Diffuse low-grade glioma (DLGG) is a well-differentiated, slow-growing tumour with an inherent tendency to progress to high-grade glioma. The potential roles of genetic alterations in DLGG development have not yet been fully delineated. Therefore, the current study performed an integrated gene expression meta-analysis of eight independent, publicly available microarray datasets including 291 DLGGs and 83 non-glioma (NG) samples to identify gene expression signatures associated with DLGG. Using INMEX, 708 differentially expressed genes (DEGs) (385 upregulated and 323 downregulated genes) were identified in DLGG compared to NG. Furthermore, 497 DEGs (222 upregulated and 275 downregulated genes) corresponding to two histological types were identified. Of these, high expression of HIP1R significantly correlated with increased overall survival, whereas high expression of TBXAS1 significantly correlated with decreased overall survival. Additionally, network-based meta-analysis identified FN1 and APP as the key hub genes in DLGG compared with NG. PTPN6 and CUL3 were the key hub genes identified in the astrocytoma relative to the oligodendroglioma. Further immunohistochemical validation revealed that MTHFD2 and SPARC were positively expressed in DLGG, whereas RBP4 was positively expressed in NG. These findings reveal potential molecular biomarkers for diagnosis and therapy in patients with DLGG and provide a rich and novel candidate reservoir for future studies.

Purpose To construct multivariate radiomics models using hybrid 18 F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).Methods Ninety patients (60 with PD and 30 with MSA) were randomised to training and validation sets in a 7:3 ratio. All patients underwent 18 F-Fluorodeoxyglucose ( 18 F-FDG) PET/MRI to simultaneously obtain metabolic images ( 18 F-FDG), structural MRI images (T1-weighted imaging [T1WI], T2-weighted imaging [T2WI] and T2-weighted uid-attenuated inversion recovery [T2/ air]) and functional MRI images (susceptibilityweighted imaging [SWI] and apparent diffusion coe cient). Using PET and ve MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).Results The radiomics signatures showed favourable diagnostic e cacy. The optimal model comprised structural (T1WI), functional (SWI), and metabolic ( 18 F-FDG) sequences (Radscore FDG_T1WI_SWI ) with the area under curves (AUCs) of the training and validation sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore FDG_T1WI_SWI , three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV max , demonstrated satisfactory calibration and discrimination in the training and validation sets (0.993 and 0.994, respectively). DCA indicated the highest clinical bene t of the clinical-radiomics integrated model. ConclusionsThe radiomics signature with metabolic, structural, and functional information provided by hybrid 18 F-FDG PET/MRI may achieve promising diagnostic e cacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.