Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T₂-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T₂-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T₂-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T₂-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T₂-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

Genome-wide microRNA (miRNA) profiling of 82 glioblastomas demonstrated that miR-181d was inversely associated with patient overall survival after correcting for age, Karnofsky performance status, extent of resection, and temozolomide (TMZ) treatment. This association was validated using the Cancer Genome Atlas (TCGA) dataset (n= 424) and an independent cohort (n= 35). In these independent cohorts, an association of miR-181d with survival was evident in patients who underwent TMZ treatment but was not observed in patients without TMZ therapy. Bioinformatic analysis of potential genes regulated by miR-181d revealed methyl-guanine-methyl-transferase (MGMT) as a downstream target. Indeed, transfection of miR-181d downregulated MGMT mRNA and protein expression. Furthermore, luciferase reporter assays and coprecipitation studies showed a direct interaction between miR-181d and MGMT 3′UTR. The suppressive effect of miR-181d on MGMT expression was rescued by the introduction of an MGMT cDNA. Finally, MGMT expression inversely correlated with miR-181d expression in independent glioblastoma cohorts. Together, these results suggest that miR-181d is a predictive biomarker for TMZ response and that its role is mediated, in part, by posttranscriptional regulation of MGMT.