CD276 Promotes an Inhibitory Tumor Microenvironment in Hepatocellular Carcinoma and is Associated with Poor Prognosis.

Background: CD276 is an emerging immune checkpoint molecule that has been implicated in various cancers. However, its specific role in hepatocellular carcinoma (HCC) remains unclear. This study examined the impact of CD276 on patient prognosis and the tumor microenvironment (TME). Methods: The Cancer Genome Atlas (TCGA) database was utilized to evaluate CD276 expression in HCC and the association between CD276 and immune indicators was also analyzed. The signaling pathways correlated with CD276 expression were identified by gene set enrichment analysis (GSEA). Different algorithms were used to assess immune cell infiltration. The effect of CD276 knockdown on HCC cell phenotypes and its relationship with macrophage polarization was examined using the cell counting kit 8 (CCK-8) assay and co-culture system. Results: CD276 was upregulated in HCC and associated with unfavorable clinical outcomes. Hgh CD276 expression was associated with enrichment of the G2/M checkpoint, E2F targets, and mitotic spindles. CD276 expression was correlated with the infiltration of immune cells, including high level of tumor-associated macrophages and low levels of CD8+ T cells. Knockdown of CD276 decreased HCC cell proliferation and increased apoptosis. CD276 silencing in HCC cells and co-culture with THP-1-derived macrophages had a regulatory effect on macrophage polarization and macrophage-mediated cell proliferation and migration. Conclusion: CD276 expression in HCC is associated with unfavorable clinical outcomes and may contribute to the development of an immunosuppressive microenvironment. Specifically, CD276 was associated with alterations in immune cell infiltration, immune marker expression, and macrophage polarization during HCC progression, suggesting its potential as a prognostic indicator and promising target for immunotherapeutic intervention in HCC.

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Considering the safety and druggability of FDA-approved drugs, virtual screening of the database was performed to discover Mcl1 inhibitors, and the hit was subsequently validated via TR-FRET, cytotoxicity, and flow cytometry assays. To reveal the binding characteristics shared by the hit and a typical Mcl1 selective inhibitor, we employed quantum mechanics and molecular mechanics (QM/MM) calculations, umbrella sampling, and metadynamics in this work. The combined studies suggested that fluvastatin had promising cell inhibitory potency and was suitable for further investigation. We believe that this research will shed light on the discovery of novel Mcl1 inhibitors that can be used as a supplemental treatment against leukemia and provide a possible method to improve the accuracy of drug repurposing with limited computational resources while balancing the costs of experimentation well.

Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies.

The expression of phosphodiesterase 7A (PDE7A) and phosphodiesterase 8A (PDE8) genes is integral to human signaling pathways, and the inhibition of PDE7A has been associated with the onset of various diseases, including effects on the immune system and nervous system. The development of PDE7 selective inhibitors can promote research on immune and nervous system diseases, such as multiple sclerosis, chronic inflammation, and autoimmune responses. PDE8A is expressed alongside PDE8B, and its inhibitory mechanism is still unclear. Studying the mechanisms of selective inhibitors against different PDE subtypes is crucial to prevent potential side effects, such as nausea and cardiac toxicity, and the sequence similarity of the two protein subtypes was 55.9%. Therefore, it is necessary to investigate the differences of both subtypes' ligand binding sites. Selective inhibitors of two proteins were chosen to summarize the reason for their selectivity through molecular docking, molecular dynamics simulation, alanine scanning mutagenesis, and MM-GBSA calculation. We found that Phe384PDE7A, Leu401PDE7A, Gln413PDE7A, Tyr419PDE7A, and Phe416PDE7A in the active site positively contribute to the selectivity towards PDE7A. Additionally, Asn729PDE8A, Phe767PDE8A, Gln778PDE8A, and Phe781PDE8A positively contribute to the selectivity towards PDE8A.