Abstract
Objective
To investigate MRI-based radiomic features in glioma and key genes related to IDH mutations, and to analyze their correlation.Methods
61 MRI files from TCIA were divided into training and validation sets (7:3). An external validation set included 66 pathologically confirmed glioma patients. Regions of interest (ROIs) were manually delineated, extracting 1,037 radiomic features. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression reduced and filtered features. RNA-seq data differential analysis identified key genes linked to glioma progression and IDH mutations. To ensure the comprehensiveness and robustness of our findings, we further validated the protein expression, tumor immune microenvironment, and cellular localization of these key genes utilizing Western blotting, immune infiltration analysis, single-cell RNA sequencing, and spatial transcriptomics. Correlation between radiomic features and key IDH mutation genes was analyzed.Results
A model with nine IDH mutation-related radiomic features showed strong predictive performance. Univariate and multivariate Cox regression indicated these features predict glioma prognosis. Eight radiomic features differed significantly across MGMT methylation levels. Differential analysis identified eight key genes promoting glioma development and associated with IDH mutations. Crucially, Western blot experiments confirmed the differential expression of these proteins across IDH subtypes. Furthermore, single-cell and spatial transcriptomic analyses revealed that these genes are predominantly expressed in specific cellular subpopulations, such as astrocytes and microglia, and correlate with immune infiltrating cells, contributing to glioma malignancy. Among the nine selected radiomic features (RF1-RF9), RF3 and RF7 were significantly negatively correlated with key IDH mutation genes, while RF4 showed significant positive correlation. RF2 and RF6 exhibited significant negative correlations with MMP9.Conclusion
MRI-based radiomic features and models are valuable for predicting IDH mutations in gliomas. Combined with bioinformatics analysis, they aid in identifying glioma pathological types and genetic characteristics.