Search

Abstract Single atomic catalysts have shown great potential in efficiently electro‐converting O2 to H2O2 with high selectivity. However, the impact of coordination environment and introduction of extra metallic aggregates on catalytic performance still remains unclear. Herein, first a series of carbon‐based catalysts with embedded coupling Ni single atomic sites and corresponding metallic nanoparticles at adjacent geometry is synthesized. Careful performance evaluation reveals NiSA/NiNP‐NSCNT catalyst with precisely controlled active centers of synergetic adjacent Ni‐N4S single sites and crystalline Ni nanoparticles exhibits a high H2O2 selectivity over 92.7% within a wide potential range (maximum selectivity can reach 98.4%). Theoretical studies uncover that spatially coupling single atomic NiN4S sites with metallic Ni aggregates in close proximity can optimize the adsorption behavior of key intermediates *OOH to achieve a nearly ideal binding strength, which thus affording a kinetically favorable pathway for H2O2 production. This strategy of manipulating the interaction between single atoms and metallic aggregates offers a promising direction to design new high‐performance catalysts for practical H2O2 electrosynthesis.

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA. Methods We performed integrated multiomics analysis in 116 iCCA samples, including whole‐exome sequencing, bulk RNA‐sequencing and proteome analysis. Based on the non‐negative matrix factorization method and the protein abundance of metabolic genes in human genome‐scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5‐ethynyl‐2'‐deoxyuridine (EdU) assay, colony formation assay, RNA‐sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells. Results Three metabolic subtypes (S1‐S3) with subtype‐specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune‐suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT‐rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype‐specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen‐activated protein kinase (MAPK) signaling. Conclusion Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

We studied the value of circulating tumor DNA (ctDNA) in predicting early postoperative tumor recurrence and monitoring tumor burden in patients with hepatocellular carcinoma (HCC). Plasma‐free DNA, germline DNA, and tissue DNA were isolated from 41 patients with HCC. Serial ctDNAs were analyzed by next‐generation sequencing before and after operation. Whole‐exome sequencing was used to detect the DNA of HCC and adjacent tissues. In total, 47 gene mutations were identified in the ctDNA of the 41 patients analyzed before surgery. ctDNA was detected in 63.4% and 46% of the patient plasma pre‐ and postoperation, respectively. The preoperative ctDNA positivity rate was significantly lower in the nonrecurrence group than in the recurrence group. With a median follow‐up of 17.7 months, nine patients (22%) experienced tumor recurrence. ctDNA positivity at two time‐points was associated with significantly shorter recurrence‐free survival (RFS). Tumors with NRAS, NEF2L2, and MET mutations had significantly shorter times to recurrence than those without mutations and showed high recurrence prediction performance by machine learning. Multivariate analyses showed that the median variant allele frequency (VAF) of mutations in preoperative ctDNA was a strong independent predictor of RFS. ctDNA is a real‐time monitoring indicator that can accurately reflect tumor burden. The median VAF of baseline ctDNA is a strong independent predictor of RFS in individuals with HCC.