P129, a pyrazole ring-containing isolongifolanone-derivate: synthesis and investigation of anti-glioma action mechanism.

BACKGROUND: Cyclin-dependent kinase-2 (CDK-2) is an important regulatory factor in the G1/S phase transition. CDK-2 targeting has been shown to suppress the viability of multiple cancers. However, the exploration and application of a CDK-2 inhibitor in the treatment of glioblastoma are sparse. METHODS: We synthesized P129 based on isolongifolanone, a natural product with anti-tumor activity. Network pharmacology analysis was conducted to predict the structural stability, affinity, and pharmacological and toxicological properties of P129. Binding analysis and CETSA verified the ability of P129 to target CDK-2. The effect of P129 on the biological behavior of glioma cells was analyzed by the cell counting kit-8, colony formation, flow cytometry, and other experiments. Western blotting was used to detect the expression changes of proteins involved in the cell cycle, cell apoptosis, and epithelial-mesenchymal transition. RESULTS: Bioinformatics analysis and CETSA showed that P129 exhibited good intestinal absorption and blood-brain barrier penetrability together with high stability and affinity with CDK-2, with no developmental toxicity. The viability, proliferation, and migration of human glioma cells were significantly inhibited by P129 in a dose- and time-dependent manner. Flow cytometry and western blotting analyses showed G0/G1 arrest and lower CDK-2 expression in cells treated with P129 than in the controls. The apoptotic ratio of glioma cells increased significantly with increasing concentrations of P129 combined with karyopyknosis and karyorrhexis. Apoptosis occurred via the mitochondrial pathway. CONCLUSION: The pyrazole ring-containing isolongifolanone derivate P129 exhibited promising anti-glioma activity by targeting CDK-2 and promoting apoptosis, indicating its potential importance as a new chemotherapeutic option for glioma.

Association between psychiatric disorders and glioma risk: evidence from Mendelian randomization analysis.

BACKGROUND: Observational studies have explored the association of psychiatric disorders and the risk of brain cancers. However, the causal effect of specific mental illness on glioma remains elusive due to the lack of solid evidence. METHODS: We performed a two-sample bidirectional Mendelian randomization (MR) analysis to explore the causal relationships between 5 common psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, autism spectrum disorder, and panic disorder) and glioma. Summary statistics for psychiatric disorders and glioma were extracted from Psychiatric Genomics Consortium (PGC) and 8 genome-wide association study (GWAS) datasets respectively. We calculated the MR estimates for odds ratio of glioma associated with each psychiatric disorder by using inverse-variance weighting (IVW) method. Sensitivity analyses such as weighted median estimator, MR-Egger and MR-PRESSO were leveraged to assess the strength of causal inference. RESULTS: A total of 30,657 participants of European ancestry were included in this study. After correction for multiple testing, we found that genetically predicted schizophrenia was associated with a statistically significant increase in odds of non-glioblastoma multiforme (non-GBM) (OR = 1.13, 95% CI: 1.03-1.23, P = 0.0096). There is little evidence for the causal relationships between the other 4 psychiatric disorders with the risk of glioma. CONCLUSIONS: In this MR analysis, we revealed an increased risk of non-GBM glioma in individuals with schizophrenia, which gives an insight into the etiology of glioma.

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. METHODS: We conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental variables to estimate the causal relationship between glioma and twelve types of viral infections from corresponding GWAS data. In addition, sensitivity analyses were performed. RESULTS: After correcting for multiple tests and sensitivity analysis, we detected that genetically predicted herpes zoster (caused by Varicella zoster virus (VZV) infection) significantly decreased risk of low-grade glioma (LGG) development (OR = 0.85, 95% CI: 0.76-0.96, P = 0.01, FDR = 0.04). No causal effects of the other eleven viral infections on glioma and reverse causality were detected. CONCLUSIONS: This is one of the first and largest studies in this field. We show robust evidence supporting that genetically predicted herpes zoster caused by VZV infection reduces risk of LGG. The findings of our research advance understanding of the etiology of 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. A total of 6,183 GBM cases and 18,169 controls from GWAS were enrolled in our study. Their associations were evaluated by applying Mendelian randomization (MR) including IVW meta-analysis, MR-Egger regression, and weighted-median analysis. To make the conclusions more robust and reliable, sensitivity analyses were performed. Results: Our results showed that genetically predicted COVID-19 hospitalization increases the risk of GBM (OR = 1.202, 95% CI = 1.035-1.395, p = 0.016). In addition, no increased risk of SARS-CoV-2 infection, COVID-19 hospitalization and severity were observed in patients with any type of genetically predicted GBM. Conclusion: Our MR study indicated for the first time that genetically predicted COVID-19 hospitalization was demonstrated as a risk factor for the development of GBM.

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma.

Introduction: The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors. Methods: In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected. Results: Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors. Discussion: This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

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. It's a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome. Results: This research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-ß, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood. Conclusion: These findings suggest that TNF-ß, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas.

Cerebrospinal fluid protein levels are elevated 100 times in a Leptomeningeal metastasis patient: a case report and literature review.

Leptomeningeal metastasis (LM) has a high degree of malignancy and high mortality. We describe a patient admitted to hospital with acute lower extremity weakness, dysuria, and high intracranial pressure. Enhanced magnetic resonance imaging (MRI) showed extensive enhancement of the leptomeningeal and spinal meninges with multiple nodular changes and extensive fusion. His cerebrospinal fluid (CSF) was yellow and cloudy, the Pandy test was strongly positive (++++), the protein was 46 g/L (normal range 0.15-0.45 g/L), which attracted our attention. Initially, miliary TB with associated tuberculous meningitis (TBM) was diagnosed, and neurosarcoidosis cannot be ruled out. After poor therapeutic effect of standard antituberculosis (anti-TB) therapy, further inspection found that malignant cells were detected by cerebrospinal fluid (CSF) cytology. PET/CT suggested the diagnosis of LM. The purpose of this paper is to describe the characteristics of atypical diffuse LM. In conclusion, when patient with unexplained high levels of CSF protein, it is necessary to be alert to the diagnosis of LM. Multiple examinations of fresh CSF are helpful to increase the positive detection rate of tumor cells. Early diagnosis and active treatment are conducive to improving survival rate.

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. Further results demonstrated that ZINC000013817014 and ZINC000004098458 were screened out from the ZINC15 database and were identified to have a much better binding affinity and more favorable interaction vitality binding with KRAS G12C and less rat carcinogenicity, Ames mutagenicity, way better dissolvability in water and noninhibition with cytochrome P-450 2D6. Molecular dynamics simulation analysis indicated that the binding capacity of these two compounds and KRAS G12C, ZINC000013817014-KRAS G12C, and ZINC000004098458-KRAS G12C is stable in the natural environment. Our findings reveal that ZINC000013817014 and ZINC000004098458 were perfect leading compounds to be inhibitors binding with KRAS G12C, which were selected as safe drug candidates and a cornerstone for KRAS G12C-related medicine plan and improvement. What is more, we have conducted a Cell Counting Kit-8 to verify the exactly inhibitory effects of the two selected drugs on Lung adenocarcinoma. This study establishes a solid framework for systematic anticancer medication research and development.

Differential regulation of H3K9/H3K14 acetylation by small molecules drives neuron-fate-induction of glioma cell.

Differentiation therapy using small molecules is a promising strategy for improving the prognosis of glioblastoma (GBM). Histone acetylation plays an important role in cell fate determination. Nevertheless, whether histone acetylation in specific sites determines GBM cells fate remains to be explored. Through screening from a 349 small molecule-library, we identified that histone deacetylase inhibitor (HDACi) MS-275 synergized with 8-CPT-cAMP was able to transdifferentiate U87MG GBM cells into neuron-like cells, which were characterized by cell cycle arrest, rich neuron biomarkers, and typical neuron electrophysiology. Intriguingly, acetylation tags of histone 3 at lysine 9 (H3K9ac) were decreased in the promoter of multiple oncogenes and cell cycle genes, while ones of H3K9ac and histone 3 at lysine 14 (H3K14ac) were increased in the promoter of neuron-specific genes. We then compiled a list of genes controlled by H3K9ac and H3K14ac, and proved that it is a good predictive power for pathologic grading and survival prediction. Moreover, cAMP agonist combined with HDACi also induced glioma stem cells (GSCs) to differentiate into neuron-like cells through the regulation of H3K9ac/K14ac, indicating that combined induction has the potential for recurrence-preventive application. Furthermore, the combination of cAMP activator plus HDACi significantly repressed the tumor growth in a subcutaneous GSC-derived tumor model, and temozolomide cooperated with the differentiation-inducing combination to prolong the survival in an orthotopic GSC-derived tumor model. These findings highlight epigenetic reprogramming through H3K9ac and H3K14ac as a novel approach for driving neuron-fate-induction of GBM cells.

Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics.

OBJECTIVE: The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features. METHODS: In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance. RESULTS: The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively). CONCLUSIONS: The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.

ELOVLs Predict Distinct Prognosis Value and Immunotherapy Efficacy In Patients With Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a primary malignancy of the liver with high prevalence worldwide and poor prognosis. It has been verified that elongation of very-long-chain fatty acids gene family (ELOVLs), a group of genes that responsible for elongation of saturated and polyunsaturated fatty acids, participate in the pathogenesis and development of multiplex disease including cancers. However, the functions and prognosis of ELOVLs in HCC are still indistinguishable. Methods: First, we searched the mRNA expression and survival data of ELOVLs in patients with HCC via the data of The Cancer Genome Atlas (TCGA). The prognosis value of ELOVLs on HCC was assessed by Kaplan-Meier plotter and Cox regression analysis. reverse transcription quantitative- polymerase chain reaction (RT-qPCR), Western blot (WB), and immunohistochemistry were applied to assess the specific mRNA and protein expression of ELOVLs in HCC clinical specimens of our cohort. Then, the functional enrichment of ELOVL1 especially the pathways relating to the immune was conducted utilizing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analysis. Additionally, TIMER, CIBERSOR, and tumor immune dysfunction and exclusion (TIDE) were employed to evaluate the relationship between ELOVL1 and immune responses. Last, the correlation of ELOVL1 with genome heterogeneity [microsatellite instability (MSI), tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH), homologous recombination deficiency (HRD), purity, ploidy, loss of heterozygosity (LOH), and neoantigens] and mutational landscape were also evaluated basing on the date in TCGA. Results: Significant expression alteration was observed in ELOVLs family at the pan-cancer level. In liver cancer, ELOVL1 and ELOVL3 were strongly associated with poor prognosis of HCC by survival analysis and differential expression analysis. Immunohistochemistry microarray, WB, and RT-qPCR confirmed that ELOVL1 but not ELOVL3 played an important role in HCC. Mechanistically, functional network analysis revealed that ELOVL1 might be involved in the immune response. ELOVL1 could affect immune cell infiltration and immune checkpoint markers such as PD-1 and CTLA4 in HCC. Meanwhile, high expression of ELOVL1 would be insensitive to immunotherapy. Correlation analysis of immunotherapy markers showed that ELOVL1 has been associated with MSI, TMB, and oncogene mutations such as TP53. Conclusion: ELOVLs play distinct prognostic value in HCC. ELOVL1 could predict the poor prognosis and might be a potential indicator of immunotherapy efficacy in HCC patients.

Identification and validation of immune related core transcription factors GTF2I in NAFLD.

Background: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide that endangers human health. Transcription factors (TFs) have gradually become hot spots for drug development in NAFLD for their impacts on metabolism. However, the specific TFs that regulate immune response in the development of NAFLD is not clear. This study aimed to investigate the TFs involved in the immune response of NAFLD and provide novel targets for drug development. Methods: Microarray data were obtained from liver samples from 26 normal volunteers and 109 NAFLD patients using the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed by limma package. Differentially expressed transcription factors (DETFs) were obtained on DEGs combined with Cistrome Cancer database. Immune signatures and pathways hallmark were identified by ssGSSEA and GSVA. The co-regulation network was constructed by the above results. Further, quantitative Real-time Polymerase Chain Reaction (qRT-PCR), Western blot (WB) and Immunohistochemistry (IHC) were used to validate the relationship between GTF2I and NAFLD. CIBERSORT analysis was performed to identify cell types to explore the relationship between differential expression of GTF2I and immune cell surface markers. Results: A total of 617 DEGs and six DETFs (ESR1, CHD2, GTF2I, EGR1, HCFC1, SP2) were obtained by differential analysis. Immune signatures and pathway hallmarks were identified by ssGSSEA and GSVA. GTF2I and CHD2 were screened through the co-regulatory networks of DEGs, DETFs, immune signatures and pathway hallmarks. Furthermore, qRT-PCR, WB and IHC indicated that GTF2I but not CHD2 was significantly upregulated in NAFLD. Finally, in silico, our data confirmed that GTF2I has a wide impact on the immune profile by negatively regulating the expression of the chemokine receptor family (227/261, count of significance). Conclusion: GTF2I plays a role in NAFLD by negatively regulating the chemokine receptor family, which affects the immune profile. This study may provide a potential target for the diagnosis or therapy of NAFLD.

Case Report: A Case of Adult Methylmalonic Acidemia With Bilateral Cerebellar Lesions Caused by a New Mutation in MMACHC Gene.

Methylmalonic acidemia is a severe heterogeneous disorder of methylmalonate and cobalamin (Cbl; vitamin B12) metabolism with poor prognosis. Around 90% of reported patients with methylmalonic acidemia (MMA) are severe infantile early onset, while cases with late-onset MMA have been rarely reported. Few reported late-onset MMA patients presented with atypical clinical symptoms, therefore, often misdiagnosed if without family history. Herein, we report a 29-year-old female who was admitted to our hospital due to symptoms manifested as encephalitis. The brain MRI showed symmetrical bilateral cerebellar lesions with Gd enhancement. Laboratory tests showed significantly elevated levels of homocysteine and methylmalonic acid. A genetic analysis identified a novel homozygous mutation (c.484G>A; p.Gly162 Arg) in the MMACHC gene. The patient was diagnosed with MMA, and her symptoms improved dramatically with intramuscular adenosine cobalamin treatment. In conclusion, for patients with symmetrical lesions in the brain, the possibility of metabolic diseases should be considered, detailed medical and family history should be collected, and metabolic screening tests as well as gene tests are necessary for correct diagnosis. The mutation diversity in MMACHC gene is an important factor leading to the heterogeneity of clinical manifestations of patients with MMA.

Identification of novel natural inhibitors targeting AKT Serine/Threonine Kinase 1 (AKT1) by computational study.

Despite great progress, the current cancer treatments often have obvious toxicity and side effects. and a poor prognosis (some patients). One of the reasons for the poor prognosis is that certain enzymes prevent anticancer drugs from killing tumor cells. AKT1 is involved in regulating PI3K/AKT/mTOR, a tumor-generating pathway. Ipatasertib, a highly selective inhibitor of AKT1, is widely used in the treatment of tumors. In this study, many structural and biochemical methodswere used to find better AKT1(Threonine Kinase 1) inhibitors, which laid a foundation for the further development of AKT1 inhibitors and provided new drugs for the treatment of tumors. ZINC15 database and Discovery Studio 4.5, a computer-aided drug screening software with many modules (LibDock for virtual screening, ADME (Absorption, Distribution, Metabolism, Excretion) and TOPKAT (toxicity prediction module) for the toxicity and properties analysis, and MD simulation for stability prediction), were employed. CCK8 assay, ELISA assay genicity and higher tolerance to cytochrome P4502D6. MD simulations indicated they could bind with AKT1 stably in the natural environment. The cell experiment and specific assay for AKT1 inhibition showed they could inhibit the proliferation and AKT1 expression of MG63 cells (Osteosarcoma cells). Moreover, these novel compounds with structural modifications can be potential contributors that lead to further rational drug design for targeting AKT1.AbbreviationAKT1, AKT Serine/Threonine Kinase 1; ADME, absorption, distribution, metabolism, excretion; TOPKAT, toxicity prediction by Computer assisted technology; CCK8, Cell Counting Kit 8; ELISA, Enzyme-linked immunosorbent assay; CYP2D6, cytochrome P4502D6 inhibition; GBM, Glioblastoma; AGC kinase, protein kinase A, G, and C families (PKA, PKC, PKG); PKB, protein kinase B; PAM pathway, PI3K/AKT/mTOR pathway; OS, overall survival; PFS, progression-free survival; LD50, lethal dose half in rats; LOAEL, lowest observed adverse effect level; NPT, normal pressure and temperature; PME, particle mesh Ewald; LINCS, linear constraint solver; RMSD, root-mean-square deviation; BBB, blood-brain barrier; DS, Discovery Studio; DTP, Developmental toxicity potential; PPB, Plasma protein binding; MTD, Maximum Tolerated Dosage; AB, Aerobic Biodegradability; NTP, US. National Toxicology Program; DTP, developmental toxicity potential.

The adiposity indicators in relation to diabetes among adults in China: a cross-sectional study from China Health and Nutrition Survey.

BACKGROUND: Diabetes is a metabolic disease which has been confirmed to be involved with abnormal or excessive body fat accumulation. There is still a lack of nationwide research in China to discuss the relationship between adiposity indicators included body mass index (BMI), waist circumference (WC), visceral adiposity index, waist-height ratio, waist-to-hip ratio (WHR) and diabetes. The question of which one is the best indicator of obesity to predict diabetes in China remains to be unclear. METHODS: Data were collected from the China Health and Nutrition Survey (CHNS) in 2009, including 7,930 participants aged over 18 years old for cross-sectional analysis. Information about height, weight, WC, hip circumference, smoking status, alcohol consumption, physical activity, energy intake and blood samples were analyzed. Binary logistic regression models were used to explore the association of WC, BMI, WHR, waist-to-height ration (WHtR) and visceral adipose index (VAI) with the prevalence of diabetes in the 2009 CHNS respectively. Predictive potential of five adiposity indicators was validated by the area under the receiver operator characteristic curve (AUROC). The optimal cut-off points were determined by Youden's index, which was used to estimate the performance of adiposity indicators. RESULTS: The study shows patients in the highest quartile were more likely to have diabetes than those in the lowest quartile of WC (OR: 4.237, 95% CI: 3.265-5.499), BMI (OR: 3.312, 95% CI: 2.601-4.218), WHR (OR: 3.199, 95% CI: 2.493-4.104), WHtR (OR: 3.760, 95% CI: 2.891-4.890), VAI (OR: 4.347, 95% CI: 3.411-5.541). The area under the receiver operator characteristic curve of WC, BMI, WHR, WHtR and VAI for diabetes was 0.700, 0.663, 0.668, and 0.697 and 0.694, respectively. The optimal cut-offs regarding diabetes in Chinese are WHtR ≥0.520 for men and VAI ≥1.878 for women. CONCLUSIONS: Our findings indicate that WC, WHtR, BMI, WHR and VAI are all independent risk factors for diabetes among Chinese adults. WHtR is the most accurate indicator for diabetes in men, while VAI for women.

New natural compound inhibitors of PDGFRA (platelet-derived growth factor receptor α) based on computational study for high-grade glioma therapy.

Background: High-grade glioma (HGG) is a malignant brain tumor that is common and aggressive in children and adults. In the current medical paradigm, surgery and radiotherapy are the standard treatments for HGG patients. Despite this, the overall prognosis is still very bleak. Studies have shown that platelet-derived growth factor receptor α (PDGFRA) is an essential target to treat tumors and inhibiting the activity of PDGFRA can improve the prognosis of HGG. Thus, PDGFRA inhibitors are critical to developing drugs and cancer treatment. Objective: The purpose of this study was to screen lead compounds and candidate drugs with potential inhibitors against platelet-derived growth factor receptor α (PDGFRA) from the drug library (ZINC database) in order to improve the prognosis of patients with high-grade glioma (HGG). Materials and methods: In our study, we selected Imatinib as the reference drug. A series of computer-aided technologies, such as Discovery Studio 2019 and Schrodinger, were used to screen and assess potential inhibitors of PDGFRA. The first step was to calculate the LibDock scores and then analyze the pharmacological and toxicological properties. Following this, we docked the small molecules selected in the previous steps with PDGFRA to study their docking mechanism and affinity. In addition, molecular dynamics simulation was used to determine whether the ligand-PDGFRA complex was stable in nature. Results: Two novel natural compounds 1 and 2 (ZINC000008829785 and ZINC000013377891) from the ZINC database were found binding to PDGFRA with more favorable interaction energy. Also, they were predicted with less Ames mutagenicity, rodent carcinogenicity, non-developmental toxic potential, and tolerant with cytochrome P450 2D6 (CYP2D6). The dynamic simulation analysis demonstrated that ZINC000008829785-PDGFRA and ZINC000013377891-PDGFRA dimer complex had more favorable potential energy compared with Imatinib, and they can exist in natural environments stably. Conclusion: ZINC000008829785 and ZINC000013377891 might provide a solid foundation for drugs that inhibit PDGFRA in HGG. In addition to being safe drug candidates, these compounds had important implications for improving drugs targeting PDGFRA.

Computational study on novel natural inhibitors targeting c-MET.

ABSTRACT: 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).A battery of computer-aided virtual techniques was used to identify possible inhibitors of c-MET. A total of 17,931 ligands were screened from the ZINC15 database. LibDock is applied for structure-based screening followed by absorption, distribution, metabolic, and excretion, and toxicity prediction. Molecular docking was conducted to confirm the binding affinity mechanism between the ligand and c-MET. Molecular dynamics simulations were used to assess the stability of ligand-c-MET complexes.Two new natural compounds ZINC000005879645 and ZINC000002528509 were found to bind to c-MET in the ZINC database, showing higher binding affinity. In addition, they were predicted to have lower rodent carcinogenicity, Ames mutagenicity, developmental toxicity potential, and high tolerance to cytochrome P4502D6. Molecular dynamics simulation shows that ZINC000005879645 and ZINC000002528509 have more favorable potential energies with c-MET, which could exist stably in the natural environment.This study suggests that ZINC000005879645 and ZINC000002528509 are ideal latent inhibitors of c-MET targeting. As drug candidates, these 2 compounds have low cytotoxicity and hepatotoxicity as well as important implications for the design and improvement of c-MET target drugs.

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. Selected compounds were performed absorption, distribution, metabolism, and excretion (ADME) and toxicity prediction. The binding affinity between the selected ligands and BCL2 was confirmed by Molecular docking. The new natural compounds, ZINC00000255131 and ZINC00013298233, were found to bind closely with BCL2. Furthermore, they all scored lower in ames-induced mutagenicity, rodent carcinogenicity, non-developmental toxicity potential, and cytochrome P4502D6 tolerance. Molecular dynamics simulation shows that the combinations of ZINC00000255131 and ZINC00013298233 with BCL2 in the natural environment are more stable. Two new compounds, ZINC00000255131 and ZINC00013298233, were found to be potential inhibitors of BCL2. These compounds have been proved to be safe, which is of great significance for the development and improvement of DLCBL drugs.

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 (CDK1) and topoisomerase II alpha had better prognoses. Various computer-aided techniques were used to identify potential CDK1 inhibitors for osteosarcoma patients, and PHA-793887 was predicted to be a safe drug with a high binding affinity for CDK1. In vitro, MTT and colony formation assays demonstrated that PHA-793887 reduced the viability and clonogenicity of osteosarcoma cells, while a scratch assay suggested that PHA-793887 impaired the migration of these cells. Flow cytometry experiments revealed that PHA-793887 dose-dependently induced apoptosis in osteosarcoma cells. Western blotting and enzyme-linked immunosorbent assays indicated that CDK1 expression in osteosarcoma cells declined with increasing PHA-793887 concentrations. These results suggest that PHA-793887 could be a promising new treatment for osteosarcoma.

Computational study on new natural compound agonists of dopamine receptor.

Dopamine receptor, a polypeptide chain composed of 7 hydrophobic transmembrane regions, is a new and vital drug target, especially Dopamine receptor 2(D2). Targeting dopamine receptors, Dopamine receptor agonists are a class of drugs similar in function and structure to dopamine and can directly act on dopamine receptors and activate it. Clinically, Dopamine receptor agonist drugs have achieved significant therapeutic effects on prolactinoma and Parkinson's Disease. In the study, we virtually screened a series of potential effective agonists of Dopamine receptor by computer techniques. Firstly, we used the Molecular Docking (LibDock) step to screen out some molecules that can dock well with the protein. Then, analysis of toxicity prediction and ADME (adsorption, distribution, metabolism and excretion) were carried out. More precise molecular docking (CDOCKER) and 3-Dimensional Quantitative Structure-Activity Relationship Modeling Study(3D-QSAR) pharmacophore generation were implemented to research and explore these compounds' binding mechanism with Dopamine receptor. Last but not least, to assess compound's binding stabilities, we carried out a molecular dynamic analysis. As the results show, two compounds (ZINC000008860530 and ZINC000004096987) from the small molecule database (ZINC database) were potential effective agonists of Dopamine receptor. These two compounds can combine with Dopamine receptor with higher affinity and proved to be no toxic. The cell experiment showed that two compounds could inhibit the proliferation and PRL secretion of MMQ cells (pituitary tumor cells). Thus, this study provided valuable information about Dopamine receptor agonist-based drug discovery. So, this study will benefit patients with prolactinoma and Parkinson's disease a lot.