A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation.

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders.

The Nrf2 transcription factor is a master regulator of the cellular response to oxidative stress, and Keap1 is its primary negative regulator. Activating Nrf2 by inhibiting the Nrf2-Keap1 protein-protein interaction has shown promise for treating cancer and inflammatory diseases. A loop derived from Nrf2 has been shown to inhibit Keap1 selectively, especially when cyclized, but there are no reliable design methods for predicting an optimal macrocyclization strategy. In this work, we employed all-atom, explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the relative degree of preorganization for a series of peptides cyclized with a set of bis-thioether "staples". We then correlated these predictions to experimentally measured binding affinities for Keap1 and crystal structures of the cyclic peptides bound to Keap1. This work showcases a computational method for designing cyclic peptides by simulating and comparing their entire solution-phase ensembles, providing key insights into designing cyclic peptides as selective inhibitors of protein-protein interactions.

Activation of Glycolysis by MCM10 Increases Stemness and Paclitaxel Resistance in Gastric Cancer Cells.

BACKGROUND/AIMS: Chemotherapy is an essential avenue for curing malignancies; however, tumor cells acquire resistance to chemotherapeutic agents, eventually leading to chemotherapy failure. At present, paclitaxel (PTX) resistance seriously hinders the therapeutic efficacy of gastric cancer (GC). Investigating the molecular mechanism of PTX resistance in GC is critical. This study attempted to delineate the impact of MCM10 on GC resistance to PTX and its mechanism in GC. MATERIALS AND METHODS: The expression of minichromosome maintenance complex component 10 (MCM10) in GC tissues, its enrichment pathways, and its correlation with glycolysis marker genes and stemness index (mRNAsi) were analyzed in a bioinformatics effort. Real-time quantitative polymerase chain reaction was used to assay the expression of MCM10 in cells. Cell counting kit-8 (CCK-8) was used to analyze cell viability and calculate the 50% inhibitor concentration (IC50) value. Western blot was used to measure the expression of MCM10, Hexokinase 2 (HK2) and stemness-related factors in cells. Sphere-forming assay was performed to study cell sphere-forming ability. Seahorse XF 96 was utilized to measure cell extracellular acidification and oxygen consumption rates. The content of glycolysisrelated products was tested with corresponding kits. RESULTS: MCM10 was significantly upregulated in GC and enriched in the glycolysis pathway, and it was positively correlated with both glycolysis-related genes and stemness index. High expression of MCM10 increased sphere-forming ability of drug-resistant cells and GC resistance to PTX. The stimulation of PTX resistance and drug-resistant cell stemness in GC by high MCM10 expression was mediated by the glycolysis pathway. CONCLUSION: MCM10 was upregulated in GC and drove stemness and PTX resistance in GC cells by activating glycolysis. These findings generated new insights into the development of PTX resistance in GC, implicating that targeting MCM10 may be a novel approach to improve GC sensitivity to PTX chemotherapy.

Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions.

Establishing structure-activity relationships is crucial to understand and optimize the activity of peptide-based inhibitors of protein-protein interactions. Single alanine substitutions provide limited information on the residues that tolerate simultaneous modifications with retention of biological activity. To guide optimization of peptide binders, we use combinatorial peptide libraries of over 4,000 variants-in which each position is varied with either the wild-type residue or alanine-with a label-free affinity selection platform to study protein-ligand interactions. Applying this platform to a peptide binder to the oncogenic protein MDM2, several multi-alanine-substituted analogs with picomolar binding affinity were discovered. We reveal a non-additive substitution pattern in the selected sequences. The alanine substitution tolerances for peptide ligands of the 12ca5 antibody and 14-3-3 regulatory protein are also characterized, demonstrating the general applicability of this new platform. We envision that binary combinatorial alanine scanning will be a powerful tool for investigating structure-activity relationships.