Identification of RUNX1 and IFNGR2 as prognostic-related biomarkers correlated with immune infiltration and subtype differentiation of low-grade glioma

Immune cell infiltration occurs in the tumor microenvironment (TME) and influences cancer progression through interaction with tumor cells. Runt-related transcription factors (RUNXs), RUNX1-3, are the master regulators of development and differentiation and are all important to the development of immune cells. However, the role of RUNXs in the immune cells of TME remains unclear. In this study, we first used online related databases and related LGG data from TCGA and CGGA to conduct bioinformatics analysis, which confirmed that RUNXs were significantly and positively correlated with immune infiltration in multiple tumors, especially in low-grade glioma (LGG) and there was the highest correlation between RUNXs and the progress and prognosis of LGG. Furthermore, the functional enrichment analysis revealed that RUNXs might be involved in the inflammatory and immune responses of the biological processes, and RUNXs were tightly associated with the multiple immune checkpoint molecules. Subsequent results confirmed that RUNX1, as an independent prognostic factor for LGG, may target interferon-gamma receptor 2 (IFNGR2) to regulate glioma cell proliferation, invasion, and migration. Besides, we also found that the expression levels of RUNX1 and IFNGR2 were significantly reduced, and their correlation was enhanced in the IDH-mutant subtype. Patients with a high expression of RUNX1 and/or IFNGR2 (HH/H) in the IDH-mutant subtype showed poorer prognosis and significantly increased infiltration of M2 macrophages. This finding implied the possible key role of RUNX1 in the differentiation of IDH mutant subtypes as well as in the formation of tumor microenvironment (TME) infiltration signatures by monitoring IFNGR2

Potential molecular mechanisms of Erlongjiaonang action in idiopathic sudden hearing loss: A network pharmacology and molecular docking analyses

BackgroundIdiopathic sudden hearing loss (ISHL) is characterized by sudden unexplainable and unilateral hearing loss as a clinically emergent symptom. The use of the herb Erlongjiaonang (ELJN) in traditional Chinese medicine is known to effectively control and cure ISHL. This study explored the underlying molecular mechanisms using network pharmacology and molecular docking analyses.MethodThe Traditional Chinese Medicine System Pharmacological database and the Swiss Target Prediction database were searched for the identification of ELJN constituents and potential gene targets, respectively, while ISHL-related gene abnormality was assessed using the Online Mendelian Inheritance in Man and Gene Card databases. The interaction of ELJN gene targets with ISHL genes was obtained after these databases were cross-screened, and a drug component–intersecting target network was constructed, and the gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes, and protein–protein interaction networks were analyzed. Cytoscape software tools were used to map the active components–crossover target–signaling pathway network and screened targets were then validated by establishing molecular docking with the corresponding components.ResultErlongjiaonang contains 85 components and 250 corresponding gene targets, while ISHL has 714 disease-related targets, resulting in 66 cross-targets. The bioinformatical analyses revealed these 66 cross-targets, including isorhamnetin and formononetin on NOS3 expression, baicalein on AKT1 activity, and kaempferol and quercetin on NOS3 and AKT1 activity, as potential ELJN-induced anti-ISHL targets.ConclusionThis study uncovered potential ELJN gene targets and molecular signaling pathways in the control of ISHL, providing a molecular basis for further investigation of the anti-ISHL activity of ELJN

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development