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/.

Integrated evolutionary pattern analyses reveal multiple origins of steroidal saponins in plants.

Steroidal saponins are a class of specialized metabolites essential for plant's response to biotic and abiotic stresses. They are also important raw materials for the industrial production of steroid drugs. Steroidal saponins are present in some monocots, such as Dioscorea and Paris, but their distribution, origin, and evolution in plants remain poorly understood. By reconstructing the evolutionary history of the steroidal saponin-associated module (SSAM) in plants, we reveal that the steroidal saponin pathway has its origin in Asparagus and Dioscorea. Through evaluating the distribution and evolutionary pattern of steroidal saponins in angiosperms, we further show that steroidal saponins originated multiple times in angiosperms, and exist in early diverged lineages of certain monocot lineages including Asparagales, Dioscoreales, and Liliales. In these lineages, steroidal saponins are synthesized through the high copy and/or high expression mechanisms of key genes in SSAM. Together with shifts in gene evolutionary rates and amino acid usage, these molecular mechanisms shape the current distribution and diversity of steroidal saponins in plants. Consequently, our results provide new insights into the distribution, diversity and evolutionary history of steroidal saponins in plants, and enhance our understanding of plants' resistance to abiotic and biotic stresses. Additionally, fundamental understanding of the steroidal saponin biosynthesis will facilitate their industrial production and pharmacological applications.

Atomically dispersed Pd catalysts in graphyne nanopore: formation and reactivity.

The formation of single-atom noble metal catalysts on carbon materials remains a challenge due to the weak interaction between metals and pristine carbon. By means of density functional theory (DFT) calculations, it is found that the atomically dispersed Pd in graphyne nanopore is much more stable than that of relative Pd clusters. The large diffusion barrier of Pd from the most stable hollow site to the bridge site confirms the kinetic stability of such structures. While CO adsorption causes the pulling of Pd from graphyne nanopore due to the low diffusion barrier, based on DFT calculations, which can be further confirmed by ab initio molecular dynamic simulations. Finally, CO oxidation on the reconstruction of Pd@graphyne exhibits an energy barrier of 0.62 eV in the rate-limiting step through the Langmuir-Hinshelwood mechanism. After the reaction, the catalyst can be restored to the original atomically dispersed state again. This study shows graphyne is an excellent support for an atomically dispersed or single-metal catalyst.