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Objective·To explore the significance and mechanism of asialoglycoprotein receptor 1 (ASGR1) in hepatocellular carcinoma.Methods·The expression of ASGR1 in patients with liver cancer in The Cancer Genome Atlas (TCGA) database was analyzed by R language and the related survival curves were drawn. The Human Protein Atlas (HPA) database was used to obtain the immunohistochemistry (IHC) data of normal human liver tissue and liver cancer tissue to analyze the protein expression of ASGR1. By using the hydrodynamic tail vein injection (HTVI) delivery method, Asgr1 was knocked out in the liver of fully immune mice to explore its tumorigenic function in vivo. Gene knockout efficiency was verified by Western blotting (WB). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and correlation analysis were performed by using R language. The GSEA hallmark correlation pathway analysis was performed by using Gene Set Enrichment Analysis (GSEA) software. The expression level of key genes of glycolysis in mouse liver cancer tissue was verified by quantitative real-time PCR (qPCR).Results·ASGR1 was significantly low-expressed in liver cancer tissue, and the low expression of ASGR1 in liver cancer patients was associated with poorer overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS). The higher the degree of tumor grade, the lower the expression level of ASGR1 in patients with liver cancer. Immunohistochemistry showed that the protein expression of ASGR1 in normal human liver tissue was significantly higher than that in liver cancer tissue. In an immunocompetent mouse model of hepatocellular carcinoma, knockout of endogenous Asgr1 in mice increased the size and number of tumor nodules in liver tissue. In the TCGA database, patients with liver cancer in the ASGR1 low-expression group were enriched in multiple cancer and metabolic pathways. The expression of ASGR1 was negatively correlated with some key genes of glycolysis. The level of glycolysis in liver cancer tissues of mice in the Asgr1 knockout group was higher than that in the control group. It was suggested that the low expression of ASGR1 be likely to promote the growth and development of liver cancer and strengthen metabolic reprogramming to promote the anabolic development of tumors.Conclusion·The expression of ASGR1 is significantly reduced in patients with liver cancer, which is positively correlated with the prognosis of patients. Knocking out Asgr1 in mice can promote the occurrence and development of hepatocellular carcinoma. ASGR1 can be used as a potential biomarker for poor prognosis of liver cancer and a new target for potential treatment.

The exceptional performance of graphdiyne (GDY) in photocatalysis for hydrogen evolution attracts much attention, but the narrow band gap of GDY complicates the effective realization of the dissociation between photogenerated charges and photogenerated holes. In this study, GDY‐CuI is synthesized by cross‐coupling method, and the wide bandgap ZnWO4 is introduced into it by low‐temperature mixing, which effectively constructed the GDY‐CuI/ZW‐50 double S‐scheme heterojunction. The optimized GDY‐CuI/ZW‐50 catalyst photocatalytic hydrogen evolution performance reached 308.61 µmol after 5 h of visible light irradiation, which is 12.86 and 6.56 times than that of GDY‐CuI and ZnWO4, respectively. The improved efficiency of hydrogen evolution is attributed to the formation of a double S‐scheme heterojunction between GDY, CuI, and ZnWO4 and an internal electric field, which promotes charge transfer, reduces the complexation rate of photogenerated electrons, and enhances the redox capacity of photogenerated charges. A combination of photoelectrochemical analysis, in situ X‐ray photoelectron spectroscopy (In situ XPS), ultraviolet photoelectron spectroscopy (UPS), and density functional theory (DFT) results revealed the electron transfer mechanism. This work will provide new ideas for the design and preparation of GDY‐based photocatalysts. [ABSTRACT FROM AUTHOR]

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning–based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster–based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs. [ABSTRACT FROM AUTHOR]