OjectivesLow-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 LGGMaterials and MethodsA 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.ResultsCCND1 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.ConclusionElevated 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 MaterialsThe 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/.

Abstract Low-grade glioma (LGG) is associated with increased mortality owing to the recrudescence and tendency for malignant transformation. Therefore, novel prognostic biomarkers must be identified as the current 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. 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. Involved in the regulation of the cell cycle and immune response, CCND1 was identified as a differentially expressed prognosis-related gene in tumor and normal samples. 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, 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CCND1 expression levels could affect the prognosis of patients with LGG. MRI-based radiomics can serve as a novel tool for predicting the prognosis.

Melatonin has important immuno-regulatory effects in inflammatory disorders but its specific role in Helicobacter pylori induced gastritis remains unclear. The aim of our study was to analyze the activity of melatonin against H. pylori induced gastritis in vivo, and explore the underlying mechanisms. The H. pylori infected mice showed extensive inflammatory cell infiltration in the gastric mucosa and submucosa, along with significantly reduced spleen and thymus weight. However, 2 and 6 weeks of treatment with 25 and 50 mg/kg melatonin restored the thymus weights relative to that of the untreated mice. TLR2 was upregulated in the gastric mucosa of the infected mice, which was restored to normal levels after 2 and 6 weeks of melatonin treatment. In contrast, TLR4 levels were similar between the treated and untreated mice. Furthermore, melatonin treatment restored spleen Foxp3 and serum TGF-β1 levels that were respectively increased and decreased in the infected mice. H. pylori infected mice also showed a decrease in the serum levels of IL-2, IL-6, IL-10, IL-17, IFN-γ and TFN-α following 2 and 6 weeks of melatonin treatment compared to the untreated mice. Melatonin treatment also resulted in decreased CD4+CD25+Foxp3+ Treg cell count in the spleen. The expression of TLR2, MyD88, p-ERK, p-p38, p65, p50 and Foxp3 in the gastric tissues were lower in the untreated mice compared to mice treated with melatonin for 2 weeks. However, the expression levels evened out after 6 weeks of treatment. Taken together, melatonin alleviates H. pylori induced gastritis by regulating TGF-β1 and Foxp3 expression via the TLR2 and TLR4 pathways.