Abstract Background: Triple-negative breast cancer (TNBC), as the most aggressive kinds of breast tumor, still cannot get effective targeted therapy. Recently, techniques of bioinformatics and molecular biology were hot topics in drug development. We want to find potential therapeutic targets of triple-negative breast cancer (TNBC) patients by bioinformatics and 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. Differential expression analysis identified the differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The functional enrichment analyses of DEGs shown the important gene ontology (GO) terms and pathways of TNBC. Protein-protein interaction (PPI) network construction screened 20 hub genes, while Kaplan website was used to analyze the relationship between the survival curve and expression of hub genes. Then Discovery Studio 4.5 screened ideal natural inhibitors of the potential therapeutic target by LibDock, ADME, toxicity prediction, CDOCKER and molecular dynamic simulation. Results: 1,212 and 353 DEGs were respectively found between TNBC tissues and normal tissues, including 88 up-regulated and 141 down-regulated DEGs in both databases. 20 hub genes were screened, and the higher expression of CDC20 was associated with a poor prognosis. Therefore, we chose CDC20 as the potential therapeutic target. 7,416 natural ligands were conducted to bind firmly with CDC20, and among these ligands, ZINC000004098930 was regarded as the potential ideal ligand, owing to its non-hepatotoxicity, more solubility level and less carcinogenicity than the reference drug, apcin. The ZINC000004098930-CDC20 could exist stably in natural environment. Conclusion: 20 genes were regarded as hub genes of TNBC and most of them were relevant to the survival curve of breast cancer patients, especially CDC20. ZINC000004098930 was chosen as the ideal natural ligand that can targeted and inhibited CDC20, which may give great contribution to TNBC targeted treatment.
Abstract BACKGROUND Glioma is one of the leading causes of brain tumors, but few etiologic factors of primary gliomas were identified. Previous observational research has shown an association between viral infection and glioma risk. Here, we sought to use Mendelian Randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infections and glioma. METHODS We conducted a two-sample bi-directional MR analysis using Genome-Wide Association Study (GWAS) data. Summary statistics data of glioma were collected from the most extensive meta-analysis GWAS involving 12,488 cases and 18,169 controls. Single Nucleotide Polymorphisms (SNPs) associated with the 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 VZV infection significantly decreased the risk of lower-grade glioma (LGG) development (OR=0.85, 95% CI: 0.76-0.96, P=0.0066, FDR=0.0396). No causal effects of the other eleven viral infections on glioma and reverse causality were detected. CONCLUSIONS Our research is one of this field's first and most significant studies. We showed robust evidence supporting that genetically predicted VZV infection reduces the risk of LGG. The findings from our research are a big step forward in understanding the etiology of glioma.
Abstract Lung adenocarcinoma is one of the most aggressive and rapidly fatal type 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, and excretion and harmless forecasts) with potential inhibition of Kirsten Rat Sarcoma Viral Oncogene Homolog 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 non-inhibition 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 inhibitions binding with KRAS G12C, which were selected as safe drug candidates and a cornerstone for KRAS G12C related medicine plan and improvement. It establishes a solid framework for systematic anticancer medication research and development.
Abstract 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.
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.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.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-β, 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.These findings suggest that TNF-β, 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.
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.
Abstract Object: 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). Methods: Identification of potential BCL2 inhibitors by computer-aided virtual screening. Libdock was applied to value 17931 compounds and top20 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. Results: 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. Conclusion: 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.
Abstract Background: Ewing’s sarcoma is the second most prevalent primary malignant bone neoplasm. RNA-binding proteins(RBPs) play a crucial role in post-transcriptional events. In tumor cells, the alterations of post-transcription enable cells to adapt to adjacent environment rapidly. Thus, the functions of RBPs in Ewing’s sarcoma can be of high value in the prognostic[1]. The underlying mechanism between Ewing’s sarcoma and RBPs remained unclear. Methods: Based on the GEO dataset, we investigated the global protein expression profile of Ewing's sarcoma patients. Differentially expressed proteins and survival-related RNA-binding protein related genes (RRGs) were evaluated by computational difference algorithm and COX regression analysis. In addition, we also explored the mutations in these RRGs. A new prognostic indicator based on RRGs was developed and tested afterwards using multivariate COX analysis. Results: The results showed that a total of 16 RRGs which closely associated with the overall survival in Ewing’s sarcoma patients using multivariate Cox regression analysis. The prognosis-related RRGs signature established using Cox regression model consists of 8 RRGs that can divide patients into high-risk and low-risk groups. Our results suggested that overall survival rate of high-risk group patients was shorter than the patients in low-risk group. According to multivariate Cox analysis, risk score index was an independent prognosis factor for Ewing’s sarcoma. In addition, the area under the curve of the corresponding receiver operating characteristic (ROC) curve of survival is 0.947. Conclusion: The 8 RRGs marker can predict the prognosis of Ewing’s sarcoma and thus help individualized treatment of patients at different risks.
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