Metal ion stimulation-related gene signatures correlate with clinical and immunologic characteristics of glioma

Abstract

Background: Environmental factors serve as one of the important pathogenic factors for gliomas. Yet people focus only on the effect of electromagnetic radiation on its pathogenicity, while metals in the environment are neglected. This study aimed to investigate the relationship between metal ion stimulation and the clinical characteristics and immune status of GM patients. Methods: Firstly, mRNA expression profiles of GM patients and normal subjects were obtained from Chinese GM Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) to identify differentially expressed metal ion stimulation-related genes(DEMISGs). Secondly, two molecular subtypes were identified and validated based on these DEMISGs using consensus clustering. Diagnostic and prognostic models for GM were constructed after screening these features based on machine learning. Finally, supervised classification and unsupervised clustering were combined to classify and predict the grade of GM based on SHAP values. Results: GM patients are divided into two different response states to metal ion stimulation, M1 and M2, which are related to the grade and IDH status of the GM. Six genes with diagnostic value were obtained: SLC30A3, CRHBP, SYT13, DLG2, CDK1, and WNT5A. The AUC in the external validation set was higher than 0.90. The SHAP value improves the performance of classification prediction. Conclusion: The gene features associated with metal ion stimulation are related to the clinical and immune characteristics of transgenic patients. XGboost/LightGBM Kmeans has a higher classification prediction accuracy in predicting glioma grades compared to using purely supervised classification techniques.

Keywords

• Supervised clustering
• Metal ion stimulation glioma