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Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using
Minxue Zhang1, Lin Zhou1, Yuying Zhao1
1Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Background:
Increasing evidence indicates a connection between intracranial aneurysm (intracranial aneurysm, IA) and autoimmune diseases. However, the molecular mechanisms from a genetic perspective remain unclear. This study aims to elucidate the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of IA.
Methods:
Three transcription profiles (GSE13353, GSE26969, and GSE75436) for intracranial aneurysm (IA) were obtained from GEO databases. Autoimmune-related genes (ARGs) were sourced from the Genecards databases. Differentially expressed ARGs (DEARGs) were identified using the "limma" R package. GO, KEGG and GSEA analyses were performed to uncover underlying molecular functions. Three machine learning methods-LASSO logistic regression, random forest (RF), and XGBoost-were employed to identify key genes. An artificial neural network was used to develop an autoimmune-related signature predictive model for IA. Immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in IA, were investigated using ssGSEA. Additionally, the miRNA-gene regulatory network and potential therapeutic drugs for hub genes were predicted. In certain sections of the written content of this manuscript, the authors have utilized text generated by an AI technology. The specific name, version, model, and source of the generative AI technology used are as follows: Generative AI Technology Name: ChatGPT, Version: 4.0, Model: GPT-4, Source: OpenAI.
Results:
A total of 39 differentially expressed ARGs (DEARGs) were identified across the GSE13353, GSE26969, and GSE75436 datasets. From these, two key diagnostic genes were identified using three machine learning algorithms: ADIPOQ and IL21R. A predictive neural network model was developed based on these genes, exhibiting strong diagnostic capability with a ROC value of 0.944, and further validated using a nomogram approach. The study focused on intracranial aneurysm (IA), revealing significant insights into the underlying genetic mechanisms.
Conclusion:
The results of bioinformatics analysis in our study elucidated the mechanism of intracranial aneurysm (IA), identifying two key differential genes. Our research highlights the significant roles of immune infiltration and the regulatory networks between genes, miRNAs, and drugs in IA. These findings not only enhance our understanding of the pathogenesis of IA but also suggest potential new avenues for its treatment.
