study, we searched for the core inflammatory genes in the formation of IA through the GEO database, and identified the interaction relationship of these genes and the core regulated gene (TLR4) through the string database. The results of enrichment analysis indicated that our obtained inflammatory genes were associated with inflammatory response, immune response and vascular disease. Then, three key genes (CLEC7A, RTP4, SOX11) were found by two machine learning algorithms. The ROC curve results showed that key genes had high clinical value. In addition, by analyzing the correlation between key genes and immune cell infiltration, we found that inflammatory cells positively correlated with the expression of key genes play an important role in the formation and development of IA. This study is the first time to find the key genes of IA formation by machine learning method. The hub genes obtained in this study can provide new ideas for the formation and molecular mechanism of IA in the future, and provide a new direction for the non-surgical treatment of IA.