Effect of the mRNA decapping enzyme scavenger (DCPS) inhibitor RG3039 on glioblastoma.

Background: Patients with glioblastoma (GBM) have a poor prognosis and limited treatment options. The mRNA decapping enzyme scavenger (DCPS) is a cap-hydrolyzing enzyme. The DCPS inhibitor RG3039 exhibited excellent central nervous system bioavailability in vivo and was safe and well tolerated in healthy volunteers in a phase 1 clinical trial.

Association between viral infections and glioma risk: a two-sample bidirectional Mendelian randomization analysis.

Background: Glioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma.

COVID-19 hospitalization increases the risk of developing glioblastoma: a bidirectional Mendelian-randomization study.

Background: As a result of the COVID-19 pandemic, patients with glioblastoma (GBM) are considered a highly vulnerable population. Despite this, the extent of the causative relationship between GBM and COVID-19 infection is uncertain.

Methods: Genetic instruments for SARS-CoV-2 infection (38,984 cases and 1,644,784 control individuals), COVID-19 hospitalization (8,316 cases and 1,549,095 control individuals), and COVID-19 severity (4,792 cases and 1,054,664 control individuals) were obtained from a genome-wide association study (GWAS) from European populations.

Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study.

Background: Meningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear.

Method: Mendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data.

Computational study of novel natural inhibitors targeting Kirsten rat sarcoma viral oncogene homolog G12C.

Lung adenocarcinoma is one of the most aggressive and rapidly fatal types of malignant lung tumor. Molecular docking and virtual screening were effectively and systematically used to identify specific targets in malignant tumors and screen potential drugs. Here, we screen perfect leading compounds from a medicate library (ZINC15 database) and analyze their properties (conveyance, absorption, metabolism, excretion, and harmless forecasts) with potential inhibition of Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) G12C.

Novel natural inhibitors targeting KRAS G12C by computational study.

Ideal leading and nominee compounds with inhibiting effects on KRAS G12C were selected from the ZINC database, laying a cornerstone for the progress of anticancer drugs. A variety of computational virtual screening methods were utilized to screen possible inhibitors of KRAS G12C. LibDock was utilized to estimate 17 930 compounds and the top 20 were nominated for additional study, which was absorption, distribution, metabolism, and excretion and harmfulness prediction.

Selected ideal natural ligand against TNBC by inhibiting CDC20, using bioinformatics and molecular biology.

Object: Find potential therapeutic targets of triple-negative breast cancer (TNBC) patients by bioinformatics. Screen ideal natural ligand that can bind with the potential target and inhibit it by using molecular biology.

Methods: Bioinformatics and molecular biology were combined to analyze potential therapeutic targets.

Computational study of effective matrix metalloproteinase 9 (MMP9) targeting natural inhibitors.

Object: The present study screened ideal lead natural compounds that could target and inhibit matrix metalloproteinase 9 (MMP9) protein from the ZINC database to develop drugs for clear cell renal cell carcinoma (CCRCC)-targeted treatment.

Methods: Discovery Studio 4.5 was used to compare and screen the ligands with the reference drug, solasodine, to identify ideal candidate compounds that could inhibit MMP9.

Computational study on novel natural inhibitors targeting c-MET.

This study was designed to select ideal lead compounds and preclinical drug candidates http://dict.youdao.com/w/eng/preclinical_drug_candidate/javascript:void (0); with inhibitory effect on c-MET from the drug library (ZINC database).

Identification of effective natural PIK3CA H1047R inhibitors by computational study.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with a poor prognosis and a high recurrence rate. gene is frequently mutated in breast cancer, with as the hotspot mutation reported in TNBC. We used the ZINC database to screen natural compounds that could be structurally modified to develop drugs targeting the PIK3CA H1047R mutant protein in the PI3K pathway.

Computational study on novel natural inhibitors targeting BCL2.

Ideal lead compounds and candidate drugs with inhibitory effect on BCL2 were screened from ZINC database, which laid a foundation for drug development and compound improvement of drug treatment for diffuse large B-cell lymphoma (DLCBL). Identification of potential BCL2 inhibitors by computer-aided virtual screening. Libdock was applied to 17,931 compounds and the top 20 were selected for further analysis.

Novel natural inhibitors targeting B-RAF(V600E) by computational study.

The aim of this research was to screen the ZINC15 database to select lead compounds and drug candidates which can inhibit B-RAF (V600E). In order to identify drugs potentially inhibited B-RAF (V600E), numerous modules of Discovery Studio 4.5 were employed.

Selected using bioinformatics and molecular docking analyses, PHA-793887 is effective against osteosarcoma.

To identify novel prognostic and therapeutic targets for osteosarcoma patients, we compared the gene expression profiles of osteosarcoma and control tissues from the GSE42352 dataset in the Gene Expression Omnibus. Differentially expressed genes were subjected to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment and protein-protein interaction network analyses. Survival curve analyses indicated that osteosarcoma patients with lower mRNA levels of cyclin-dependent kinase 1 () and topoisomerase II alpha had better prognoses.