Radiomic Prediction of CCND1 Expression Levels and Prognosis in Low-grade Glioma Based on Magnetic Resonance Imaging.

OJECTIVES: Low-grade glioma (LGG) is associated with increased mortality owing to recrudescence and the tendency for malignant transformation. Therefore, it is imperative to discover novel prognostic biomarkers as existing traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG MATERIALS AND METHODS: A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses. RESULTS: CCND1 was identified as a prognosis-related gene with differential expression in tumor and normal samples and plays a role in regulating both the cell cycle and immune response. Landmark analysis revealed that high-expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703 and 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CONCLUSION: Elevated levels of CCND1 expression could impact the prognosis of patients with LGG. MRI-based radiomics, especially the AUC values, can serve as a novel tool for predicting CCND1 expression and understanding the correlation between elevated CCND1 expression and prognosis. AVAILABILITY OF DATA AND MATERIALS: The datasets analyzed during the current study are available in the TCGA, TCIA, UCSC XENA and GTEx repository, https://portal.gdc.cancer.gov/, https://www.cancerimagingarchive.net/, https://xenabrowser.net/datapages/, https://www.gtexportal.org/home/.

The clinical correlation between Alzheimer's disease and epilepsy.

Alzheimer's disease and epilepsy are common nervous system diseases in older adults, and their incidence rates tend to increase with age. Patients with mild cognitive impairment and Alzheimer's disease are more prone to have seizures. In patients older than 65 years, neurodegenerative conditions accounted for ~10% of all late-onset epilepsy cases, most of which are Alzheimer's disease. Epilepsy and seizure can occur in the early and late stages of Alzheimer's disease, leading to functional deterioration and behavioral alterations. Seizures promote amyloid-ß and tau deposits, leading to neurodegenerative processes. Thus, there is a bi-directional association between Alzheimer's disease and epilepsy. Epilepsy is a risk factor for Alzheimer's disease and, in turn, Alzheimer's disease is an independent risk factor for developing epilepsy in old age. Many studies have evaluated the shared pathogenesis and clinical relevance of Alzheimer's disease and epilepsy. In this review, we discuss the clinical associations between Alzheimer's disease and epilepsy, including their incidence, clinical features, and electroencephalogram abnormalities. Clinical studies of the two disorders in recent years are summarized, and new antiepileptic drugs used for treating Alzheimer's disease are reviewed.