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Background : Liver hepatocellular carcinoma (LIHC) is one of the leading causes of cancer-related death. The prognostic outcomes of advanced LIHC patients are poor. Hence, reliable prognostic biomarkers for LIHC are urgently needed. Methods : Data for vesicle-mediated transport-related genes (VMTRGs) were profiled from 338 LIHC and 50 normal tissue samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the prognostic risk model. Five GEO datasets were used to validate the risk model. The roles of the differentially expressed genes (DEGs) were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. Differences in immune cell infiltration between the high- and low-risk groups were evaluated using five algorithms. The "pRRophetic" was used to calculate the anticancer drug sensitivity of the two groups. Transwell and wound healing assays were performed to assess the role of GDP dissociation inhibitor 2 (GDI2) on LIHC cells. Results : A total of 166 prognosis-associated VMTRGs were identified, and VMTRGs-based risk model was constructed for the prognosis of LIHC patients. Four VMTRGs (GDI2, DYNC1LI1, KIF2C, and RAB32) constitute the principal components of the risk model associated with the clinical outcomes of LIHC. Tumor stage and risk score were extracted as the main prognostic indicators for LIHC patients. The VMTRGs-based risk model was significantly associated with immune responses and high expression of immune checkpoint molecules. High-risk patients were less sensitive to most chemotherapeutic drugs but benefited from immunotherapies. In vitro cellular assays revealed that GDI2 significantly promoted the growth and migration of LIHC cells. Conclusions : A VMTRGs-based risk model was constructed to predict the prognosis of LIHC patients effectively. This risk model was closely associated with the immune infiltration microenvironment. The four key VMTRGs are powerful prognostic biomarkers and therapeutic targets for LIHC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Peptide stapling represents a versatile strategy to generate peptide derivatives with stable helical structures. While a wide range of skeletons have been investigated for cyclizing the side chains of peptides, the stereochemical outcomes from the linkers remain to be better understood. In this study, we incorporated α-amino acids (α-AAs) as bridges to construct side chain-stapled analogs of an interleukin-17A-binding peptide (HAP) and evaluated the impacts of the staples on the peptide's properties. While all AA-derived peptidyl staples drastically increase the enzymatic stability of HAP, our results indicate that compared to the D-amino acid bridges, the L-AA-based staples may generate more significant impacts in increasing the helicity and enhancing the interleukin-17A(IL-17A)-binding affinity of the modified peptide. Using Rosetta modelling and molecular dynamics (MD) simulations, we demonstrate that the chirality (L/D) possessed within the AAs substantially influences the conformation of stapled HAP peptides, providing either stabilizing or destabilizing effects. Based on the computational model, a modification of the stapled HAP leads to the discovery of a peptide with further enhanced helicity, enzymatic stability and IL-17A-inhibiting ability. This systematic study reveals that chiral AAs can serve as modulatory linkers for optimizing the structures and properties of stapled peptides., (© 2023 Wiley-VCH GmbH.)